Covid_segmentation.ipynb

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   "source": [
    "## Сегментация изображений\n",
    "\n",
    "Перед нами стоит задача **сегментации** кт-снимков легких и определения областей поражения от Covid-19. \n",
    "\n",
    "Исходные данные: **json** с названиями файлов,\n",
    "**images** - сами данные, **labels** - разметка: 0 - этот пиксель **НЕ относится** к поврежденному лёгкому, 1 - пиксель **относится** к повреждённому лёгкому\n",
    "\n",
    "Каждый скан - numpy arrays of shape (512, 512, n_slices)\n",
    "\n"
   ]
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   "source": [
    "## 0. Подготовка данных и импорт библиотек"
   ]
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     "text": [
      "Collecting git+https://github.com/qubvel/segmentation_models.pytorch\r\n",
      "  Cloning https://github.com/qubvel/segmentation_models.pytorch to /tmp/pip-req-build-yyskgz1t\r\n",
      "  Running command git clone -q https://github.com/qubvel/segmentation_models.pytorch /tmp/pip-req-build-yyskgz1t\r\n",
      "  Resolved https://github.com/qubvel/segmentation_models.pytorch to commit 06fdd31a6fadf659f9c90648e876dbe097e0e399\r\n",
      "  Installing build dependencies ... \u001b[?25l-\b \b\\\b \b|\b \b/\b \b-\b \b\\\b \bdone\r\n",
      "\u001b[?25h  Getting requirements to build wheel ... \u001b[?25l-\b \bdone\r\n",
      "\u001b[?25h    Preparing wheel metadata ... \u001b[?25l-\b \bdone\r\n",
      "\u001b[?25hRequirement already satisfied: torchvision>=0.5.0 in /opt/conda/lib/python3.7/site-packages (from segmentation-models-pytorch==0.2.1) (0.10.1)\r\n",
      "Collecting pretrainedmodels==0.7.4\r\n",
      "  Downloading pretrainedmodels-0.7.4.tar.gz (58 kB)\r\n",
      "\u001b[K     |████████████████████████████████| 58 kB 279 kB/s \r\n",
      "\u001b[?25hCollecting timm==0.4.12\r\n",
      "  Downloading timm-0.4.12-py3-none-any.whl (376 kB)\r\n",
      "\u001b[K     |████████████████████████████████| 376 kB 559 kB/s \r\n",
      "\u001b[?25hRequirement already satisfied: pillow in /opt/conda/lib/python3.7/site-packages (from segmentation-models-pytorch==0.2.1) (8.2.0)\r\n",
      "Requirement already satisfied: tqdm in /opt/conda/lib/python3.7/site-packages (from segmentation-models-pytorch==0.2.1) (4.62.3)\r\n",
      "Collecting efficientnet-pytorch==0.6.3\r\n",
      "  Downloading efficientnet_pytorch-0.6.3.tar.gz (16 kB)\r\n",
      "Requirement already satisfied: torch in /opt/conda/lib/python3.7/site-packages (from efficientnet-pytorch==0.6.3->segmentation-models-pytorch==0.2.1) (1.9.1)\r\n",
      "Requirement already satisfied: munch in /opt/conda/lib/python3.7/site-packages (from pretrainedmodels==0.7.4->segmentation-models-pytorch==0.2.1) (2.5.0)\r\n",
      "Requirement already satisfied: typing-extensions in /opt/conda/lib/python3.7/site-packages (from torch->efficientnet-pytorch==0.6.3->segmentation-models-pytorch==0.2.1) (3.10.0.2)\r\n",
      "Requirement already satisfied: numpy in /opt/conda/lib/python3.7/site-packages (from torchvision>=0.5.0->segmentation-models-pytorch==0.2.1) (1.19.5)\r\n",
      "Requirement already satisfied: six in /opt/conda/lib/python3.7/site-packages (from munch->pretrainedmodels==0.7.4->segmentation-models-pytorch==0.2.1) (1.16.0)\r\n",
      "Building wheels for collected packages: segmentation-models-pytorch, efficientnet-pytorch, pretrainedmodels\r\n",
      "  Building wheel for segmentation-models-pytorch (PEP 517) ... \u001b[?25l-\b \bdone\r\n",
      "\u001b[?25h  Created wheel for segmentation-models-pytorch: filename=segmentation_models_pytorch-0.2.1-py3-none-any.whl size=97450 sha256=0f95b28b18d29a17b5ab16524144c9b28f2ec0f9770d7f098a012874a1619712\r\n",
      "  Stored in directory: /tmp/pip-ephem-wheel-cache-90b62gp1/wheels/fa/c5/a8/1e8af6cb04a0974db8a4a156ebd2fdd1d99ad2558d3fce49d4\r\n",
      "  Building wheel for efficientnet-pytorch (setup.py) ... \u001b[?25l-\b \b\\\b \bdone\r\n",
      "\u001b[?25h  Created wheel for efficientnet-pytorch: filename=efficientnet_pytorch-0.6.3-py3-none-any.whl size=12421 sha256=a392e877d718032c9d7a0b9dedddcde780b7a5438491cf7841799f7ae23cd64a\r\n",
      "  Stored in directory: /root/.cache/pip/wheels/90/6b/0c/f0ad36d00310e65390b0d4c9218ae6250ac579c92540c9097a\r\n",
      "  Building wheel for pretrainedmodels (setup.py) ... \u001b[?25l-\b \b\\\b \bdone\r\n",
      "\u001b[?25h  Created wheel for pretrainedmodels: filename=pretrainedmodels-0.7.4-py3-none-any.whl size=60965 sha256=8653ab1bbb6811be542711912e0086dddbd438c17af6f7be9a3b53a66c4f4646\r\n",
      "  Stored in directory: /root/.cache/pip/wheels/ed/27/e8/9543d42de2740d3544db96aefef63bda3f2c1761b3334f4873\r\n",
      "Successfully built segmentation-models-pytorch efficientnet-pytorch pretrainedmodels\r\n",
      "Installing collected packages: timm, pretrainedmodels, efficientnet-pytorch, segmentation-models-pytorch\r\n",
      "Successfully installed efficientnet-pytorch-0.6.3 pretrainedmodels-0.7.4 segmentation-models-pytorch-0.2.1 timm-0.4.12\r\n",
      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\r\n"
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   "source": [
    "!pip install -U git+https://github.com/qubvel/segmentation_models.pytorch"
   ]
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   "source": [
    "import os\n",
    "import json\n",
    "import nibabel as nib\n",
    "import numpy as np\n",
    "from tqdm.notebook import tqdm\n",
    "import torchvision\n",
    "import random\n",
    "\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "from torch.nn.utils import clip_grad_norm_\n",
    "from torch.utils.data import Dataset, DataLoader\n",
    "import albumentations as A # Будем использовать для аугментации данных\n",
    "import segmentation_models_pytorch as smp"
   ]
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     "text": [
      "cuda:0\n"
     ]
    }
   ],
   "source": [
    "device = \"cuda:0\" if torch.cuda.is_available() else \"cpu\" # Определяем доступность gpu\n",
    "\n",
    "device = torch.device(device)\n",
    "print(device)"
   ]
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   "source": [
    "# from google.colab import drive # Маунтим диск \n",
    "# drive.mount('/content/drive')"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "Структура проекта:\n",
    "1. core_path - общий путь до проекта. В коллабе это \"./drive/MyDrive/Deep Learning/CovidKaggleTask/\"\n",
    "\n",
    "2. path = core_path + \"data/data/\" - Путь до самих файлов. В этой директории есть сам json файл, папки images и labels, в которых есть информация о кт-сканах\n",
    "\n",
    "3. core_path/models - путь до моделей для обучения. В силу больших по объёму данных, необходимо сохранять каждый раз модель. Таким образом в этой папке сохраняются модели после каждой эпохи. lungs_ct_model_1.h5 - корректное название для модели, которая была обучена только на одной эпохе"
   ]
  },
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   "source": [
    "# core_path = \"./drive/MyDrive/Deep Learning/CovidKaggleTask/\" # Определяем пути до файлов\n",
    "# path = core_path + \"data/data/\""
   ]
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   "id": "63b0adc5",
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   "source": [
    "# Для каггла\n",
    "core_path = \"../input/tgcovid/\"\n",
    "path = core_path + \"data/data/\""
   ]
  },
  {
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   },
   "source": [
    "## 1. Работа с датасетом"
   ]
  },
  {
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "['study_0298.nii',\n",
       " 'study_0291.nii',\n",
       " 'study_0271.nii',\n",
       " 'study_0284.nii',\n",
       " 'study_0292.nii']"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from os import listdir\n",
    "from os.path import isfile, join\n",
    "onlyfiles = [f for f in listdir(path + \"images\") if isfile(join(path + \"images\", f))]\n",
    "onlyfiles[:5] # Получаем список всех файлов"
   ]
  },
  {
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   "id": "8131cc99",
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   "source": [
    "Определяемся с аугментацией данных. Пытаемся понять, какие преобразования могут быть полезными для данной задачи:\n",
    "- **Повороты на небольшое количество градусов** (поворот никак не меняет логику распределения классов)\n",
    "- **Вертикальное отображение**\n",
    "- **Горизонтальное отображение**\n",
    "- **Перспектива** (несколько иной угол снятия кт увеличит набор исходных данных)\n",
    "- **Блюр** (шум поможет не переобучаться модели)"
   ]
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   "source": [
    "Создаём класс датасета, в котором прописываем метод __init__(self, список из сканов для трейна, список из сканов для валидации, аугментации), метод длины датасета и получения элемента из датасета"
   ]
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   "source": [
    "class CovidDataset(Dataset):\n",
    "    def __init__(self, X_data, without_covid_max=9999999):\n",
    "        # Загружаем сканы кт\n",
    "        path_images = os.path.join(path, 'images')\n",
    "        path_labels = os.path.join(path, 'labels')\n",
    "        \n",
    "        # Подгружаем json с инфой по разметке\n",
    "        with open(core_path + 'training_data.json', 'r') as f:\n",
    "            dict_training = json.load(f)\n",
    "\n",
    "        self.X = [] \n",
    "        self.Y = []\n",
    "        without_covid = 0\n",
    "        for entry in tqdm(dict_training):\n",
    "            image = nib.load(os.path.join(path_images, entry['image'][:-3])) # Загружаем конкретный кт-скан по названию из json\n",
    "            label = nib.load(os.path.join(path_labels, entry['label'][:-3])) # Загружаем лейблы/разметку для кт-скана\n",
    "            image = torch.tensor(image.get_fdata(), dtype=torch.uint8).transpose(1, 2).transpose(0, 1) # Меняем размерность с [43, 512, 512]\n",
    "            label = torch.tensor(label.get_fdata(), dtype=torch.uint8).transpose(1, 2).transpose(0, 1) # на [512, 512, 43] для всех картинок\n",
    "            \n",
    "            \n",
    "            if entry['image'][:-3] in X_data: # Если этот кт-скан в трейне - загружаем его туда\n",
    "                for i in range(len(image)): # Пробегаемся по всем слоям в нужном кт-скане image\n",
    "                    if label[i].sum() != 0:\n",
    "                        self.X.append(image[i]) # Добавляем отдельные картинки\n",
    "                        self.Y.append(label[i])\n",
    "                    else:\n",
    "                        if without_covid >= without_covid_max:\n",
    "                            continue\n",
    "                        else:\n",
    "                            without_covid += 1\n",
    "                            self.X.append(image[i]) # Добавляем отдельные картинки\n",
    "                            self.Y.append(label[i])\n",
    "    \n",
    "    \n",
    "    def __len__(self):\n",
    "        return len(self.X)\n",
    "    \n",
    "    \n",
    "    def __getitem__(self, idx):\n",
    "        # Делаем случайную аугментацию\n",
    "        # Метод делает аугментацию как для image - нашего скана слоя, так и для его разметки\n",
    "        # Для начала определяем поворот на угол...\n",
    "        degrees = [-35, -30, -25, -20, -15, -10, -5, 0, 5, 10, 15, 20, 25, 30, 35]\n",
    "        X = self.X[idx]\n",
    "        y = self.Y[idx]\n",
    "        X = X.type(torch.float)\n",
    "        y = y.type(torch.float)\n",
    "        X = (torch.Tensor(np.array([X.numpy()]) / 255))\n",
    "        y = (torch.Tensor(np.array([y.numpy()])))\n",
    "        value = random.random()\n",
    "        if random.random() > 0.5:\n",
    "            value = random.random()\n",
    "            if value > 0.5:\n",
    "                X = torchvision.transforms.functional.vflip(X)\n",
    "                y = torchvision.transforms.functional.vflip(y)\n",
    "            else:\n",
    "                X = torchvision.transforms.functional.hflip(X)\n",
    "                y = torchvision.transforms.functional.hflip(y)\n",
    "        value = random.random()\n",
    "        if value >= 0.1:\n",
    "            degree = random.choice(degrees)\n",
    "            X = torchvision.transforms.functional.rotate(X, degree)\n",
    "            y = torchvision.transforms.functional.rotate(y, degree)\n",
    "        else:\n",
    "            pass\n",
    "        value = random.random()\n",
    "        if value > 0.5:\n",
    "            X = torchvision.transforms.RandomPerspective(distortion_scale=0.15, p=0.5, interpolation=2, fill=0)(X)\n",
    "            y = torchvision.transforms.RandomPerspective(distortion_scale=0.15, p=0.5, interpolation=2, fill=0)(y)\n",
    "        else:\n",
    "            pass\n",
    "        value = random.random()\n",
    "        if value > 0.5:\n",
    "            X = torchvision.transforms.GaussianBlur(1)(X)\n",
    "            y = torchvision.transforms.GaussianBlur(1)(y)\n",
    "        else:\n",
    "            pass\n",
    "        \n",
    "        # Важно! Нельзя передать просто картинку (512, 512), так как используется свёртка по многим измерениям\n",
    "        # Необходимо передать в формате [палитра, ширина, высота] - [1, 512, 512]\n",
    "        return torch.Tensor(X), torch.Tensor(y) \n",
    "                                            "
   ]
  },
  {
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   "id": "d8d75119",
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   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7b35256046604cdf8f174eb164abbe89",
       "version_major": 2,
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       "  0%|          | 0/40 [00:00<?, ?it/s]"
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   "source": [
    "batch_size = 8\n",
    "\n",
    "# Перемешаем названия файлов в случайном порядке (для генерации трэйна и валидации)\n",
    "np.random.shuffle(onlyfiles) \n",
    "train_dataset = CovidDataset(onlyfiles[:33], 50)\n",
    "valid_dataset = CovidDataset(onlyfiles[33:], 50)\n",
    "train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)\n",
    "valid_loader = DataLoader(valid_dataset, batch_size=batch_size, shuffle=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "70170a6a",
   "metadata": {
    "id": "1Im-MvYF9cPa",
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    "tags": []
   },
   "source": [
    "## 2. Определяемся со структурой нейронной сети\n",
    "\n",
    "В данном случае, была выбрана сеть Unet (https://pytorch.org/hub/mateuszbuda_brain-segmentation-pytorch_unet/)\n",
    "\n",
    "Она отлично подходит для анализа медицинских изображений, так как в ней есть множество skip-connection для решения проблемы затухания градиента (vanishing gradient), а также увеличения информации для следующих слоёв, которые отвечают за выбор признаков (т. е. decoder)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "def430c2",
   "metadata": {
    "execution": {
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   "outputs": [],
   "source": [
    "import torch.nn as nn\n",
    "\n",
    "class Unet(nn.Module): # Определим структуру нейронной сети Unet\n",
    "    def block_down(self, in_features, out_features):\n",
    "        return nn.Sequential(*[nn.Conv2d(in_features, out_features, (3, 3), padding=1),\n",
    "                              nn.ReLU(),\n",
    "                              nn.BatchNorm2d(out_features)])\n",
    "    \n",
    "    def block_up(self, in_features, out_features):\n",
    "        return nn.Sequential(*[nn.Conv2d(in_features, out_features, (3, 3), padding=1),\n",
    "                              nn.ReLU(),\n",
    "                              nn.BatchNorm2d(out_features)])\n",
    "    \n",
    "    \n",
    "    def __init__(self):\n",
    "        super(Unet, self).__init__()\n",
    "        self.block_up11 = self.block_down(1, 32)\n",
    "        self.block_up12 = self.block_down(32, 32)\n",
    "        self.max_pooling11 = nn.MaxPool2d((2, 2), stride=(2, 2))\n",
    "        \n",
    "        self.block_up21 = self.block_down(32, 64)\n",
    "        self.block_up22 = self.block_down(64, 64)\n",
    "        self.max_pooling22 = nn.MaxPool2d((2, 2), stride=(2, 2))\n",
    "        \n",
    "        self.block_up31 = self.block_down(64, 128)\n",
    "        self.block_up32 = self.block_down(128, 128)\n",
    "        self.max_pooling33 = nn.MaxPool2d((2, 2), stride=(2, 2))\n",
    "        \n",
    "        self.block_up41 = self.block_down(128, 256)\n",
    "        self.block_up42 = self.block_down(256, 256)\n",
    "        self.max_pooling44 = nn.MaxPool2d((2, 2), stride=(2, 2))\n",
    "        \n",
    "        self.block_up51 = self.block_down(256, 512)\n",
    "        self.block_up52 = self.block_down(512, 512)\n",
    "        \n",
    "        self.block_up61 = nn.Upsample(scale_factor=2)\n",
    "        self.block_up62 = self.block_up(512, 256)\n",
    "        self.block_up63 = self.block_up(512, 256)\n",
    "        self.block_up64 = self.block_up(256, 256)\n",
    "        \n",
    "        self.block_up71 = nn.Upsample(scale_factor=2)\n",
    "        self.block_up72 = self.block_up(256, 128)\n",
    "        self.block_up73 = self.block_up(256, 128)\n",
    "        self.block_up74 = self.block_up(128, 128)\n",
    "        \n",
    "        self.block_up81 = nn.Upsample(scale_factor=2)\n",
    "        self.block_up82 = self.block_up(128, 64)\n",
    "        self.block_up83 = self.block_up(128, 64)\n",
    "        self.block_up84 = self.block_up(64, 64)\n",
    "        \n",
    "        self.block_up91 = nn.Upsample(scale_factor=2)\n",
    "        self.block_up92 = self.block_up(64, 32)\n",
    "        self.block_up93 = self.block_up(64, 32)\n",
    "        self.block_up94 = self.block_up(32, 32)\n",
    "        \n",
    "        self.block_up100 = self.block_up(32, 1) \n",
    "        \n",
    "    \n",
    "    def forward(self, x):\n",
    "        out = self.block_up11(x)\n",
    "        out = self.block_up12(out)\n",
    "        \n",
    "        save1 = out.clone()\n",
    "        \n",
    "        out = self.max_pooling11(out)\n",
    "        \n",
    "        out = self.block_up21(out)\n",
    "        out = self.block_up22(out)\n",
    "        \n",
    "        save2 = out.clone()\n",
    "        \n",
    "        out = self.max_pooling22(out)\n",
    "        \n",
    "        out = self.block_up31(out)\n",
    "        out = self.block_up32(out)\n",
    "        \n",
    "        save3 = out.clone()\n",
    "        \n",
    "        out = self.max_pooling33(out)\n",
    "        \n",
    "        out = self.block_up41(out)\n",
    "        out = self.block_up42(out)\n",
    "        \n",
    "        save4 = out.clone()\n",
    "        \n",
    "        out = self.max_pooling44(out)\n",
    "        \n",
    "        out = self.block_up51(out)\n",
    "        out = self.block_up52(out)\n",
    "        \n",
    "        \n",
    "        out = self.block_up61(out)\n",
    "        out = self.block_up62(out)\n",
    "        out = self.block_up63(torch.cat((out, save4), 1))\n",
    "        out = self.block_up64(out)\n",
    "\n",
    "        out = self.block_up71(out)\n",
    "        out = self.block_up72(out)\n",
    "        out = self.block_up73(torch.cat((out, save3), 1))\n",
    "        out = self.block_up74(out)\n",
    "\n",
    "        out = self.block_up81(out)\n",
    "        out = self.block_up82(out)\n",
    "        out = self.block_up83(torch.cat((out, save2), 1))\n",
    "        out = self.block_up84(out)\n",
    "\n",
    "        out = self.block_up91(out)\n",
    "        out = self.block_up92(out)\n",
    "        out = self.block_up93(torch.cat((out, save1), 1))\n",
    "        out = self.block_up94(out)\n",
    "\n",
    "        out = self.block_up100(out)\n",
    "        out = nn.Sigmoid()(out)\n",
    "        \n",
    "        return out"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a2791f0b",
   "metadata": {
    "papermill": {
     "duration": 0.037612,
     "end_time": "2022-01-13T20:51:01.508159",
     "exception": false,
     "start_time": "2022-01-13T20:51:01.470547",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Мы реализовали собственную модель **Unet**. Она довольна эффективна, однако в сложных задачах её может не хватать, поэтому к ней навешивают хорошую модель для классификации (например, ResNet).\n",
    "\n",
    "Таким образом, сложная модель будет **Unet++**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "59d601b5",
   "metadata": {
    "id": "dk0IUVaR-HPY",
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   "source": [
    "Рассмотрим три лосса и выберем, который более **эффективный**:\n",
    "- Dice Loss\n",
    "- TverskyLoss\n",
    "- Focal loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "3b5b1e8d",
   "metadata": {
    "execution": {
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    "tags": []
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   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn.functional as F\n",
    "\n",
    "def dice_loss(inputs: torch.Tensor, targets: torch.Tensor):\n",
    "    inp = inputs.contiguous().view(-1)\n",
    "    tar = targets.contiguous().view(-1)\n",
    "    noise = random.randint(1, 1000) / 10000000000\n",
    "    \n",
    "    return 1 - ((2 * (inp * tar).sum() + noise) / ((inp).sum() + (tar).sum() + noise))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "280fa42d",
   "metadata": {
    "execution": {
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   "outputs": [],
   "source": [
    "class TverskyLoss(nn.Module):\n",
    "    def __init__(self, alpha=0.7):\n",
    "        super(TverskyLoss, self).__init__()\n",
    "        self.alpha = alpha\n",
    "\n",
    "    def forward(self, inputs, targets, smooth=1):\n",
    "        y_pred = inputs\n",
    "        y_true = targets\n",
    "        y_true_pos = y_true.view(-1)\n",
    "        y_pred_pos = y_pred.view(-1)\n",
    "        true_pos = torch.sum(y_true_pos * y_pred_pos)\n",
    "        false_neg = torch.sum(y_true_pos * (1 - y_pred_pos))\n",
    "        false_pos = torch.sum((1 - y_true_pos) * y_pred_pos)\n",
    "        return 1 - (true_pos + smooth) / (true_pos + self.alpha * false_neg + (1 - self.alpha) * false_pos + smooth)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "c16412ec",
   "metadata": {
    "execution": {
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     "shell.execute_reply.started": "2022-01-13T20:23:27.800214Z"
    },
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     "exception": false,
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   "source": [
    "def sigmoid_focal_loss(inputs, targets, alpha=0.25, gamma=2, reduction=\"none\"):\n",
    "    p = torch.sigmoid(inputs)\n",
    "    ce_loss = F.binary_cross_entropy_with_logits(inputs, targets, reduction=\"none\")\n",
    "    p_t = p * targets + (1 - p) * (1 - targets)\n",
    "    loss = ce_loss * ((1 - p_t) ** gamma)\n",
    "\n",
    "    if alpha >= 0:\n",
    "        alpha_t = alpha * targets + (1 - alpha) * (1 - targets)\n",
    "        loss = alpha_t * loss\n",
    "\n",
    "    if reduction == \"mean\":\n",
    "        loss = loss.mean()\n",
    "    elif reduction == \"sum\":\n",
    "        loss = loss.sum()\n",
    "\n",
    "    return loss"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6a5f7312",
   "metadata": {
    "papermill": {
     "duration": 0.037846,
     "end_time": "2022-01-13T20:51:01.921799",
     "exception": false,
     "start_time": "2022-01-13T20:51:01.883953",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Уже изрядно опробовав различные лоссы, лучшим решением на данный момент является **TverskyLoss**\n",
    "\n",
    "Его и будем использовать для итогового обучения"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9fb0c827",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-10T16:09:36.503391Z",
     "iopub.status.busy": "2021-12-10T16:09:36.503126Z",
     "iopub.status.idle": "2021-12-10T16:10:18.211861Z",
     "shell.execute_reply": "2021-12-10T16:10:18.210917Z",
     "shell.execute_reply.started": "2021-12-10T16:09:36.503362Z"
    },
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     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### 3. Обучение модели"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd20bd42",
   "metadata": {
    "papermill": {
     "duration": 0.037858,
     "end_time": "2022-01-13T20:51:02.075781",
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     "start_time": "2022-01-13T20:51:02.037923",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "#### Подготовка модели\n",
    "\n",
    "Сначала подгружаем модель **Unet++**, если у нас нет других **уже обученных** моделей"
   ]
  },
  {
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   "execution_count": 14,
   "id": "95196bec",
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    {
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     "output_type": "stream",
     "text": [
      "Downloading: \"https://download.pytorch.org/models/resnet18-5c106cde.pth\" to /root/.cache/torch/hub/checkpoints/resnet18-5c106cde.pth\n"
     ]
    },
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     "output_type": "stream",
     "text": [
      "Загружен непредобученный Unet++\n"
     ]
    }
   ],
   "source": [
    "import segmentation_models_pytorch as smp\n",
    "\n",
    "use_previous_versions = False\n",
    "previous_i = 0\n",
    "path_to_model = \"output/kaggle/working/\"\n",
    "\n",
    "# Проверяем, вдруг мы можем подгрузить уже обученную модель\n",
    "if use_previous_versions:\n",
    "    models_variation = []\n",
    "    for put, papki, files in os.walk(\".\"):\n",
    "        for el in files:\n",
    "            if \"lungs_ct_model\" in el:\n",
    "                models_variation.append(el)\n",
    "                \n",
    "    # Название файла - lungs_ct_model_1.h5\n",
    "    if len(models_variation) != 0:\n",
    "        models_variation = sorted(models_variation, key=lambda x: - int(x.split(\"_\")[-1].split(\".\")[0]))\n",
    "        model = torch.load(models_variation[-1])\n",
    "        previous_i = int(models_variation[0].split(\"_\")[-1].split(\".\")[0])\n",
    "        print(\"Загружена прошлая модель: {}\".format(str(previous_i)))\n",
    "    else:\n",
    "        model = smp.UnetPlusPlus(encoder_name='resnet18', in_channels=1, classes=1, activation=\"tanh\")\n",
    "        print(\"Загружен непредобученный Unet++\")\n",
    "else:\n",
    "    model = smp.UnetPlusPlus(encoder_name='resnet18', in_channels=1, classes=1, activation=\"sigmoid\")\n",
    "    print(\"Загружен непредобученный Unet++\")\n",
    "\n",
    "\n",
    "device = torch.device('cuda:0')\n",
    "model = model.to(device)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0183462c",
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   "source": [
    "#### Метрики\n",
    "\n",
    "Определяемся с метриками:\n",
    "\n",
    "Нам необходимо детектировать ковид, а значит, нам подойдёт полнота **recall** (если мы будем детектировать ковид, где только есть подозрения - будет хорошо)\n",
    "\n",
    "Однако детектировать всё подряд не очень, а значит, нужно и смотреть на **precision**\n",
    "\n",
    "Таким образом, **f-мера** прекрасно подойдёт"
   ]
  },
  {
   "cell_type": "code",
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   "id": "56f4c0b6",
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   "source": [
    "def recall(output_batch, correct_batch, threshold=0.99):    \n",
    "    output_numpy = output_batch.detach().numpy()\n",
    "    correct_numpy = correct_batch.detach().numpy()\n",
    "    \n",
    "    amount_of_correct = np.count_nonzero(np.where(output_numpy > threshold, output_numpy, 0) + correct_numpy == 2)\n",
    "    amount_all = np.count_nonzero(correct_numpy == 1)\n",
    "    try:\n",
    "        return amount_of_correct / amount_all\n",
    "    except:\n",
    "        return np.nan\n",
    "\n",
    "def precision(output_batch, correct_batch, threshold=0.99):   \n",
    "    output_numpy = output_batch.detach().numpy()\n",
    "    correct_numpy = correct_batch.detach().numpy()\n",
    "    \n",
    "    amount_of_correct = np.count_nonzero(np.where(output_numpy > threshold, output_numpy, 0) + correct_numpy == 2)\n",
    "    amount_all = np.count_nonzero(output_numpy == 1)\n",
    "    try:\n",
    "        return amount_of_correct / amount_all\n",
    "    except:\n",
    "        return np.nan\n",
    "    \n",
    "def f1_score(precision, recall):\n",
    "    return 2 * (precision * recall) / (precision + recall)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "50d8bbae",
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   "source": [
    "num_epoch = 125\n",
    "lr = 0.0005\n",
    "\n",
    "dice_loss_criterion = dice_loss\n",
    "focal_loss_criterion = sigmoid_focal_loss\n",
    "tverskoy_loss = TverskyLoss(alpha=0.7)\n",
    "\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr=lr)\n",
    "scheduler = torch.optim.lr_scheduler.MultiplicativeLR(optimizer, lambda x: 0.9825)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
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    {
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.7/site-packages/torchvision/transforms/transforms.py:716: UserWarning: Argument interpolation should be of type InterpolationMode instead of int. Please, use InterpolationMode enum.\n",
      "  \"Argument interpolation should be of type InterpolationMode instead of int. \"\n",
      "/opt/conda/lib/python3.7/site-packages/torchvision/transforms/functional.py:594: UserWarning: torch.lstsq is deprecated in favor of torch.linalg.lstsq and will be removed in a future PyTorch release.\n",
      "torch.linalg.lstsq has reversed arguments and does not return the QR decomposition in the returned tuple (although it returns other information about the problem).\n",
      "To get the qr decomposition consider using torch.linalg.qr.\n",
      "The returned solution in torch.lstsq stored the residuals of the solution in the last m - n columns of the returned value whenever m > n. In torch.linalg.lstsq, the residuals in the field 'residuals' of the returned named tuple.\n",
      "The unpacking of the solution, as in\n",
      "X, _ = torch.lstsq(B, A).solution[:A.size(1)]\n",
      "should be replaced with\n",
      "X = torch.linalg.lstsq(A, B).solution (Triggered internally at  /usr/local/src/pytorch/aten/src/ATen/LegacyTHFunctionsCPU.cpp:389.)\n",
      "  res = torch.lstsq(b_matrix, a_matrix)[0]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------------------\n",
      "Epoch: 0\n",
      "Loss:\t\t 0.93248\n",
      "Learning rate: 0.00050000\n",
      "Precision:\t 28.068%\n",
      "Recall:\t\t 13.785%\n",
      "F1-score: \t   0.185\n",
      "------------------------------\n",
      "Epoch: 1\n",
      "Loss:\t\t 0.76047\n",
      "Learning rate: 0.00049125\n",
      "Precision:\t 35.836%\n",
      "Recall:\t\t 19.811%\n",
      "F1-score: \t   0.255\n",
      "------------------------------\n",
      "Epoch: 2\n",
      "Loss:\t\t 0.59851\n",
      "Learning rate: 0.00048265\n",
      "Precision:\t 34.039%\n",
      "Recall:\t\t 23.163%\n",
      "F1-score: \t   0.276\n",
      "------------------------------\n",
      "Epoch: 3\n",
      "Loss:\t\t 0.56222\n",
      "Learning rate: 0.00047421\n",
      "Precision:\t 36.532%\n",
      "Recall:\t\t 25.566%\n",
      "F1-score: \t   0.301\n",
      "------------------------------\n",
      "Epoch: 4\n",
      "Loss:\t\t 0.52782\n",
      "Learning rate: 0.00046591\n",
      "Precision:\t 37.571%\n",
      "Recall:\t\t 29.499%\n",
      "F1-score: \t   0.330\n",
      "------------------------------\n",
      "Epoch: 5\n",
      "Loss:\t\t 0.51916\n",
      "Learning rate: 0.00045775\n",
      "Precision:\t 39.926%\n",
      "Recall:\t\t 36.244%\n",
      "F1-score: \t   0.380\n",
      "------------------------------\n",
      "Epoch: 6\n",
      "Loss:\t\t 0.49088\n",
      "Learning rate: 0.00044974\n",
      "Precision:\t 37.659%\n",
      "Recall:\t\t 31.074%\n",
      "F1-score: \t   0.341\n",
      "------------------------------\n",
      "Epoch: 7\n",
      "Loss:\t\t 0.48745\n",
      "Learning rate: 0.00044187\n",
      "Precision:\t 38.250%\n",
      "Recall:\t\t 34.633%\n",
      "F1-score: \t   0.364\n",
      "------------------------------\n",
      "Epoch: 8\n",
      "Loss:\t\t 0.48359\n",
      "Learning rate: 0.00043414\n",
      "Precision:\t 45.210%\n",
      "Recall:\t\t 37.159%\n",
      "F1-score: \t   0.408\n",
      "------------------------------\n",
      "Epoch: 9\n",
      "Loss:\t\t 0.48263\n",
      "Learning rate: 0.00042654\n",
      "Precision:\t 49.086%\n",
      "Recall:\t\t 33.582%\n",
      "F1-score: \t   0.399\n",
      "------------------------------\n",
      "Epoch: 10\n",
      "Loss:\t\t 0.46283\n",
      "Learning rate: 0.00041908\n",
      "Precision:\t 39.742%\n",
      "Recall:\t\t 27.770%\n",
      "F1-score: \t   0.327\n",
      "------------------------------\n",
      "Epoch: 11\n",
      "Loss:\t\t 0.50117\n",
      "Learning rate: 0.00041174\n",
      "Precision:\t 42.685%\n",
      "Recall:\t\t 35.065%\n",
      "F1-score: \t   0.385\n",
      "------------------------------\n",
      "Epoch: 12\n",
      "Loss:\t\t 0.48971\n",
      "Learning rate: 0.00040454\n",
      "Precision:\t 34.342%\n",
      "Recall:\t\t 34.336%\n",
      "F1-score: \t   0.343\n",
      "------------------------------\n",
      "Epoch: 13\n",
      "Loss:\t\t 0.48490\n",
      "Learning rate: 0.00039746\n",
      "Precision:\t 43.617%\n",
      "Recall:\t\t 36.758%\n",
      "F1-score: \t   0.399\n",
      "------------------------------\n",
      "Epoch: 14\n",
      "Loss:\t\t 0.49025\n",
      "Learning rate: 0.00039050\n",
      "Precision:\t 36.590%\n",
      "Recall:\t\t 37.258%\n",
      "F1-score: \t   0.369\n",
      "------------------------------\n",
      "Epoch: 15\n",
      "Loss:\t\t 0.46990\n",
      "Learning rate: 0.00038367\n",
      "Precision:\t 42.823%\n",
      "Recall:\t\t 38.278%\n",
      "F1-score: \t   0.404\n",
      "------------------------------\n",
      "Epoch: 16\n",
      "Loss:\t\t 0.45210\n",
      "Learning rate: 0.00037696\n",
      "Precision:\t 44.429%\n",
      "Recall:\t\t 40.448%\n",
      "F1-score: \t   0.423\n",
      "------------------------------\n",
      "Epoch: 17\n",
      "Loss:\t\t 0.44380\n",
      "Learning rate: 0.00037036\n",
      "Precision:\t 53.556%\n",
      "Recall:\t\t 34.591%\n",
      "F1-score: \t   0.420\n",
      "------------------------------\n",
      "Epoch: 18\n",
      "Loss:\t\t 0.45271\n",
      "Learning rate: 0.00036388\n",
      "Precision:\t 47.620%\n",
      "Recall:\t\t 34.300%\n",
      "F1-score: \t   0.399\n",
      "------------------------------\n",
      "Epoch: 19\n",
      "Loss:\t\t 0.44449\n",
      "Learning rate: 0.00035751\n",
      "Precision:\t 39.282%\n",
      "Recall:\t\t 36.411%\n",
      "F1-score: \t   0.378\n",
      "------------------------------\n",
      "Epoch: 20\n",
      "Loss:\t\t 0.44302\n",
      "Learning rate: 0.00035125\n",
      "Precision:\t 43.183%\n",
      "Recall:\t\t 32.513%\n",
      "F1-score: \t   0.371\n",
      "------------------------------\n",
      "Epoch: 21\n",
      "Loss:\t\t 0.43064\n",
      "Learning rate: 0.00034511\n",
      "Precision:\t 50.455%\n",
      "Recall:\t\t 36.582%\n",
      "F1-score: \t   0.424\n",
      "------------------------------\n",
      "Epoch: 22\n",
      "Loss:\t\t 0.43625\n",
      "Learning rate: 0.00033907\n",
      "Precision:\t 43.144%\n",
      "Recall:\t\t 40.711%\n",
      "F1-score: \t   0.419\n",
      "------------------------------\n",
      "Epoch: 23\n",
      "Loss:\t\t 0.44480\n",
      "Learning rate: 0.00033313\n",
      "Precision:\t 45.393%\n",
      "Recall:\t\t 38.866%\n",
      "F1-score: \t   0.419\n",
      "------------------------------\n",
      "Epoch: 24\n",
      "Loss:\t\t 0.42893\n",
      "Learning rate: 0.00032730\n",
      "Precision:\t 39.950%\n",
      "Recall:\t\t 37.361%\n",
      "F1-score: \t   0.386\n",
      "------------------------------\n",
      "Epoch: 25\n",
      "Loss:\t\t 0.41640\n",
      "Learning rate: 0.00032158\n",
      "Precision:\t 43.842%\n",
      "Recall:\t\t 42.015%\n",
      "F1-score: \t   0.429\n",
      "------------------------------\n",
      "Epoch: 26\n",
      "Loss:\t\t 0.42601\n",
      "Learning rate: 0.00031595\n",
      "Precision:\t 49.565%\n",
      "Recall:\t\t 39.732%\n",
      "F1-score: \t   0.441\n",
      "------------------------------\n",
      "Epoch: 27\n",
      "Loss:\t\t 0.43598\n",
      "Learning rate: 0.00031042\n",
      "Precision:\t 42.733%\n",
      "Recall:\t\t 39.259%\n",
      "F1-score: \t   0.409\n",
      "------------------------------\n",
      "Epoch: 28\n",
      "Loss:\t\t 0.41668\n",
      "Learning rate: 0.00030499\n",
      "Precision:\t 41.252%\n",
      "Recall:\t\t 37.446%\n",
      "F1-score: \t   0.393\n",
      "------------------------------\n",
      "Epoch: 29\n",
      "Loss:\t\t 0.40267\n",
      "Learning rate: 0.00029965\n",
      "Precision:\t 52.007%\n",
      "Recall:\t\t 32.998%\n",
      "F1-score: \t   0.404\n",
      "------------------------------\n",
      "Epoch: 30\n",
      "Loss:\t\t 0.42966\n",
      "Learning rate: 0.00029441\n",
      "Precision:\t 45.801%\n",
      "Recall:\t\t 33.817%\n",
      "F1-score: \t   0.389\n",
      "------------------------------\n",
      "Epoch: 31\n",
      "Loss:\t\t 0.42470\n",
      "Learning rate: 0.00028925\n",
      "Precision:\t 41.727%\n",
      "Recall:\t\t 32.436%\n",
      "F1-score: \t   0.365\n",
      "------------------------------\n",
      "Epoch: 32\n",
      "Loss:\t\t 0.42504\n",
      "Learning rate: 0.00028419\n",
      "Precision:\t 45.883%\n",
      "Recall:\t\t 39.663%\n",
      "F1-score: \t   0.425\n",
      "------------------------------\n",
      "Epoch: 33\n",
      "Loss:\t\t 0.42474\n",
      "Learning rate: 0.00027922\n",
      "Precision:\t 45.324%\n",
      "Recall:\t\t 40.009%\n",
      "F1-score: \t   0.425\n",
      "------------------------------\n",
      "Epoch: 34\n",
      "Loss:\t\t 0.41881\n",
      "Learning rate: 0.00027433\n",
      "Precision:\t 46.764%\n",
      "Recall:\t\t 36.308%\n",
      "F1-score: \t   0.409\n",
      "------------------------------\n",
      "Epoch: 35\n",
      "Loss:\t\t 0.39000\n",
      "Learning rate: 0.00026953\n",
      "Precision:\t 48.661%\n",
      "Recall:\t\t 35.092%\n",
      "F1-score: \t   0.408\n",
      "------------------------------\n",
      "Epoch: 36\n",
      "Loss:\t\t 0.41254\n",
      "Learning rate: 0.00026481\n",
      "Precision:\t 45.992%\n",
      "Recall:\t\t 44.332%\n",
      "F1-score: \t   0.451\n",
      "------------------------------\n",
      "Epoch: 37\n",
      "Loss:\t\t 0.41121\n",
      "Learning rate: 0.00026018\n",
      "Precision:\t 43.780%\n",
      "Recall:\t\t 43.824%\n",
      "F1-score: \t   0.438\n",
      "------------------------------\n",
      "Epoch: 38\n",
      "Loss:\t\t 0.40694\n",
      "Learning rate: 0.00025563\n",
      "Precision:\t 44.353%\n",
      "Recall:\t\t 39.203%\n",
      "F1-score: \t   0.416\n",
      "------------------------------\n",
      "Epoch: 39\n",
      "Loss:\t\t 0.39642\n",
      "Learning rate: 0.00025115\n",
      "Precision:\t 43.831%\n",
      "Recall:\t\t 42.286%\n",
      "F1-score: \t   0.430\n",
      "------------------------------\n",
      "Epoch: 40\n",
      "Loss:\t\t 0.39426\n",
      "Learning rate: 0.00024676\n",
      "Precision:\t 49.635%\n",
      "Recall:\t\t 39.665%\n",
      "F1-score: \t   0.441\n",
      "------------------------------\n",
      "Epoch: 41\n",
      "Loss:\t\t 0.39383\n",
      "Learning rate: 0.00024244\n",
      "Precision:\t 53.653%\n",
      "Recall:\t\t 36.255%\n",
      "F1-score: \t   0.433\n",
      "------------------------------\n",
      "Epoch: 42\n",
      "Loss:\t\t 0.38441\n",
      "Learning rate: 0.00023820\n",
      "Precision:\t 47.672%\n",
      "Recall:\t\t 37.273%\n",
      "F1-score: \t   0.418\n",
      "------------------------------\n",
      "Epoch: 43\n",
      "Loss:\t\t 0.39934\n",
      "Learning rate: 0.00023403\n",
      "Precision:\t 47.845%\n",
      "Recall:\t\t 42.228%\n",
      "F1-score: \t   0.449\n",
      "------------------------------\n",
      "Epoch: 44\n",
      "Loss:\t\t 0.38652\n",
      "Learning rate: 0.00022993\n",
      "Precision:\t 47.768%\n",
      "Recall:\t\t 38.600%\n",
      "F1-score: \t   0.427\n",
      "------------------------------\n",
      "Epoch: 45\n",
      "Loss:\t\t 0.40154\n",
      "Learning rate: 0.00022591\n",
      "Precision:\t 49.271%\n",
      "Recall:\t\t 40.471%\n",
      "F1-score: \t   0.444\n",
      "------------------------------\n",
      "Epoch: 46\n",
      "Loss:\t\t 0.39802\n",
      "Learning rate: 0.00022196\n",
      "Precision:\t 45.381%\n",
      "Recall:\t\t 36.149%\n",
      "F1-score: \t   0.402\n",
      "------------------------------\n",
      "Epoch: 47\n",
      "Loss:\t\t 0.40043\n",
      "Learning rate: 0.00021807\n",
      "Precision:\t 50.677%\n",
      "Recall:\t\t 31.781%\n",
      "F1-score: \t   0.391\n",
      "------------------------------\n",
      "Epoch: 48\n",
      "Loss:\t\t 0.39589\n",
      "Learning rate: 0.00021426\n",
      "Precision:\t 53.004%\n",
      "Recall:\t\t 34.616%\n",
      "F1-score: \t   0.419\n",
      "------------------------------\n",
      "Epoch: 49\n",
      "Loss:\t\t 0.39748\n",
      "Learning rate: 0.00021051\n",
      "Precision:\t 47.178%\n",
      "Recall:\t\t 38.725%\n",
      "F1-score: \t   0.425\n",
      "------------------------------\n",
      "Epoch: 50\n",
      "Loss:\t\t 0.39642\n",
      "Learning rate: 0.00020682\n",
      "Precision:\t 46.823%\n",
      "Recall:\t\t 38.692%\n",
      "F1-score: \t   0.424\n",
      "------------------------------\n",
      "Epoch: 51\n",
      "Loss:\t\t 0.38589\n",
      "Learning rate: 0.00020320\n",
      "Precision:\t 55.996%\n",
      "Recall:\t\t 33.834%\n",
      "F1-score: \t   0.422\n",
      "------------------------------\n",
      "Epoch: 52\n",
      "Loss:\t\t 0.38635\n",
      "Learning rate: 0.00019965\n",
      "Precision:\t 49.224%\n",
      "Recall:\t\t 37.799%\n",
      "F1-score: \t   0.428\n",
      "------------------------------\n",
      "Epoch: 53\n",
      "Loss:\t\t 0.40659\n",
      "Learning rate: 0.00019615\n",
      "Precision:\t 47.404%\n",
      "Recall:\t\t 40.873%\n",
      "F1-score: \t   0.439\n",
      "------------------------------\n",
      "Epoch: 54\n",
      "Loss:\t\t 0.39944\n",
      "Learning rate: 0.00019272\n",
      "Precision:\t 49.228%\n",
      "Recall:\t\t 41.956%\n",
      "F1-score: \t   0.453\n",
      "------------------------------\n",
      "Epoch: 55\n",
      "Loss:\t\t 0.39203\n",
      "Learning rate: 0.00018935\n",
      "Precision:\t 47.538%\n",
      "Recall:\t\t 35.908%\n",
      "F1-score: \t   0.409\n",
      "------------------------------\n",
      "Epoch: 56\n",
      "Loss:\t\t 0.39427\n",
      "Learning rate: 0.00018603\n",
      "Precision:\t 54.662%\n",
      "Recall:\t\t 40.504%\n",
      "F1-score: \t   0.465\n",
      "------------------------------\n",
      "Epoch: 57\n",
      "Loss:\t\t 0.38347\n",
      "Learning rate: 0.00018278\n",
      "Precision:\t 46.905%\n",
      "Recall:\t\t 37.426%\n",
      "F1-score: \t   0.416\n",
      "------------------------------\n",
      "Epoch: 58\n",
      "Loss:\t\t 0.38526\n",
      "Learning rate: 0.00017958\n",
      "Precision:\t 52.991%\n",
      "Recall:\t\t 38.544%\n",
      "F1-score: \t   0.446\n",
      "------------------------------\n",
      "Epoch: 59\n",
      "Loss:\t\t 0.37930\n",
      "Learning rate: 0.00017644\n",
      "Precision:\t 49.234%\n",
      "Recall:\t\t 38.266%\n",
      "F1-score: \t   0.431\n",
      "------------------------------\n",
      "Epoch: 60\n",
      "Loss:\t\t 0.39248\n",
      "Learning rate: 0.00017335\n",
      "Precision:\t 49.793%\n",
      "Recall:\t\t 41.898%\n",
      "F1-score: \t   0.455\n",
      "------------------------------\n",
      "Epoch: 61\n",
      "Loss:\t\t 0.39489\n",
      "Learning rate: 0.00017032\n",
      "Precision:\t 49.749%\n",
      "Recall:\t\t 33.210%\n",
      "F1-score: \t   0.398\n",
      "------------------------------\n",
      "Epoch: 62\n",
      "Loss:\t\t 0.36411\n",
      "Learning rate: 0.00016734\n",
      "Precision:\t 48.272%\n",
      "Recall:\t\t 31.406%\n",
      "F1-score: \t   0.381\n",
      "------------------------------\n",
      "Epoch: 63\n",
      "Loss:\t\t 0.37575\n",
      "Learning rate: 0.00016441\n",
      "Precision:\t 53.186%\n",
      "Recall:\t\t 36.090%\n",
      "F1-score: \t   0.430\n",
      "------------------------------\n",
      "Epoch: 64\n",
      "Loss:\t\t 0.36943\n",
      "Learning rate: 0.00016153\n",
      "Precision:\t 58.073%\n",
      "Recall:\t\t 40.916%\n",
      "F1-score: \t   0.480\n",
      "------------------------------\n",
      "Epoch: 65\n",
      "Loss:\t\t 0.36713\n",
      "Learning rate: 0.00015870\n",
      "Precision:\t 56.116%\n",
      "Recall:\t\t 29.977%\n",
      "F1-score: \t   0.391\n",
      "------------------------------\n",
      "Epoch: 66\n",
      "Loss:\t\t 0.35791\n",
      "Learning rate: 0.00015593\n",
      "Precision:\t 53.691%\n",
      "Recall:\t\t 35.293%\n",
      "F1-score: \t   0.426\n",
      "------------------------------\n",
      "Epoch: 67\n",
      "Loss:\t\t 0.35784\n",
      "Learning rate: 0.00015320\n",
      "Precision:\t 50.341%\n",
      "Recall:\t\t 37.595%\n",
      "F1-score: \t   0.430\n",
      "------------------------------\n",
      "Epoch: 68\n",
      "Loss:\t\t 0.36192\n",
      "Learning rate: 0.00015052\n",
      "Precision:\t 51.199%\n",
      "Recall:\t\t 38.986%\n",
      "F1-score: \t   0.443\n",
      "------------------------------\n",
      "Epoch: 69\n",
      "Loss:\t\t 0.36487\n",
      "Learning rate: 0.00014788\n",
      "Precision:\t 54.134%\n",
      "Recall:\t\t 34.569%\n",
      "F1-score: \t   0.422\n",
      "------------------------------\n",
      "Epoch: 70\n",
      "Loss:\t\t 0.37202\n",
      "Learning rate: 0.00014529\n",
      "Precision:\t 47.050%\n",
      "Recall:\t\t 32.196%\n",
      "F1-score: \t   0.382\n",
      "------------------------------\n",
      "Epoch: 71\n",
      "Loss:\t\t 0.37547\n",
      "Learning rate: 0.00014275\n",
      "Precision:\t 53.791%\n",
      "Recall:\t\t 35.490%\n",
      "F1-score: \t   0.428\n",
      "------------------------------\n",
      "Epoch: 72\n",
      "Loss:\t\t 0.34947\n",
      "Learning rate: 0.00014025\n",
      "Precision:\t 53.436%\n",
      "Recall:\t\t 40.559%\n",
      "F1-score: \t   0.461\n",
      "------------------------------\n",
      "Epoch: 73\n",
      "Loss:\t\t 0.35955\n",
      "Learning rate: 0.00013780\n",
      "Precision:\t 52.014%\n",
      "Recall:\t\t 37.393%\n",
      "F1-score: \t   0.435\n",
      "------------------------------\n",
      "Epoch: 74\n",
      "Loss:\t\t 0.37739\n",
      "Learning rate: 0.00013539\n",
      "Precision:\t 56.919%\n",
      "Recall:\t\t 34.369%\n",
      "F1-score: \t   0.429\n",
      "------------------------------\n",
      "Epoch: 75\n",
      "Loss:\t\t 0.36422\n",
      "Learning rate: 0.00013302\n",
      "Precision:\t 52.046%\n",
      "Recall:\t\t 40.761%\n",
      "F1-score: \t   0.457\n",
      "------------------------------\n",
      "Epoch: 76\n",
      "Loss:\t\t 0.36446\n",
      "Learning rate: 0.00013069\n",
      "Precision:\t 51.452%\n",
      "Recall:\t\t 37.990%\n",
      "F1-score: \t   0.437\n",
      "------------------------------\n",
      "Epoch: 77\n",
      "Loss:\t\t 0.35957\n",
      "Learning rate: 0.00012840\n",
      "Precision:\t 52.430%\n",
      "Recall:\t\t 34.582%\n",
      "F1-score: \t   0.417\n",
      "------------------------------\n",
      "Epoch: 78\n",
      "Loss:\t\t 0.37276\n",
      "Learning rate: 0.00012616\n",
      "Precision:\t 50.778%\n",
      "Recall:\t\t 36.628%\n",
      "F1-score: \t   0.426\n",
      "------------------------------\n",
      "Epoch: 79\n",
      "Loss:\t\t 0.34961\n",
      "Learning rate: 0.00012395\n",
      "Precision:\t 47.166%\n",
      "Recall:\t\t 37.339%\n",
      "F1-score: \t   0.417\n",
      "------------------------------\n",
      "Epoch: 80\n",
      "Loss:\t\t 0.35352\n",
      "Learning rate: 0.00012178\n",
      "Precision:\t 49.237%\n",
      "Recall:\t\t 33.909%\n",
      "F1-score: \t   0.402\n",
      "------------------------------\n",
      "Epoch: 81\n",
      "Loss:\t\t 0.36068\n",
      "Learning rate: 0.00011965\n",
      "Precision:\t 53.408%\n",
      "Recall:\t\t 35.289%\n",
      "F1-score: \t   0.425\n",
      "------------------------------\n",
      "Epoch: 82\n",
      "Loss:\t\t 0.35964\n",
      "Learning rate: 0.00011755\n",
      "Precision:\t 51.988%\n",
      "Recall:\t\t 35.760%\n",
      "F1-score: \t   0.424\n",
      "------------------------------\n",
      "Epoch: 83\n",
      "Loss:\t\t 0.35670\n",
      "Learning rate: 0.00011550\n",
      "Precision:\t 52.403%\n",
      "Recall:\t\t 36.547%\n",
      "F1-score: \t   0.431\n",
      "------------------------------\n",
      "Epoch: 84\n",
      "Loss:\t\t 0.36288\n",
      "Learning rate: 0.00011348\n",
      "Precision:\t 50.271%\n",
      "Recall:\t\t 35.465%\n",
      "F1-score: \t   0.416\n",
      "------------------------------\n",
      "Epoch: 85\n",
      "Loss:\t\t 0.35535\n",
      "Learning rate: 0.00011149\n",
      "Precision:\t 50.871%\n",
      "Recall:\t\t 35.063%\n",
      "F1-score: \t   0.415\n",
      "------------------------------\n",
      "Epoch: 86\n",
      "Loss:\t\t 0.33747\n",
      "Learning rate: 0.00010954\n",
      "Precision:\t 58.161%\n",
      "Recall:\t\t 40.444%\n",
      "F1-score: \t   0.477\n",
      "------------------------------\n",
      "Epoch: 87\n",
      "Loss:\t\t 0.35842\n",
      "Learning rate: 0.00010762\n",
      "Precision:\t 47.789%\n",
      "Recall:\t\t 40.421%\n",
      "F1-score: \t   0.438\n",
      "------------------------------\n",
      "Epoch: 88\n",
      "Loss:\t\t 0.33180\n",
      "Learning rate: 0.00010574\n",
      "Precision:\t 55.574%\n",
      "Recall:\t\t 37.187%\n",
      "F1-score: \t   0.446\n",
      "------------------------------\n",
      "Epoch: 89\n",
      "Loss:\t\t 0.33948\n",
      "Learning rate: 0.00010389\n",
      "Precision:\t 46.222%\n",
      "Recall:\t\t 33.992%\n",
      "F1-score: \t   0.392\n",
      "------------------------------\n",
      "Epoch: 90\n",
      "Loss:\t\t 0.33532\n",
      "Learning rate: 0.00010207\n",
      "Precision:\t 58.814%\n",
      "Recall:\t\t 32.136%\n",
      "F1-score: \t   0.416\n",
      "------------------------------\n",
      "Epoch: 91\n",
      "Loss:\t\t 0.34591\n",
      "Learning rate: 0.00010028\n",
      "Precision:\t 53.836%\n",
      "Recall:\t\t 27.961%\n",
      "F1-score: \t   0.368\n",
      "------------------------------\n",
      "Epoch: 92\n",
      "Loss:\t\t 0.35773\n",
      "Learning rate: 0.00009853\n",
      "Precision:\t 50.606%\n",
      "Recall:\t\t 39.725%\n",
      "F1-score: \t   0.445\n",
      "------------------------------\n",
      "Epoch: 93\n",
      "Loss:\t\t 0.34624\n",
      "Learning rate: 0.00009681\n",
      "Precision:\t 50.173%\n",
      "Recall:\t\t 33.254%\n",
      "F1-score: \t   0.400\n",
      "------------------------------\n",
      "Epoch: 94\n",
      "Loss:\t\t 0.35899\n",
      "Learning rate: 0.00009511\n",
      "Precision:\t 57.336%\n",
      "Recall:\t\t 36.914%\n",
      "F1-score: \t   0.449\n",
      "------------------------------\n",
      "Epoch: 95\n",
      "Loss:\t\t 0.34736\n",
      "Learning rate: 0.00009345\n",
      "Precision:\t 53.769%\n",
      "Recall:\t\t 39.185%\n",
      "F1-score: \t   0.453\n",
      "------------------------------\n",
      "Epoch: 96\n",
      "Loss:\t\t 0.35688\n",
      "Learning rate: 0.00009181\n",
      "Precision:\t 54.622%\n",
      "Recall:\t\t 36.075%\n",
      "F1-score: \t   0.435\n",
      "------------------------------\n",
      "Epoch: 97\n",
      "Loss:\t\t 0.35872\n",
      "Learning rate: 0.00009020\n",
      "Precision:\t 59.302%\n",
      "Recall:\t\t 33.447%\n",
      "F1-score: \t   0.428\n",
      "------------------------------\n",
      "Epoch: 98\n",
      "Loss:\t\t 0.33944\n",
      "Learning rate: 0.00008863\n",
      "Precision:\t 46.880%\n",
      "Recall:\t\t 35.981%\n",
      "F1-score: \t   0.407\n",
      "------------------------------\n",
      "Epoch: 99\n",
      "Loss:\t\t 0.33817\n",
      "Learning rate: 0.00008707\n",
      "Precision:\t 55.242%\n",
      "Recall:\t\t 35.664%\n",
      "F1-score: \t   0.433\n",
      "------------------------------\n",
      "Epoch: 100\n",
      "Loss:\t\t 0.33884\n",
      "Learning rate: 0.00008555\n",
      "Precision:\t 55.245%\n",
      "Recall:\t\t 35.242%\n",
      "F1-score: \t   0.430\n",
      "------------------------------\n",
      "Epoch: 101\n",
      "Loss:\t\t 0.34432\n",
      "Learning rate: 0.00008405\n",
      "Precision:\t 53.171%\n",
      "Recall:\t\t 39.338%\n",
      "F1-score: \t   0.452\n",
      "------------------------------\n",
      "Epoch: 102\n",
      "Loss:\t\t 0.34009\n",
      "Learning rate: 0.00008258\n",
      "Precision:\t 50.304%\n",
      "Recall:\t\t 34.771%\n",
      "F1-score: \t   0.411\n",
      "------------------------------\n",
      "Epoch: 103\n",
      "Loss:\t\t 0.34554\n",
      "Learning rate: 0.00008114\n",
      "Precision:\t 56.958%\n",
      "Recall:\t\t 35.868%\n",
      "F1-score: \t   0.440\n",
      "------------------------------\n",
      "Epoch: 104\n",
      "Loss:\t\t 0.33556\n",
      "Learning rate: 0.00007972\n",
      "Precision:\t 53.529%\n",
      "Recall:\t\t 36.584%\n",
      "F1-score: \t   0.435\n",
      "------------------------------\n",
      "Epoch: 105\n",
      "Loss:\t\t 0.34120\n",
      "Learning rate: 0.00007832\n",
      "Precision:\t 47.801%\n",
      "Recall:\t\t 33.494%\n",
      "F1-score: \t   0.394\n",
      "------------------------------\n",
      "Epoch: 106\n",
      "Loss:\t\t 0.32889\n",
      "Learning rate: 0.00007695\n",
      "Precision:\t 54.403%\n",
      "Recall:\t\t 33.398%\n",
      "F1-score: \t   0.414\n",
      "------------------------------\n",
      "Epoch: 107\n",
      "Loss:\t\t 0.35992\n",
      "Learning rate: 0.00007561\n",
      "Precision:\t 57.765%\n",
      "Recall:\t\t 34.378%\n",
      "F1-score: \t   0.431\n",
      "------------------------------\n",
      "Epoch: 108\n",
      "Loss:\t\t 0.33981\n",
      "Learning rate: 0.00007428\n",
      "Precision:\t 51.480%\n",
      "Recall:\t\t 38.749%\n",
      "F1-score: \t   0.442\n",
      "------------------------------\n",
      "Epoch: 109\n",
      "Loss:\t\t 0.33499\n",
      "Learning rate: 0.00007298\n",
      "Precision:\t 55.972%\n",
      "Recall:\t\t 34.314%\n",
      "F1-score: \t   0.425\n",
      "------------------------------\n",
      "Epoch: 110\n",
      "Loss:\t\t 0.33464\n",
      "Learning rate: 0.00007171\n",
      "Precision:\t 51.256%\n",
      "Recall:\t\t 32.868%\n",
      "F1-score: \t   0.401\n",
      "------------------------------\n",
      "Epoch: 111\n",
      "Loss:\t\t 0.33221\n",
      "Learning rate: 0.00007045\n",
      "Precision:\t 49.624%\n",
      "Recall:\t\t 39.380%\n",
      "F1-score: \t   0.439\n",
      "------------------------------\n",
      "Epoch: 112\n",
      "Loss:\t\t 0.33084\n",
      "Learning rate: 0.00006922\n",
      "Precision:\t 53.141%\n",
      "Recall:\t\t 39.221%\n",
      "F1-score: \t   0.451\n",
      "------------------------------\n",
      "Epoch: 113\n",
      "Loss:\t\t 0.33923\n",
      "Learning rate: 0.00006801\n",
      "Precision:\t 51.084%\n",
      "Recall:\t\t 38.860%\n",
      "F1-score: \t   0.441\n",
      "------------------------------\n",
      "Epoch: 114\n",
      "Loss:\t\t 0.31634\n",
      "Learning rate: 0.00006682\n",
      "Precision:\t 54.055%\n",
      "Recall:\t\t 39.338%\n",
      "F1-score: \t   0.455\n",
      "------------------------------\n",
      "Epoch: 115\n",
      "Loss:\t\t 0.34336\n",
      "Learning rate: 0.00006565\n",
      "Precision:\t 48.550%\n",
      "Recall:\t\t 37.810%\n",
      "F1-score: \t   0.425\n",
      "------------------------------\n",
      "Epoch: 116\n",
      "Loss:\t\t 0.33297\n",
      "Learning rate: 0.00006450\n",
      "Precision:\t 53.429%\n",
      "Recall:\t\t 42.028%\n",
      "F1-score: \t   0.470\n",
      "------------------------------\n",
      "Epoch: 117\n",
      "Loss:\t\t 0.34248\n",
      "Learning rate: 0.00006337\n",
      "Precision:\t 53.590%\n",
      "Recall:\t\t 37.967%\n",
      "F1-score: \t   0.444\n",
      "------------------------------\n",
      "Epoch: 118\n",
      "Loss:\t\t 0.34317\n",
      "Learning rate: 0.00006226\n",
      "Precision:\t 57.148%\n",
      "Recall:\t\t 32.668%\n",
      "F1-score: \t   0.416\n",
      "------------------------------\n",
      "Epoch: 119\n",
      "Loss:\t\t 0.35259\n",
      "Learning rate: 0.00006117\n",
      "Precision:\t 57.895%\n",
      "Recall:\t\t 36.768%\n",
      "F1-score: \t   0.450\n",
      "------------------------------\n",
      "Epoch: 120\n",
      "Loss:\t\t 0.32606\n",
      "Learning rate: 0.00006010\n",
      "Precision:\t 51.171%\n",
      "Recall:\t\t 36.203%\n",
      "F1-score: \t   0.424\n",
      "------------------------------\n",
      "Epoch: 121\n",
      "Loss:\t\t 0.33620\n",
      "Learning rate: 0.00005905\n",
      "Precision:\t 51.585%\n",
      "Recall:\t\t 37.859%\n",
      "F1-score: \t   0.437\n",
      "------------------------------\n",
      "Epoch: 122\n",
      "Loss:\t\t 0.32076\n",
      "Learning rate: 0.00005802\n",
      "Precision:\t 56.389%\n",
      "Recall:\t\t 38.694%\n",
      "F1-score: \t   0.459\n",
      "------------------------------\n",
      "Epoch: 123\n",
      "Loss:\t\t 0.33005\n",
      "Learning rate: 0.00005700\n",
      "Precision:\t 54.988%\n",
      "Recall:\t\t 38.327%\n",
      "F1-score: \t   0.452\n",
      "------------------------------\n",
      "Epoch: 124\n",
      "Loss:\t\t 0.34953\n",
      "Learning rate: 0.00005600\n",
      "Precision:\t 58.408%\n",
      "Recall:\t\t 37.256%\n",
      "F1-score: \t   0.455\n"
     ]
    }
   ],
   "source": [
    "losses = []\n",
    "\n",
    "for epoch in tqdm(range(num_epoch)):\n",
    "    epoch_losses = []\n",
    "    \n",
    "    # Обучаем модель\n",
    "    for X, Y in train_loader:\n",
    "        X = X.to(device)\n",
    "        Y = Y.to(device)\n",
    "\n",
    "        optimizer.zero_grad()\n",
    "        output = model(X)\n",
    "        \n",
    "        loss = tverskoy_loss(output, Y)\n",
    "        loss.backward()\n",
    "        clip_grad_norm_(model.parameters(), 99999)\n",
    "        \n",
    "        optimizer.step()\n",
    "\n",
    "        del X\n",
    "        del Y\n",
    "        torch.cuda.empty_cache()\n",
    "        epoch_losses.append(loss.item())\n",
    "        \n",
    "    # Добавляем лосс\n",
    "    common_loss = sum(epoch_losses) / len(epoch_losses)\n",
    "    losses.append(common_loss)\n",
    "    \n",
    "    # Считаем метрики на валидации:\n",
    "    valid_precision = []\n",
    "    valid_recall = []\n",
    "    for X, Y in valid_loader:\n",
    "        X = X.to(device)\n",
    "        rec = recall(model(X).cpu(), Y)\n",
    "        prec = precision(model(X).cpu(), Y)\n",
    "        if prec is not np.nan:\n",
    "            valid_precision.append(prec)\n",
    "        if rec is not np.nan:\n",
    "            valid_recall.append(rec)\n",
    "        del X\n",
    "        del Y\n",
    "    \n",
    "    # Выводим информацию\n",
    "    print(\"--\" * 15)\n",
    "    print(\"Epoch: {}\".format(str(epoch)))\n",
    "    print(\"Loss:\\t\\t {:7.5f}\".format(common_loss))\n",
    "    print(\"Learning rate: {:9.8f}\".format(float(optimizer.state_dict()[\"param_groups\"][0][\"lr\"])))\n",
    "    \n",
    "    try:\n",
    "        prec = sum(valid_precision) / len(valid_precision)\n",
    "        print(\"Precision:\\t {:7.3%}\".format(prec))\n",
    "    except:\n",
    "        print(\"Precision:\\t No info\")\n",
    "        \n",
    "    try:\n",
    "        rec = sum(valid_recall) / len(valid_recall)\n",
    "        print(\"Recall:\\t\\t {:7.3%}\".format(rec))\n",
    "    except:\n",
    "        print(\"Recall:\\t\\t No info\")\n",
    "        \n",
    "    try:\n",
    "        print(\"F1-score: \\t {:7.3f}\".format(f1_score(prec, rec)))\n",
    "    except:\n",
    "        print(\"F1-score: No info\".format(f1_score(prec, rec)))\n",
    "    \n",
    "    # Сохраняем модель и делаем шаг scheduler\n",
    "    torch.save(model, \"lungs_ct_model_\" + str(epoch + previous_i) + \".h5\")\n",
    "    scheduler.step()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8c36e4e",
   "metadata": {
    "papermill": {
     "duration": 0.080839,
     "end_time": "2022-01-13T22:55:40.556510",
     "exception": false,
     "start_time": "2022-01-13T22:55:40.475671",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Таким образом, мы обучили модель и она достигла приемлемого результата"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "c51f8ca8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-13T22:55:40.720773Z",
     "iopub.status.busy": "2022-01-13T22:55:40.720172Z",
     "iopub.status.idle": "2022-01-13T22:55:51.507951Z",
     "shell.execute_reply": "2022-01-13T22:55:51.507472Z",
     "shell.execute_reply.started": "2022-01-13T20:36:11.570917Z"
    },
    "papermill": {
     "duration": 10.869046,
     "end_time": "2022-01-13T22:55:51.508100",
     "exception": false,
     "start_time": "2022-01-13T22:55:40.639054",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import roc_curve\n",
    "\n",
    "y_real = []\n",
    "y_pred = []\n",
    "for X, Y in valid_loader:\n",
    "    X = X.to(device)\n",
    "    y_real += Y.view(Y.shape[0] * Y.shape[1] * Y.shape[2] * Y.shape[3]).int().cpu().detach().tolist()\n",
    "    y_pred += model(X).view(Y.shape[0] * Y.shape[1] * Y.shape[2] * Y.shape[3]).cpu().detach().tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "1d3772c8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-13T22:56:08.042203Z",
     "iopub.status.busy": "2022-01-13T22:56:01.919611Z",
     "iopub.status.idle": "2022-01-13T22:56:33.005827Z",
     "shell.execute_reply": "2022-01-13T22:56:33.006686Z",
     "shell.execute_reply.started": "2022-01-13T20:36:55.501487Z"
    },
    "papermill": {
     "duration": 41.427271,
     "end_time": "2022-01-13T22:56:33.006927",
     "exception": false,
     "start_time": "2022-01-13T22:55:51.579656",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 864x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.figure(figsize=(12, 12))\n",
    "fpr, tpr, thresholds = roc_curve(np.array(y_real), np.array(y_pred))\n",
    "plt.plot(fpr, tpr, 'b', linewidth=3)\n",
    "plt.plot([0, 1], [0, 1], 'k--')\n",
    "plt.plot([0, 0], [0, 1], 'k')\n",
    "plt.plot([1, 1], [0, 1], 'k')\n",
    "plt.plot([0, 1], [0, 0], 'k')\n",
    "plt.plot([0, 1], [1, 1], 'k')\n",
    "plt.xlabel('False positive rate')\n",
    "plt.ylabel('True positive rate')\n",
    "plt.xlim((0, 1))\n",
    "plt.ylim((0, 1))\n",
    "plt.axis('equal')\n",
    "plt.title('ROC curve')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "4c1c97bf",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-13T22:56:53.454717Z",
     "iopub.status.busy": "2022-01-13T22:56:53.453422Z",
     "iopub.status.idle": "2022-01-13T22:57:38.157359Z",
     "shell.execute_reply": "2022-01-13T22:57:38.157813Z",
     "shell.execute_reply.started": "2022-01-13T20:40:01.940026Z"
    },
    "papermill": {
     "duration": 65.010854,
     "end_time": "2022-01-13T22:57:38.157967",
     "exception": false,
     "start_time": "2022-01-13T22:56:33.147113",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6345963155850477"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import roc_auc_score\n",
    "\n",
    "roc_auc_score(y_real, y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d063bf2e",
   "metadata": {
    "id": "1C1JAvvK8gKO",
    "papermill": {
     "duration": 0.074511,
     "end_time": "2022-01-13T22:57:38.307570",
     "exception": false,
     "start_time": "2022-01-13T22:57:38.233059",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### 4. Визуализация"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "2a20ab69",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-13T22:57:38.469663Z",
     "iopub.status.busy": "2022-01-13T22:57:38.465099Z",
     "iopub.status.idle": "2022-01-13T22:57:39.204955Z",
     "shell.execute_reply": "2022-01-13T22:57:39.205836Z",
     "shell.execute_reply.started": "2022-01-13T20:41:36.966772Z"
    },
    "id": "pJ_UA6zE8fXp",
    "outputId": "a1451820-596a-44b2-a3d1-26a897bc1b54",
    "papermill": {
     "duration": 0.823954,
     "end_time": "2022-01-13T22:57:39.206068",
     "exception": false,
     "start_time": "2022-01-13T22:57:38.382114",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.7/site-packages/torchvision/transforms/transforms.py:716: UserWarning: Argument interpolation should be of type InterpolationMode instead of int. Please, use InterpolationMode enum.\n",
      "  \"Argument interpolation should be of type InterpolationMode instead of int. \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0. 0. 0. ... 0. 0. 0.]\n",
      " [0. 0. 0. ... 0. 0. 0.]\n",
      " [0. 0. 0. ... 0. 0. 0.]\n",
      " ...\n",
      " [0. 0. 0. ... 0. 0. 0.]\n",
      " [0. 0. 0. ... 0. 0. 0.]\n",
      " [0. 0. 0. ... 0. 0. 0.]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x1296 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Visualize some of the slices\n",
    "from PIL import Image\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def blend(image, mask): # Функция возвращает картинку с наложенной на неё маской - label, которые являются ковидной штукой\n",
    "    print(image)\n",
    "    image = image.astype(np.float32)\n",
    "    min_in = image.min()\n",
    "    max_in = image.max()\n",
    "    image = (image - min_in) / (max_in - min_in + 1e-8) * 255\n",
    "    image = np.dstack((image, image, image)).astype(np.uint8)\n",
    "    zeros = np.zeros_like(mask)\n",
    "    mask = np.dstack((zeros, zeros, mask * 255)).astype(np.uint8)\n",
    "    return Image.blend(\n",
    "        Image.fromarray(image),\n",
    "        Image.fromarray(mask),\n",
    "        alpha=.3\n",
    "    )\n",
    "\n",
    "slices_num = (100, )\n",
    "slices = []\n",
    "for idx in slices_num:\n",
    "    k = valid_dataset[idx]\n",
    "    slices.append(blend(\n",
    "        k[0][0].numpy(),\n",
    "        k[1][0].numpy()\n",
    "    ))\n",
    "    prediction = model.forward(k[0].view(1, 1, 512, 512).to(device)).cpu().detach().transpose(0, 1).transpose(1, 2).transpose(2, 3)[0]\n",
    "    prediction[prediction >= 0.99] = 1\n",
    "    prediction[prediction < 0.99] = 0\n",
    "    slices.append(\n",
    "        torch.cat([prediction, prediction, prediction], 2)\n",
    "    )\n",
    "\n",
    "figure = plt.figure(figsize=(18, 18))\n",
    "for i, image in enumerate(slices):\n",
    "    ax = figure.add_subplot(1, len(slices), i + 1)\n",
    "    ax.imshow(slices[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "101187c1",
   "metadata": {
    "id": "FVtWkPSI8egB",
    "papermill": {
     "duration": 0.141841,
     "end_time": "2022-01-13T22:57:39.489876",
     "exception": false,
     "start_time": "2022-01-13T22:57:39.348035",
     "status": "completed"
    },
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   "source": [
    "### 5. Inference\n",
    "\n",
    "Ниже код только для отправки решения на **Kaggle**"
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    "\"\"\"\n",
    "Load testing data into images and labels lists\n",
    "\n",
    "images list consists of CT scans -  numpy arrays of shape (512, 512, n_slices)\n",
    "\"\"\"\n",
    "with open(core_path + 'testing_data.json', 'r') as f:\n",
    "    dict_testing = json.load(f)\n",
    "\n",
    "images_testing = []\n",
    "label_testing = []\n",
    "for entry in tqdm(dict_testing):\n",
    "    image = nib.load(os.path.join(path + \"images/\", entry['image'][:-3]))\n",
    "    images_testing.append(image.get_fdata())"
   ]
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   "source": [
    "def rle_encoding(x):\n",
    "    dots = np.where(x.T.flatten() >= 1)[0]\n",
    "    run_lengths = []\n",
    "    prev = -2\n",
    "    for b in dots:\n",
    "        if (b > prev + 1):\n",
    "            run_lengths.extend((b + 1, 0))\n",
    "        run_lengths[-1] += 1\n",
    "        prev = b\n",
    "    return [str(item) for item in run_lengths]"
   ]
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    "thresh = 0.99\n",
    "n_id = len(images_testing)\n",
    "\n",
    "for i in range(n_id):\n",
    "    n_imgs = images_testing[i][1].shape[-1]\n",
    "    name = images_testing[i][0]\n",
    "    \n",
    "    for j in range(n_imgs):\n",
    "        inp = images_testing[i][:, :, j]\n",
    "\n",
    "        inp = torch.from_numpy(np.array(inp))\n",
    "        inp = inp.to(device)\n",
    "        inp = inp.type(torch.float)\n",
    "        inp = inp.view(-1, 1, 512, 512)\n",
    "\n",
    "        res = model(inp)\n",
    "        res = res.cpu()\n",
    "        outp = res.detach().numpy()[0][0]\n",
    "\n",
    "        outp /= np.max(outp)\n",
    "        outp[outp >= thresh] = 1\n",
    "        outp[outp < thresh] = 0\n",
    "\n",
    "        label_testing.append(outp)"
   ]
  },
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   "source": [
    "import csv\n",
    "with open(f'{core_path}testing_data.json', 'r') as f:\n",
    "            dict_testing = json.load(f)\n",
    "\n",
    "with open(f'submission.csv', \"wt\") as sb:\n",
    "    submission_writer = csv.writer(sb, delimiter=',')\n",
    "    submission_writer.writerow([\"Id\", \"Predicted\"])\n",
    "    for k_i, patient_i in tqdm(zip(dict_testing, label_testing)):\n",
    "        submission_writer.writerow([\n",
    "                f\"{k_i['image'][:-7]}\",\n",
    "                \" \".join(rle_encoding(patient_i))\n",
    "            ])"
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