CovidQUEx_Infection_FINAL_cross_val.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "id": "8a066263-6a5c-4f96-8337-98c40fff6b58",
      "metadata": {
        "tags": [],
        "id": "8a066263-6a5c-4f96-8337-98c40fff6b58"
      },
      "source": [
        "### Начало работы"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "0312c7b6-6567-48fc-8898-41bb3848b01c",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2024-05-21T07:26:02.034193Z",
          "iopub.status.busy": "2024-05-21T07:26:02.033389Z",
          "iopub.status.idle": "2024-05-21T07:26:03.601325Z",
          "shell.execute_reply": "2024-05-21T07:26:03.600372Z",
          "shell.execute_reply.started": "2024-05-21T07:26:02.034160Z"
        },
        "tags": [],
        "id": "0312c7b6-6567-48fc-8898-41bb3848b01c"
      },
      "outputs": [],
      "source": [
        "\"Проставляем сиды, чтобы сделать результаты воспроизводимыми\"\n",
        "\n",
        "import random\n",
        "\n",
        "import numpy as np\n",
        "import torch\n",
        "\n",
        "np.random.seed(1)\n",
        "torch.manual_seed(1)\n",
        "torch.cuda.manual_seed(1)\n",
        "random.seed(1)\n",
        "# torch.backends.cudnn.deterministic = True #Сделать результаты воспроизводимыми"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "1007a886-ed98-4983-b9df-c32c8939b216",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2024-05-21T07:26:03.603846Z",
          "iopub.status.busy": "2024-05-21T07:26:03.602661Z",
          "iopub.status.idle": "2024-05-21T07:26:03.613898Z",
          "shell.execute_reply": "2024-05-21T07:26:03.613220Z",
          "shell.execute_reply.started": "2024-05-21T07:26:03.603803Z"
        },
        "tags": [],
        "id": "1007a886-ed98-4983-b9df-c32c8939b216"
      },
      "outputs": [],
      "source": [
        "torch.backends.cudnn.deterministic = True\n",
        "torch.backends.cudnn.benchmark = True"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "6f2b8b1e-32c7-4cb1-ace2-f74ef3db04be",
      "metadata": {
        "tags": [],
        "id": "6f2b8b1e-32c7-4cb1-ace2-f74ef3db04be"
      },
      "source": [
        "### Создаю датафрейм с данными"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "0c00e19c-1e09-4436-8add-1dc7984c15ce",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2024-05-21T07:26:05.102397Z",
          "iopub.status.busy": "2024-05-21T07:26:05.101125Z",
          "iopub.status.idle": "2024-05-21T07:26:05.121275Z",
          "shell.execute_reply": "2024-05-21T07:26:05.120495Z",
          "shell.execute_reply.started": "2024-05-21T07:26:05.102344Z"
        },
        "tags": [],
        "id": "0c00e19c-1e09-4436-8add-1dc7984c15ce"
      },
      "outputs": [],
      "source": [
        "# ''' Ячейка 1: Скачиваю данные '''\n",
        "# import os\n",
        "# os.system(f'unzip -q -n \"Infection Segmentation Data.zip\"')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "b6768632-d0dc-4949-a77e-b6275793c92a",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2024-05-21T07:26:05.245117Z",
          "iopub.status.busy": "2024-05-21T07:26:05.243966Z",
          "iopub.status.idle": "2024-05-21T07:26:05.260660Z",
          "shell.execute_reply": "2024-05-21T07:26:05.259937Z",
          "shell.execute_reply.started": "2024-05-21T07:26:05.245077Z"
        },
        "tags": [],
        "id": "b6768632-d0dc-4949-a77e-b6275793c92a"
      },
      "outputs": [],
      "source": [
        "\"Если из Visual Studio\"\n",
        "import os\n",
        "train_dir = '/home/jupyter/datasphere/project/Segmentation Tests/Infection Segmentation Data/Infection Segmentation Data/Train'\n",
        "val_dir = '/home/jupyter/datasphere/project/Segmentation Tests/Infection Segmentation Data/Infection Segmentation Data/Val'\n",
        "test_dir = '/home/jupyter/datasphere/project/Segmentation Tests/Infection Segmentation Data/Infection Segmentation Data/Test'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "c9450bf8-c505-43d4-b352-889dd0c70b3b",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2024-05-21T07:26:05.363881Z",
          "iopub.status.busy": "2024-05-21T07:26:05.363064Z",
          "iopub.status.idle": "2024-05-21T07:26:05.372666Z",
          "shell.execute_reply": "2024-05-21T07:26:05.371949Z",
          "shell.execute_reply.started": "2024-05-21T07:26:05.363849Z"
        },
        "tags": [],
        "id": "c9450bf8-c505-43d4-b352-889dd0c70b3b"
      },
      "outputs": [],
      "source": [
        "import pandas as pd"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "2141f34a-2d12-40e9-a030-bc82b92be911",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2024-05-21T07:26:05.576670Z",
          "iopub.status.busy": "2024-05-21T07:26:05.575478Z",
          "iopub.status.idle": "2024-05-21T07:26:05.621372Z",
          "shell.execute_reply": "2024-05-21T07:26:05.620612Z",
          "shell.execute_reply.started": "2024-05-21T07:26:05.576620Z"
        },
        "tags": [],
        "id": "2141f34a-2d12-40e9-a030-bc82b92be911"
      },
      "outputs": [],
      "source": [
        "\"Функция которая создает датафрейм с данными\"\n",
        "\n",
        "def make_df_with_path(pathdir):\n",
        "    image_paths, mask_paths, infection_paths = [],[],[]\n",
        "    for folder in os.listdir(pathdir):\n",
        "        images = os.path.join(pathdir, folder, 'images')\n",
        "        masks = os.path.join(pathdir, folder, 'lung masks')\n",
        "        infections = os.path.join(pathdir, folder, 'infection masks')\n",
        "        image_paths.extend([os.path.join(images, file) for file in os.listdir(images)])\n",
        "        mask_paths.extend([os.path.join(masks, file) for file in os.listdir(masks)])\n",
        "        infection_paths.extend([os.path.join(infections, file) for file in os.listdir(infections)])\n",
        "    return pd.DataFrame({'image': image_paths, 'mask': mask_paths, 'infection': infection_paths})\n",
        "\n",
        "train = make_df_with_path(train_dir)\n",
        "val = make_df_with_path(val_dir)\n",
        "test = make_df_with_path(test_dir)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "763e980c-2cd5-4f60-a986-9d644910f45c",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2024-05-21T07:26:05.684393Z",
          "iopub.status.busy": "2024-05-21T07:26:05.683570Z",
          "iopub.status.idle": "2024-05-21T07:26:05.710301Z",
          "shell.execute_reply": "2024-05-21T07:26:05.709617Z",
          "shell.execute_reply.started": "2024-05-21T07:26:05.684360Z"
        },
        "tags": [],
        "id": "763e980c-2cd5-4f60-a986-9d644910f45c",
        "outputId": "716d377e-be27-49e1-cf4a-22b887259647"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>image</th>\n",
              "      <th>mask</th>\n",
              "      <th>infection</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>/home/jupyter/datasphere/project/Segmentation ...</td>\n",
              "      <td>/home/jupyter/datasphere/project/Segmentation ...</td>\n",
              "      <td>/home/jupyter/datasphere/project/Segmentation ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>/home/jupyter/datasphere/project/Segmentation ...</td>\n",
              "      <td>/home/jupyter/datasphere/project/Segmentation ...</td>\n",
              "      <td>/home/jupyter/datasphere/project/Segmentation ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>/home/jupyter/datasphere/project/Segmentation ...</td>\n",
              "      <td>/home/jupyter/datasphere/project/Segmentation ...</td>\n",
              "      <td>/home/jupyter/datasphere/project/Segmentation ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>/home/jupyter/datasphere/project/Segmentation ...</td>\n",
              "      <td>/home/jupyter/datasphere/project/Segmentation ...</td>\n",
              "      <td>/home/jupyter/datasphere/project/Segmentation ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>/home/jupyter/datasphere/project/Segmentation ...</td>\n",
              "      <td>/home/jupyter/datasphere/project/Segmentation ...</td>\n",
              "      <td>/home/jupyter/datasphere/project/Segmentation ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3723</th>\n",
              "      <td>/home/jupyter/datasphere/project/Segmentation ...</td>\n",
              "      <td>/home/jupyter/datasphere/project/Segmentation ...</td>\n",
              "      <td>/home/jupyter/datasphere/project/Segmentation ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3724</th>\n",
              "      <td>/home/jupyter/datasphere/project/Segmentation ...</td>\n",
              "      <td>/home/jupyter/datasphere/project/Segmentation ...</td>\n",
              "      <td>/home/jupyter/datasphere/project/Segmentation ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3725</th>\n",
              "      <td>/home/jupyter/datasphere/project/Segmentation ...</td>\n",
              "      <td>/home/jupyter/datasphere/project/Segmentation ...</td>\n",
              "      <td>/home/jupyter/datasphere/project/Segmentation ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3726</th>\n",
              "      <td>/home/jupyter/datasphere/project/Segmentation ...</td>\n",
              "      <td>/home/jupyter/datasphere/project/Segmentation ...</td>\n",
              "      <td>/home/jupyter/datasphere/project/Segmentation ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3727</th>\n",
              "      <td>/home/jupyter/datasphere/project/Segmentation ...</td>\n",
              "      <td>/home/jupyter/datasphere/project/Segmentation ...</td>\n",
              "      <td>/home/jupyter/datasphere/project/Segmentation ...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>3728 rows × 3 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                  image  ...                                          infection\n",
              "0     /home/jupyter/datasphere/project/Segmentation ...  ...  /home/jupyter/datasphere/project/Segmentation ...\n",
              "1     /home/jupyter/datasphere/project/Segmentation ...  ...  /home/jupyter/datasphere/project/Segmentation ...\n",
              "2     /home/jupyter/datasphere/project/Segmentation ...  ...  /home/jupyter/datasphere/project/Segmentation ...\n",
              "3     /home/jupyter/datasphere/project/Segmentation ...  ...  /home/jupyter/datasphere/project/Segmentation ...\n",
              "4     /home/jupyter/datasphere/project/Segmentation ...  ...  /home/jupyter/datasphere/project/Segmentation ...\n",
              "...                                                 ...  ...                                                ...\n",
              "3723  /home/jupyter/datasphere/project/Segmentation ...  ...  /home/jupyter/datasphere/project/Segmentation ...\n",
              "3724  /home/jupyter/datasphere/project/Segmentation ...  ...  /home/jupyter/datasphere/project/Segmentation ...\n",
              "3725  /home/jupyter/datasphere/project/Segmentation ...  ...  /home/jupyter/datasphere/project/Segmentation ...\n",
              "3726  /home/jupyter/datasphere/project/Segmentation ...  ...  /home/jupyter/datasphere/project/Segmentation ...\n",
              "3727  /home/jupyter/datasphere/project/Segmentation ...  ...  /home/jupyter/datasphere/project/Segmentation ...\n",
              "\n",
              "[3728 rows x 3 columns]"
            ]
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "train"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "698ac930-f6e3-4a34-b3fb-a03290296b01",
      "metadata": {
        "jp-MarkdownHeadingCollapsed": true,
        "tags": [],
        "id": "698ac930-f6e3-4a34-b3fb-a03290296b01"
      },
      "source": [
        "### DataLoader"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "032ab3c9-0e41-42de-bf94-0e3493eb7283",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2024-05-21T07:26:08.021126Z",
          "iopub.status.busy": "2024-05-21T07:26:08.020079Z",
          "iopub.status.idle": "2024-05-21T07:26:08.034649Z",
          "shell.execute_reply": "2024-05-21T07:26:08.033902Z",
          "shell.execute_reply.started": "2024-05-21T07:26:08.021079Z"
        },
        "tags": [],
        "id": "032ab3c9-0e41-42de-bf94-0e3493eb7283"
      },
      "outputs": [],
      "source": [
        "from PIL import Image\n",
        "from torch.utils.data import Dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "649c96d5-04cd-4cbd-b3e8-c8d056364aba",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2024-05-21T07:26:08.158510Z",
          "iopub.status.busy": "2024-05-21T07:26:08.157399Z",
          "iopub.status.idle": "2024-05-21T07:26:08.172693Z",
          "shell.execute_reply": "2024-05-21T07:26:08.171943Z",
          "shell.execute_reply.started": "2024-05-21T07:26:08.158460Z"
        },
        "tags": [],
        "id": "649c96d5-04cd-4cbd-b3e8-c8d056364aba"
      },
      "outputs": [],
      "source": [
        "\"Создание класса для набора данных, вариант изображение + инфекция\"\n",
        "class InfectionData(Dataset):\n",
        "    def __init__(self, df, transform = None, augmentation = None):\n",
        "        self.df = df\n",
        "        self.transform = transform\n",
        "        self.augmentation = augmentation\n",
        "        self.image = df['image'].tolist()\n",
        "        self.infection = df['infection'].tolist()\n",
        "    def __len__(self):\n",
        "        return len(self.image)\n",
        "    def __getitem__(self, idx):\n",
        "        image_path = self.image[idx]\n",
        "        infection_path = self.infection[idx]\n",
        "        if not os.path.exists(image_path) or not os.path.exists(infection_path):\n",
        "            print(f'Файл не найдет {image_path} или {infection_path}')\n",
        "\n",
        "        image = Image.open(image_path).convert('L')\n",
        "        infection = Image.open(infection_path).convert('L')\n",
        "\n",
        "        if self.augmentation:\n",
        "            image, infection = self.augmentation(image,infection)\n",
        "        if self.transform:\n",
        "            image, infection = self.transform(image, infection)\n",
        "\n",
        "        return image, infection"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "ceb4c075-4321-48ad-8058-bb0481916d5d",
      "metadata": {
        "jp-MarkdownHeadingCollapsed": true,
        "tags": [],
        "id": "ceb4c075-4321-48ad-8058-bb0481916d5d"
      },
      "source": [
        "### Transforms"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "bdf60c22-c215-45a4-a30f-cef2d226aeaf",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2024-05-21T07:26:11.206149Z",
          "iopub.status.busy": "2024-05-21T07:26:11.205009Z",
          "iopub.status.idle": "2024-05-21T07:26:11.387742Z",
          "shell.execute_reply": "2024-05-21T07:26:11.386930Z",
          "shell.execute_reply.started": "2024-05-21T07:26:11.206104Z"
        },
        "tags": [],
        "id": "bdf60c22-c215-45a4-a30f-cef2d226aeaf",
        "outputId": "46bb92ee-10c1-46c3-a55f-4be93d71b736"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/requests/__init__.py:102: RequestsDependencyWarning: urllib3 (2.2.1) or chardet (4.0.0)/charset_normalizer (2.0.12) doesn't match a supported version!\n",
            "  warnings.warn(\"urllib3 ({}) or chardet ({})/charset_normalizer ({}) doesn't match a supported \"\n"
          ]
        }
      ],
      "source": [
        "import torch\n",
        "from torchvision import transforms\n",
        "\n",
        "PATCH_SIZE = (256, 256)\n",
        "\n",
        "def Transform(image, infection):\n",
        "    image_transform = transforms.Compose([\n",
        "        transforms.Resize(size=PATCH_SIZE, antialias=True),\n",
        "        transforms.ToTensor() # Ensure ToTensor is before Normalize\n",
        "    ])\n",
        "    infection_transform = transforms.Compose([\n",
        "        transforms.ToTensor(),\n",
        "        transforms.Resize(size=PATCH_SIZE, antialias=False)\n",
        "    ])\n",
        "    return image_transform(image), infection_transform(infection).type(torch.int)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "16fdac84-bbfb-4a28-b458-8d87481293ae",
      "metadata": {
        "tags": [],
        "id": "16fdac84-bbfb-4a28-b458-8d87481293ae"
      },
      "source": [
        "### Augmentation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "bf2f25e7-ae4e-40ac-acf8-7e93abdf8ce3",
      "metadata": {
        "id": "bf2f25e7-ae4e-40ac-acf8-7e93abdf8ce3"
      },
      "outputs": [],
      "source": [
        "# def augmentation(image, infection):\n",
        "#     augmentation_transform = A.Compose([\n",
        "#         A.HorizontalFlip(p=0.5),\n",
        "#         A.VerticalFlip(p=0.5),\n",
        "#         A.RandomRotate90(p=0.5),\n",
        "#         A.Transpose(p=0.5),\n",
        "#         A.ShiftScaleRotate(shift_limit=0.01, scale_limit=0.04, rotate_limit=0, p=0.25)\n",
        "#         transforms.RandomAffine(\n",
        "#         degrees=(-15, 15),         # Rotation\n",
        "#         translate=(0.2, 0.2),      # Translation\n",
        "#         scale=(0.8, 1.2),          # Scale\n",
        "#         shear=(-15, 15)            # Shear\n",
        "#                                 )\n",
        "#         A.RandomBrightnessContrast(brightness_limit=(-0.1, 0.1), contrast_limit=(0, 0), p=0.5),  # Случайные изменения яркости\n",
        "#         A.MultiplicativeNoise(multiplier=(-0.08, 0.08), p=0.5),  # Изменение яркости через умножение\n",
        "#         A.GammaTransform(gamma_limit=(-0.08, 0.08), p=0.5),  # Изменение гаммы\n",
        "\n",
        "#     ])\n",
        "#     return augmentation_transform(image), augmentation_transform(infection)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "8c587d65-8b0b-41f0-abd6-235bcbc0a532",
      "metadata": {
        "tags": [],
        "id": "8c587d65-8b0b-41f0-abd6-235bcbc0a532"
      },
      "source": [
        "### Метрики"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "3b78e322-234a-44ad-90ac-207bf17ae6c6",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2024-05-21T07:26:38.820479Z",
          "iopub.status.busy": "2024-05-21T07:26:38.819243Z",
          "iopub.status.idle": "2024-05-21T07:26:41.354730Z",
          "shell.execute_reply": "2024-05-21T07:26:41.353912Z",
          "shell.execute_reply.started": "2024-05-21T07:26:38.820436Z"
        },
        "tags": [],
        "id": "3b78e322-234a-44ad-90ac-207bf17ae6c6"
      },
      "outputs": [],
      "source": [
        "\"Супер полезная библиотека с моделями сегментации и лосами для торча\"\n",
        "\n",
        "try:\n",
        "    import segmentation_models_pytorch as smp\n",
        "except ImportError:\n",
        "    %pip install -q segmentation_models_pytorch\n",
        "    import segmentation_models_pytorch as smp"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "a81cdc93-94ac-42d3-84ba-313bd1c2b4df",
      "metadata": {
        "id": "a81cdc93-94ac-42d3-84ba-313bd1c2b4df"
      },
      "outputs": [],
      "source": [
        "\"BCE_DICE с регуляризацией\"\n",
        "\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "\n",
        "class DiceBCELoss(nn.Module):\n",
        "    def __init__(self, weight=None, size_average=True, lambda_l2=0.01):\n",
        "        super(DiceBCELoss, self).__init__()\n",
        "        self.lambda_l2 = lambda_l2  # Коэффициент регуляризации\n",
        "\n",
        "    def forward(self, inputs, targets, model, smooth=1):\n",
        "        # Применение сигмоиды\n",
        "        inputs = torch.sigmoid(inputs)\n",
        "\n",
        "        # Сглаживание массивов\n",
        "        inputs_flat = inputs.view(-1)\n",
        "        targets_flat = targets.view(-1)\n",
        "\n",
        "        # Вычисление пересечения\n",
        "        intersection = (inputs_flat * targets_flat).sum()\n",
        "        dice_loss = 1 - (2. * intersection + smooth) / (inputs_flat.sum() + targets_flat.sum() + smooth)\n",
        "        BCE = F.binary_cross_entropy(inputs_flat, targets_flat, reduction='mean')\n",
        "\n",
        "        # Суммирование Binary Cross-Entropy и Dice Loss\n",
        "        Dice_BCE = BCE + dice_loss\n",
        "\n",
        "        # Вычисление L2 регуляризации\n",
        "        l2_reg = sum(p.pow(2.0).sum() for p in model.parameters())\n",
        "        Dice_BCE_reg = Dice_BCE + self.lambda_l2 * l2_reg\n",
        "\n",
        "        return Dice_BCE_reg\n",
        "\n",
        "# Использование\n",
        "lambda_l2 = 0.01  # Задание коэффициента регуляризации\n",
        "BCE_Dice = DiceBCELoss(lambda_l2=lambda_l2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "c826f537-325b-4b63-bb8a-cc4c9f4efe78",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2024-05-21T07:26:41.356681Z",
          "iopub.status.busy": "2024-05-21T07:26:41.356188Z",
          "iopub.status.idle": "2024-05-21T07:26:41.370058Z",
          "shell.execute_reply": "2024-05-21T07:26:41.369390Z",
          "shell.execute_reply.started": "2024-05-21T07:26:41.356653Z"
        },
        "tags": [],
        "id": "c826f537-325b-4b63-bb8a-cc4c9f4efe78"
      },
      "outputs": [],
      "source": [
        "\"BCE_DICE без регуляризации\"\n",
        "#PyTorch\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "class DiceBCELoss(nn.Module):\n",
        "    def __init__(self, weight=None, size_average=True):\n",
        "        super(DiceBCELoss, self).__init__()\n",
        "\n",
        "    def forward(self, inputs, targets, smooth=1):\n",
        "\n",
        "        #comment out if your model contains a sigmoid or equivalent activation layer\n",
        "        inputs = F.sigmoid(inputs)\n",
        "\n",
        "        #flatten label and prediction tensors\n",
        "        inputs = inputs.view(-1)\n",
        "        targets = targets.view(-1)\n",
        "\n",
        "        intersection = (inputs * targets).sum()\n",
        "        dice_loss = 1 - (2.*intersection + smooth)/(inputs.sum() + targets.sum() + smooth)\n",
        "        BCE = F.binary_cross_entropy(inputs, targets, reduction='mean')\n",
        "        Dice_BCE = BCE + dice_loss\n",
        "\n",
        "        return Dice_BCE\n",
        "BCE_Dice = DiceBCELoss()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "651d0d92-eaae-478a-bdb6-dda0144458b2",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2024-05-21T07:26:41.371780Z",
          "iopub.status.busy": "2024-05-21T07:26:41.370936Z",
          "iopub.status.idle": "2024-05-21T07:26:41.386690Z",
          "shell.execute_reply": "2024-05-21T07:26:41.385984Z",
          "shell.execute_reply.started": "2024-05-21T07:26:41.371751Z"
        },
        "tags": [],
        "id": "651d0d92-eaae-478a-bdb6-dda0144458b2"
      },
      "outputs": [],
      "source": [
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "\n",
        "#PyTorch\n",
        "ALPHA = 0.7\n",
        "BETA = 0.3\n",
        "GAMMA = 1.3\n",
        "\n",
        "class FocalTverskyLoss(nn.Module):\n",
        "    def __init__(self, weight=None, size_average=True):\n",
        "        super(FocalTverskyLoss, self).__init__()\n",
        "\n",
        "    def forward(self, inputs, targets, smooth=1, alpha=ALPHA, beta=BETA, gamma=GAMMA):\n",
        "\n",
        "        #comment out if your model contains a sigmoid or equivalent activation layer\n",
        "        inputs = F.sigmoid(inputs)\n",
        "\n",
        "        #flatten label and prediction tensors\n",
        "        inputs = inputs.view(-1)\n",
        "        targets = targets.view(-1)\n",
        "\n",
        "        #True Positives, False Positives & False Negatives\n",
        "        TP = (inputs * targets).sum()\n",
        "        FP = ((1-targets) * inputs).sum()\n",
        "        FN = (targets * (1-inputs)).sum()\n",
        "\n",
        "        Tversky = (TP + smooth) / (TP + alpha*FP + beta*FN + smooth)\n",
        "        FocalTversky = (1 - Tversky)**gamma\n",
        "\n",
        "        return FocalTversky\n",
        "FocalTversky = FocalTverskyLoss()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "d1c9f551-bea0-47db-82c1-e387d229babe",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2024-05-21T07:26:41.389077Z",
          "iopub.status.busy": "2024-05-21T07:26:41.388314Z",
          "iopub.status.idle": "2024-05-21T07:26:41.401457Z",
          "shell.execute_reply": "2024-05-21T07:26:41.400749Z",
          "shell.execute_reply.started": "2024-05-21T07:26:41.389047Z"
        },
        "tags": [],
        "id": "d1c9f551-bea0-47db-82c1-e387d229babe"
      },
      "outputs": [],
      "source": [
        "\"Определяю метрики и лосс\"\n",
        "\n",
        "JaccardLoss = smp.losses.JaccardLoss(mode='multilabel')\n",
        "DiceLoss    = smp.losses.DiceLoss(mode='multilabel')\n",
        "BCELoss     = smp.losses.SoftBCEWithLogitsLoss()\n",
        "LovaszLoss  = smp.losses.LovaszLoss(mode='multilabel', per_image=False)\n",
        "TverskyLoss = smp.losses.TverskyLoss(alpha=0.5, beta=0.7, mode='multilabel', log_loss=False)\n",
        "\n",
        "def dice_coef(y_true, y_pred, thr=0.5, dim=(2,3), epsilon=0.001):\n",
        "    y_true = y_true.to(torch.float32)\n",
        "    y_pred = (y_pred>thr).to(torch.float32)\n",
        "    inter = (y_true*y_pred).sum(dim=dim)\n",
        "    den = y_true.sum(dim=dim) + y_pred.sum(dim=dim)\n",
        "    dice = ((2*inter+epsilon)/(den+epsilon)).mean(dim=(1,0))\n",
        "    return dice\n",
        "\n",
        "def iou_coef(y_true, y_pred, thr=0.5, dim=(2,3), epsilon=0.001):\n",
        "    y_true = y_true.to(torch.float32)\n",
        "    y_pred = (y_pred>thr).to(torch.float32)\n",
        "    inter = (y_true*y_pred).sum(dim=dim)\n",
        "    union = (y_true + y_pred - y_true*y_pred).sum(dim=dim)\n",
        "    iou = ((inter+epsilon)/(union+epsilon)).mean(dim=(1,0))\n",
        "    return iou\n",
        "\n",
        "def criterion(y_pred, y_true):\n",
        "    return TverskyLoss(y_pred, y_true)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "c1409ed6-b471-4bc7-9694-9a060a271ca7",
      "metadata": {
        "tags": [],
        "id": "c1409ed6-b471-4bc7-9694-9a060a271ca7"
      },
      "source": [
        "### Создание Train, Val, Test наборов"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "9d8c13e3-c56f-491f-9e02-306c5ffd4e3a",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2024-05-21T07:26:41.403066Z",
          "iopub.status.busy": "2024-05-21T07:26:41.402311Z",
          "iopub.status.idle": "2024-05-21T07:26:41.414706Z",
          "shell.execute_reply": "2024-05-21T07:26:41.413916Z",
          "shell.execute_reply.started": "2024-05-21T07:26:41.403037Z"
        },
        "tags": [],
        "id": "9d8c13e3-c56f-491f-9e02-306c5ffd4e3a",
        "outputId": "159be364-41e3-4447-fce6-6cad3d7039a7"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Количество физических ядер процессора: 8\n",
            "Общий объем RAM: 94.30 ГБ\n"
          ]
        }
      ],
      "source": [
        "\"Проверь количество ядер, чтобы выставить оптимальный num_workers\"\n",
        "\n",
        "import psutil\n",
        "\n",
        "# Количество ядер процессора\n",
        "cpu_cores = psutil.cpu_count(logical=False)\n",
        "print(f\"Количество физических ядер процессора: {cpu_cores}\")\n",
        "\n",
        "# Общий объем оперативной памяти\n",
        "total_ram = psutil.virtual_memory().total / (1024 ** 3)  # в ГБ\n",
        "print(f\"Общий объем RAM: {total_ram:.2f} ГБ\")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "1c9f1d17-2f49-4f26-a858-b3c4ae4aae22",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2024-05-21T07:26:41.416016Z",
          "iopub.status.busy": "2024-05-21T07:26:41.415550Z",
          "iopub.status.idle": "2024-05-21T07:26:41.535606Z",
          "shell.execute_reply": "2024-05-21T07:26:41.534791Z",
          "shell.execute_reply.started": "2024-05-21T07:26:41.415989Z"
        },
        "tags": [],
        "id": "1c9f1d17-2f49-4f26-a858-b3c4ae4aae22",
        "outputId": "72f53903-0173-4a6d-d947-6ae6a832aee0"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "'cuda'"
            ]
          },
          "execution_count": 16,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from torch.utils.data import DataLoader\n",
        "\n",
        "BATCH_SIZE = 32\n",
        "DEVICE = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
        "DEVICE"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "952c5c01-7509-4bfd-8a82-110fdb22af21",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2024-05-21T07:26:41.537449Z",
          "iopub.status.busy": "2024-05-21T07:26:41.536541Z",
          "iopub.status.idle": "2024-05-21T07:26:41.548206Z",
          "shell.execute_reply": "2024-05-21T07:26:41.547533Z",
          "shell.execute_reply.started": "2024-05-21T07:26:41.537418Z"
        },
        "tags": [],
        "id": "952c5c01-7509-4bfd-8a82-110fdb22af21"
      },
      "outputs": [],
      "source": [
        "\"Вариант изображение + инфекция\"\n",
        "\n",
        "train_dataset = InfectionData(train, transform=Transform)\n",
        "train_loader = DataLoader(train_dataset, batch_size = BATCH_SIZE,shuffle = True, num_workers= 4)\n",
        "\n",
        "val_dataset = InfectionData(val, transform=Transform)\n",
        "val_loader = DataLoader(val_dataset, batch_size = BATCH_SIZE,shuffle = True, num_workers = 4)\n",
        "\n",
        "test_dataset = InfectionData(test, transform = Transform)\n",
        "test_loader = DataLoader(test_dataset, batch_size = BATCH_SIZE,shuffle = True, num_workers = 0)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "2e694c05-1e9f-4e8e-aeec-cd922321feb4",
      "metadata": {
        "tags": [],
        "id": "2e694c05-1e9f-4e8e-aeec-cd922321feb4"
      },
      "source": [
        "### Нужные библиотеки"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "55f92c72-578b-4dd3-b79f-5e363a65efa7",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2024-05-21T07:26:41.549919Z",
          "iopub.status.busy": "2024-05-21T07:26:41.549113Z",
          "iopub.status.idle": "2024-05-21T07:26:55.153903Z",
          "shell.execute_reply": "2024-05-21T07:26:55.152970Z",
          "shell.execute_reply.started": "2024-05-21T07:26:41.549890Z"
        },
        "tags": [],
        "id": "55f92c72-578b-4dd3-b79f-5e363a65efa7",
        "outputId": "aa4093aa-d9af-4f04-ea82-d2ac61f52ad0"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.0.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.0\u001b[0m\n",
            "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n",
            "\n",
            "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.0.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.0\u001b[0m\n",
            "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n"
          ]
        }
      ],
      "source": [
        "\"Импорт библиотек с git\"\n",
        "\n",
        "try:\n",
        "    import segmentation_models_pytorch as smp\n",
        "except ImportError:\n",
        "    %pip install -q segmentation_models_pytorch\n",
        "    import segmentation_models_pytorch as smp\n",
        "\n",
        "try:\n",
        "    import pytorch_warmup as warmup\n",
        "except ImportError:\n",
        "    %pip install -U pytorch_warmup -q\n",
        "    import pytorch_warmup as warmup\n",
        "\n",
        "%pip install git+https://github.com/ildoonet/pytorch-gradual-warmup-lr.git -q\n",
        "%pip install 'git+https://github.com/katsura-jp/pytorch-cosine-annealing-with-warmup' -q\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "09c5919e-a572-497b-9b74-c9d45e3e9523",
      "metadata": {
        "tags": [],
        "id": "09c5919e-a572-497b-9b74-c9d45e3e9523"
      },
      "source": [
        "### Инициализация W&B и установка pkbar для отслеживания"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "47f6460c-4f25-4a79-aacd-3b7f36211037",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2024-05-21T07:26:55.157366Z",
          "iopub.status.busy": "2024-05-21T07:26:55.156649Z",
          "iopub.status.idle": "2024-05-21T07:26:55.178038Z",
          "shell.execute_reply": "2024-05-21T07:26:55.177281Z",
          "shell.execute_reply.started": "2024-05-21T07:26:55.157331Z"
        },
        "tags": [],
        "id": "47f6460c-4f25-4a79-aacd-3b7f36211037"
      },
      "outputs": [],
      "source": [
        "try:\n",
        "    import pkbar\n",
        "except ImportError:\n",
        "    %pip install -q pkbar\n",
        "    import pkbar"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "5d62d8a0-5fdc-4a31-a1ef-b67867c10824",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2024-05-21T07:26:55.179787Z",
          "iopub.status.busy": "2024-05-21T07:26:55.178987Z",
          "iopub.status.idle": "2024-05-21T07:26:55.189331Z",
          "shell.execute_reply": "2024-05-21T07:26:55.188622Z",
          "shell.execute_reply.started": "2024-05-21T07:26:55.179756Z"
        },
        "tags": [],
        "id": "5d62d8a0-5fdc-4a31-a1ef-b67867c10824"
      },
      "outputs": [],
      "source": [
        "try:\n",
        "    import wandb\n",
        "except ImportError:\n",
        "    %pip install -q wandb\n",
        "    import wandb"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "1aa11c96-4e77-4bca-bd83-df237cb42bbb",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2024-05-21T07:26:55.191071Z",
          "iopub.status.busy": "2024-05-21T07:26:55.190260Z",
          "iopub.status.idle": "2024-05-21T07:26:58.072327Z",
          "shell.execute_reply": "2024-05-21T07:26:58.071447Z",
          "shell.execute_reply.started": "2024-05-21T07:26:55.191040Z"
        },
        "tags": [],
        "id": "1aa11c96-4e77-4bca-bd83-df237cb42bbb",
        "outputId": "fb6d83f3-b57e-4a52-e734-5191604e0815"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/requests/__init__.py:102: RequestsDependencyWarning: urllib3 (2.2.1) or chardet (4.0.0)/charset_normalizer (2.0.12) doesn't match a supported version!\n",
            "  warnings.warn(\"urllib3 ({}) or chardet ({})/charset_normalizer ({}) doesn't match a supported \"\n",
            "wandb: Currently logged in as: danilaaniva. Use `wandb login --relogin` to force relogin\n",
            "wandb: WARNING If you're specifying your api key in code, ensure this code is not shared publicly.\n",
            "wandb: WARNING Consider setting the WANDB_API_KEY environment variable, or running `wandb login` from the command line.\n",
            "wandb: Appending key for api.wandb.ai to your netrc file: /home/jupyter/.netrc\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "execution_count": 21,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "\"Нужно  поменять ключ\"\n",
        "wandb.login(key='')"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "74f5d430-7f0e-4143-8400-061cf4439b4b",
      "metadata": {
        "jp-MarkdownHeadingCollapsed": true,
        "tags": [],
        "id": "74f5d430-7f0e-4143-8400-061cf4439b4b"
      },
      "source": [
        "### Код TorchTrainer"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "5689ebf8-d198-426d-b00c-b7e422e84973",
      "metadata": {
        "tags": [],
        "id": "5689ebf8-d198-426d-b00c-b7e422e84973"
      },
      "outputs": [],
      "source": [
        "\"Модель\"\n",
        "\n",
        "from warmup_scheduler import GradualWarmupScheduler\n",
        "import torch.optim as optim\n",
        "from torch.optim.lr_scheduler import ReduceLROnPlateau\n",
        "import torch.optim.lr_scheduler as lr_scheduler\n",
        "from torch.optim.lr_scheduler import ExponentialLR\n",
        "from cosine_annealing_warmup import CosineAnnealingWarmupRestarts\n",
        "\n",
        "# ResNet\n",
        "# ResNeXt\n",
        "# ResNeSt\n",
        "# Res2Ne(X)t\n",
        "# RegNet(x/y)\n",
        "# GERNet\n",
        "# SE-Net\n",
        "# SK-ResNe(X)t\n",
        "# DenseNet\n",
        "# Inception\n",
        "# EfficientNet\n",
        "\n",
        "# model = smp.Unet(\n",
        "#     encoder_name = 'timm-efficientnet-b4',\n",
        "#     encoder_weights = 'imagenet',\n",
        "#     in_channels = 1,\n",
        "#     classes = 1\n",
        "# )\n",
        "model = smp.UnetPlusPlus(\n",
        "    encoder_name=\"timm-efficientnet-b4\",  # Choosing a supported encoder\n",
        "    encoder_weights=\"imagenet\",           # Using pre-trained weights from ImageNet\n",
        "    in_channels=1,                        # Number of input channels\n",
        "    classes=1                             # Number of output classes (e.g., for binary classification)\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "ad7d3867-556f-4011-8ba7-52f86bf4a4d6",
      "metadata": {
        "tags": [],
        "id": "ad7d3867-556f-4011-8ba7-52f86bf4a4d6"
      },
      "outputs": [],
      "source": [
        "\n",
        "\n",
        "\"--------Scheduler, optimizer-----------\"\n",
        "\n",
        "LEARNING_RATE = 0.001\n",
        "LR_FACTOR = 0.5\n",
        "LR_PATIENCE = 2\n",
        "EARLY_STOP_PATIENCE = 4\n",
        "\n",
        "# optimizer = torch.optim.AdamW(model.parameters(), lr=LEARNING_RATE, betas=(0.9, 0.999), weight_decay=0.01)\n",
        "from torch.optim import RAdam\n",
        "optimizer = RAdam(model.parameters(), lr=LEARNING_RATE, betas=(0.9, 0.999), weight_decay=0.01)\n",
        "\n",
        "\n",
        "\"Пробую разные learning rate schedulers\"\n",
        "# scheduler_warmupr = warmup.UntunedLinearWarmup(optimizer)\n",
        "\n",
        "\n",
        "\"Warmup + CosineAnealing\"\n",
        "# scheduler = CosineAnnealingWarmupRestarts(optimizer,\n",
        "#                                           first_cycle_steps=10,\n",
        "#                                           cycle_mult=1.0,\n",
        "#                                           max_lr=0.01,\n",
        "#                                           min_lr=0.000001,\n",
        "#                                           warmup_steps=4,\n",
        "#                                           gamma=1.0)\n",
        "\n",
        "# \"Learning Rate Warmup + ExponentialLR\"\n",
        "scheduler = ExponentialLR(optimizer, gamma=0.95)\n",
        "# \"На плато LR падает\"\n",
        "# # scheduler_reduce = ReduceLROnPlateau(optimizer,mode = 'min', factor=LR_FACTOR, patience=LR_PATIENCE, verbose = False)\n",
        "scheduler_warmup = GradualWarmupScheduler(optimizer, multiplier=1, total_epoch=5, after_scheduler=None)\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\"Переменные\"\n",
        "early_stop_counter = 0\n",
        "best_val_loss = float('inf')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "1510a5ee-8a75-4ab5-8d0f-c6aac3af4d38",
      "metadata": {
        "tags": [],
        "id": "1510a5ee-8a75-4ab5-8d0f-c6aac3af4d38"
      },
      "outputs": [],
      "source": [
        "\"Тренировка модели. Вариант №1. На вход изображение + инфекция\"\n",
        "\n",
        "def train_model(model, train_loader, val_loader, criterion, optimizer,scheduler, num_epochs, early_stop_patience,encoder_name = 'Unknown', decoder_name = 'U-NET'):\n",
        "    try:\n",
        "        wandb.init(project = 'Segmentation Infection New', config = {\n",
        "        'learning_rate': LEARNING_RATE,\n",
        "        'epochs': num_epochs,\n",
        "        'batch_size':train_loader.batch_size,\n",
        "        'encoder_name':encoder_name,\n",
        "        'decoder_name':decoder_name})\n",
        "        early_stop_counter = 0\n",
        "        best_val_loss = float('inf')\n",
        "        model.to(DEVICE)\n",
        "        criterion.to(DEVICE)\n",
        "\n",
        "        wandb.watch(model, criterion, log=\"all\", log_freq=10)\n",
        "\n",
        "        train_losses = []\n",
        "        val_losses = []\n",
        "        train_dice_scores_stat, train_iou_scores_stat = [], []\n",
        "        val_dice_scores_stat, val_iou_scores_stat = [], []\n",
        "\n",
        "        optimizer.zero_grad()  # Избежание предупреждения, issue #8\n",
        "        optimizer.step()\n",
        "\n",
        "        for epoch in range(1, num_epochs+1):\n",
        "            model.train()\n",
        "            scheduler_warmup.step()  # Exponential LR + Warmup\n",
        "            if epoch == 1:\n",
        "              print(f'Epoch {epoch}: Начальный Learning Rate: {optimizer.param_groups[0][\"lr\"]}')\n",
        "            train_loss = 0.0\n",
        "            dice_scores = []\n",
        "            jaccard_scores = []\n",
        "            kbar = pkbar.Kbar(target = len(train_loader), epoch = epoch-1, width = 20, always_stateful=False)\n",
        "            for batch_index, (images, infections) in enumerate(train_loader):\n",
        "                images, infections = images.to(DEVICE), infections.to(DEVICE)\n",
        "                optimizer.zero_grad()\n",
        "                outputs = model(images)\n",
        "                loss = criterion(outputs, infections.float())\n",
        "                loss.backward()\n",
        "                optimizer.step()\n",
        "\n",
        "                train_loss += loss.item()\n",
        "\n",
        "                dice = dice_coef(infections, outputs)\n",
        "                jaccard = iou_coef(infections, outputs)\n",
        "                dice_scores.append(dice.item())\n",
        "                jaccard_scores.append(jaccard.item())\n",
        "\n",
        "                kbar.update(batch_index, values=[(\"loss\", loss.item()), (\"dice\", dice.item()), (\"jaccard\", jaccard.item())])\n",
        "\n",
        "\n",
        "\n",
        "            avg_train_loss = train_loss/len(train_loader)\n",
        "            avg_train_iou = sum(jaccard_scores) / len(jaccard_scores)\n",
        "            avg_train_dice = sum(dice_scores) / len(dice_scores)\n",
        "            train_losses.append(avg_train_loss)\n",
        "            train_dice_scores_stat.append(avg_train_dice)\n",
        "            train_iou_scores_stat.append(avg_train_iou)\n",
        "\n",
        "            wandb.log({\n",
        "                \"epoch\": epoch,\n",
        "                \"train_loss\": avg_train_loss,\n",
        "                \"train_dice\": avg_train_dice,\n",
        "                \"train_iou\": avg_train_iou\n",
        "            })\n",
        "\n",
        "            model.eval()\n",
        "            val_loss = 0.0\n",
        "            val_dice_scores = []\n",
        "            val_iou_scores = []\n",
        "            with torch.no_grad():\n",
        "                for images, infections in val_loader:\n",
        "                    images, infections = images.to(DEVICE), infections.to(DEVICE)\n",
        "                    outputs = model(images)\n",
        "                    loss = criterion(outputs, infections.float())\n",
        "                    val_loss += loss.item()\n",
        "                    dice = dice_coef(infections, outputs)\n",
        "                    iou = iou_coef(infections, outputs)\n",
        "                    val_dice_scores.append(dice.item())\n",
        "                    val_iou_scores.append(iou.item())\n",
        "            avg_val_loss = val_loss / len(val_loader)\n",
        "            avg_val_dice = sum(val_dice_scores)/len(val_dice_scores)\n",
        "            avg_val_iou = sum(val_iou_scores)/len(val_iou_scores)\n",
        "\n",
        "            val_losses.append(avg_val_loss)\n",
        "            val_dice_scores_stat.append(avg_val_dice)\n",
        "            val_iou_scores_stat.append(avg_val_iou)\n",
        "            # scheduler.step() #Cosine Annealing\n",
        "            # scheduler.step(val_loss) #ReduceLROnPlateu\n",
        "            wandb.log({\n",
        "                \"epoch\": epoch,\n",
        "                \"val_loss\": avg_val_loss,\n",
        "                \"val_dice\": avg_val_dice,\n",
        "                \"val_iou\": avg_val_iou\n",
        "            })\n",
        "\n",
        "\n",
        "            # scheduler.step(avg_val_loss) #Обычный\n",
        "            kbar.add(1, values=[(\"loss\", avg_train_loss), (\"dice\", avg_train_dice), (\"jaccard\", avg_train_iou),\n",
        "                                (\"val_loss\", avg_val_loss), (\"val_dice\", avg_val_dice), (\"val_jaccard\", avg_val_iou)])\n",
        "\n",
        "            current_lr = optimizer.param_groups[0]['lr']\n",
        "            print(f'Epoch {epoch}: Current Learning Rate: {current_lr}')\n",
        "\n",
        "            if avg_val_loss < best_val_loss:\n",
        "                best_val_loss = avg_val_loss\n",
        "                torch.save(model.state_dict(), 'best_model.pth')\n",
        "                early_stop_counter = 0\n",
        "            else:\n",
        "                early_stop_counter+=1\n",
        "            if early_stop_counter >= early_stop_patience:\n",
        "                print(f'Early stop counter : {early_stop_counter}.')\n",
        "                # break\n",
        "        model.load_state_dict(torch.load('best_model.pth'))\n",
        "    except Exception as e:\n",
        "        print(f\"Произошла ошибка: {e}\")\n",
        "    finally:\n",
        "        wandb.finish()\n",
        "    return (model, train_losses, val_losses, train_dice_scores_stat,train_iou_scores_stat, val_dice_scores_stat,val_iou_scores_stat)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "8a835d10-8805-4b18-9f00-2b0f5ad55f2b",
      "metadata": {
        "tags": [],
        "id": "8a835d10-8805-4b18-9f00-2b0f5ad55f2b"
      },
      "source": [
        "### С ReduceLrOnPlateu"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "675a1dd5-319a-4ea0-afc1-f818a68dd5d6",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2024-05-21T08:25:12.471065Z",
          "iopub.status.busy": "2024-05-21T08:25:12.469979Z",
          "iopub.status.idle": "2024-05-21T08:25:13.173466Z",
          "shell.execute_reply": "2024-05-21T08:25:13.172568Z",
          "shell.execute_reply.started": "2024-05-21T08:25:12.471028Z"
        },
        "tags": [],
        "id": "675a1dd5-319a-4ea0-afc1-f818a68dd5d6"
      },
      "outputs": [],
      "source": [
        "\"Модель\"\n",
        "\n",
        "from warmup_scheduler import GradualWarmupScheduler\n",
        "import torch.optim as optim\n",
        "from torch.optim.lr_scheduler import ReduceLROnPlateau\n",
        "import torch.optim.lr_scheduler as lr_scheduler\n",
        "from torch.optim.lr_scheduler import ExponentialLR\n",
        "from cosine_annealing_warmup import CosineAnnealingWarmupRestarts\n",
        "\n",
        "# ResNet\n",
        "# ResNeXt\n",
        "# ResNeSt\n",
        "# Res2Ne(X)t\n",
        "# RegNet(x/y)\n",
        "# GERNet\n",
        "# SE-Net\n",
        "# SK-ResNe(X)t\n",
        "# DenseNet\n",
        "# Inception\n",
        "# EfficientNet\n",
        "\n",
        "# model = smp.Unet(\n",
        "#     encoder_name = 'timm-efficientnet-b4',\n",
        "#     encoder_weights = 'imagenet',\n",
        "#     in_channels = 1,\n",
        "#     classes = 1\n",
        "# )\n",
        "# model = smp.DeepLabV3Plus(\n",
        "#     encoder_name=\"timm-efficientnet-b4\",  # Вы можете выбрать другой encoder, поддерживаемый библиотекой\n",
        "#     encoder_weights=\"imagenet\",           # Использование весов, предобученных на ImageNet\n",
        "#     in_channels=1,                        # Количество входных каналов\n",
        "#     classes=1                             # Количество выходных классов (например, для бинарной классификации)\n",
        "# )\n",
        "\n",
        "# # \"Надо попробовать PSPNet, PAN и MAnet (Пробуй Manet, он для Tumor Segmentation)\"\n",
        "# model = smp.FPN(\n",
        "#     encoder_name=\"timm-efficientnet-b4\",  # Выбор энкодера, поддерживаемого библиотекой\n",
        "#     encoder_weights=\"imagenet\",           # Использование весов, предобученных на ImageNet\n",
        "#     in_channels=1,                        # Количество входных каналов\n",
        "#     classes=1                             # Количество выходных классов (например, для бинарной классификации)\n",
        "# )\n",
        "\n",
        "import segmentation_models_pytorch as smp\n",
        "\n",
        "# MA-Net model initialization\n",
        "model = smp.UnetPlusPlus(\n",
        "    encoder_name=\"timm-efficientnet-b4\",  # Choosing a supported encoder\n",
        "    encoder_weights=\"imagenet\",           # Using pre-trained weights from ImageNet\n",
        "    in_channels=1,                        # Number of input channels\n",
        "    classes=1                             # Number of output classes (e.g., for binary classification)\n",
        ")\n",
        "\n",
        "# MA-Net model initialization\n",
        "# model = smp.PAN(\n",
        "#     encoder_name=\"timm-efficientnet-b4\",  # Choosing a supported encoder\n",
        "#     encoder_weights=\"imagenet\",           # Using pre-trained weights from ImageNet\n",
        "#     in_channels=1,                        # Number of input channels\n",
        "#     classes=1                             # Number of output classes (e.g., for binary classification)\n",
        "# )\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "1dd50388-c71d-4ecf-ac08-1cb9e467a7d6",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2024-05-21T07:27:00.843111Z",
          "iopub.status.busy": "2024-05-21T07:27:00.841996Z",
          "iopub.status.idle": "2024-05-21T07:27:10.757457Z",
          "shell.execute_reply": "2024-05-21T07:27:10.756392Z",
          "shell.execute_reply.started": "2024-05-21T07:27:00.843064Z"
        },
        "tags": [],
        "id": "1dd50388-c71d-4ecf-ac08-1cb9e467a7d6",
        "outputId": "f0e519d0-4f0c-4579-cdb8-a74826445be7"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.0.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.0\u001b[0m\n",
            "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n"
          ]
        }
      ],
      "source": [
        "%pip install git+https://github.com/LiyuanLucasLiu/RAdam.git -q"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "8e4b7d2b-1453-47c0-bb98-14c5404f5df2",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2024-05-21T08:25:13.175916Z",
          "iopub.status.busy": "2024-05-21T08:25:13.174933Z",
          "iopub.status.idle": "2024-05-21T08:25:13.202870Z",
          "shell.execute_reply": "2024-05-21T08:25:13.202121Z",
          "shell.execute_reply.started": "2024-05-21T08:25:13.175879Z"
        },
        "tags": [],
        "id": "8e4b7d2b-1453-47c0-bb98-14c5404f5df2"
      },
      "outputs": [],
      "source": [
        "\"--------Scheduler, optimizer-----------\"\n",
        "# from radam import RAdam\n",
        "# from torch.optim import RAdam\n",
        "LEARNING_RATE = 0.001\n",
        "LR_FACTOR = 0.2\n",
        "LR_PATIENCE = 4\n",
        "EARLY_STOP_PATIENCE = 10\n",
        "\n",
        "optimizer = torch.optim.AdamW(model.parameters(), lr=LEARNING_RATE, betas=(0.9, 0.999), weight_decay=0.01)\n",
        "# from torch.optim import RAdam\n",
        "# optimizer = RAdam(model.parameters(), lr=LEARNING_RATE, betas=(0.9, 0.999), weight_decay=0.01)\n",
        "\n",
        "\n",
        "\"Пробую разные learning rate schedulers\"\n",
        "scheduler = ReduceLROnPlateau(optimizer, mode='min', factor=LR_FACTOR, patience=LR_PATIENCE)\n",
        "\"Переменные\"\n",
        "early_stop_counter = 0\n",
        "best_val_loss = float('inf')\n",
        "\n",
        "# Segmentation Infection New\n",
        "\"Тренировка модели. Вариант №1. На вход изображение + инфекция\"\n",
        "\n",
        "def train_model(model, train_loader, val_loader, criterion, optimizer,scheduler, num_epochs, early_stop_patience,encoder_name = 'Unknown', decoder_name = 'U-NET'):\n",
        "    wandb.init(project = 'Segmentation Infection DICEloss', config = {\n",
        "    'learning_rate': LEARNING_RATE,\n",
        "    'epochs': num_epochs,\n",
        "    'batch_size':train_loader.batch_size,\n",
        "    'encoder_name':encoder_name,\n",
        "    'decoder_name':decoder_name})\n",
        "    early_stop_counter = 0\n",
        "    best_val_loss = float('inf')\n",
        "    model.to(DEVICE)\n",
        "    criterion.to(DEVICE)\n",
        "\n",
        "    wandb.watch(model, criterion, log=\"all\", log_freq=10)\n",
        "\n",
        "    train_losses = []\n",
        "    val_losses = []\n",
        "    train_dice_scores_stat, train_iou_scores_stat = [], []\n",
        "    val_dice_scores_stat, val_iou_scores_stat = [], []\n",
        "\n",
        "    optimizer.zero_grad()  # Избежание предупреждения, issue #8\n",
        "    optimizer.step()\n",
        "\n",
        "    for epoch in range(1, num_epochs+1):\n",
        "        model.train()\n",
        "        # scheduler_warmup.step()  # Exponential LR + Warmup\n",
        "        if epoch == 1:\n",
        "          print(f'Epoch {epoch}: Начальный Learning Rate: {optimizer.param_groups[0][\"lr\"]}')\n",
        "        train_loss = 0.0\n",
        "        dice_scores = []\n",
        "        jaccard_scores = []\n",
        "        kbar = pkbar.Kbar(target = len(train_loader), epoch = epoch-1, width = 20, always_stateful=False)\n",
        "        for batch_index, (images, infections) in enumerate(train_loader):\n",
        "            images, infections = images.to(DEVICE), infections.to(DEVICE)\n",
        "            optimizer.zero_grad()\n",
        "            outputs = model(images)\n",
        "            loss = criterion(outputs, infections.float())\n",
        "            loss.backward()\n",
        "            optimizer.step()\n",
        "\n",
        "            train_loss += loss.item()\n",
        "\n",
        "            dice = dice_coef(infections, outputs)\n",
        "            jaccard = iou_coef(infections, outputs)\n",
        "            dice_scores.append(dice.item())\n",
        "            jaccard_scores.append(jaccard.item())\n",
        "\n",
        "            kbar.update(batch_index, values=[(\"loss\", loss.item()), (\"dice\", dice.item()), (\"jaccard\", jaccard.item())])\n",
        "\n",
        "\n",
        "\n",
        "        avg_train_loss = train_loss/len(train_loader)\n",
        "        avg_train_iou = sum(jaccard_scores) / len(jaccard_scores)\n",
        "        avg_train_dice = sum(dice_scores) / len(dice_scores)\n",
        "        train_losses.append(avg_train_loss)\n",
        "        train_dice_scores_stat.append(avg_train_dice)\n",
        "        train_iou_scores_stat.append(avg_train_iou)\n",
        "\n",
        "        wandb.log({\n",
        "            \"epoch\": epoch,\n",
        "            \"train_loss\": avg_train_loss,\n",
        "            \"train_dice\": avg_train_dice,\n",
        "            \"train_iou\": avg_train_iou\n",
        "        })\n",
        "\n",
        "        model.eval()\n",
        "        val_loss = 0.0\n",
        "        val_dice_scores = []\n",
        "        val_iou_scores = []\n",
        "        with torch.no_grad():\n",
        "            for images, infections in val_loader:\n",
        "                images, infections = images.to(DEVICE), infections.to(DEVICE)\n",
        "                outputs = model(images)\n",
        "                loss = criterion(outputs, infections.float())\n",
        "                val_loss += loss.item()\n",
        "                dice = dice_coef(infections, outputs)\n",
        "                iou = iou_coef(infections, outputs)\n",
        "                val_dice_scores.append(dice.item())\n",
        "                val_iou_scores.append(iou.item())\n",
        "        avg_val_loss = val_loss / len(val_loader)\n",
        "        avg_val_dice = sum(val_dice_scores)/len(val_dice_scores)\n",
        "        avg_val_iou = sum(val_iou_scores)/len(val_iou_scores)\n",
        "\n",
        "        val_losses.append(avg_val_loss)\n",
        "        val_dice_scores_stat.append(avg_val_dice)\n",
        "        val_iou_scores_stat.append(avg_val_iou)\n",
        "        # scheduler.step() #Cosine Annealing\n",
        "        scheduler.step(val_loss) #ReduceLROnPlateu\n",
        "        wandb.log({\n",
        "            \"epoch\": epoch,\n",
        "            \"val_loss\": avg_val_loss,\n",
        "            \"val_dice\": avg_val_dice,\n",
        "            \"val_iou\": avg_val_iou\n",
        "        })\n",
        "\n",
        "\n",
        "        # scheduler.step(avg_val_loss) #Обычный\n",
        "        kbar.add(1, values=[(\"loss\", avg_train_loss), (\"dice\", avg_train_dice), (\"jaccard\", avg_train_iou),\n",
        "                            (\"val_loss\", avg_val_loss), (\"val_dice\", avg_val_dice), (\"val_jaccard\", avg_val_iou)])\n",
        "\n",
        "        current_lr = optimizer.param_groups[0]['lr']\n",
        "        print(f'Epoch {epoch}: Current Learning Rate: {current_lr}')\n",
        "\n",
        "        if avg_val_loss < best_val_loss:\n",
        "            best_val_loss = avg_val_loss\n",
        "            torch.save(model.state_dict(), 'best_model.pth')\n",
        "            early_stop_counter = 0\n",
        "        else:\n",
        "            early_stop_counter+=1\n",
        "        if early_stop_counter >= early_stop_patience:\n",
        "            print(f'Early stopping after {early_stop_patience} epochs without improvement.')\n",
        "            break\n",
        "    model.load_state_dict(torch.load('best_model.pth'))\n",
        "    return (model, train_losses, val_losses, train_dice_scores_stat,train_iou_scores_stat, val_dice_scores_stat,val_iou_scores_stat)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "2384bc79-cd56-45b2-a221-e3b01b0e0f4a",
      "metadata": {
        "id": "2384bc79-cd56-45b2-a221-e3b01b0e0f4a"
      },
      "source": [
        "### Начало тренировки"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "71dae638-978b-46c4-910c-c2b705ad347a",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2024-05-21T08:25:15.052618Z",
          "iopub.status.busy": "2024-05-21T08:25:15.051442Z",
          "iopub.status.idle": "2024-05-21T08:25:15.377541Z",
          "shell.execute_reply": "2024-05-21T08:25:15.376633Z",
          "shell.execute_reply.started": "2024-05-21T08:25:15.052580Z"
        },
        "tags": [],
        "id": "71dae638-978b-46c4-910c-c2b705ad347a"
      },
      "outputs": [],
      "source": [
        "import gc\n",
        "gc.collect()\n",
        "import torch\n",
        "torch.cuda.empty_cache()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "593cd986-fdc3-4f98-984c-fdf08444b6fa",
      "metadata": {
        "jp-MarkdownHeadingCollapsed": true,
        "tags": [],
        "id": "593cd986-fdc3-4f98-984c-fdf08444b6fa"
      },
      "source": [
        "### Инициализация W&B и тренировка модели"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "c00a9f60-c303-4916-a2ca-ab754b26989f",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2024-05-21T08:06:51.755701Z",
          "iopub.status.busy": "2024-05-21T08:06:51.754546Z",
          "iopub.status.idle": "2024-05-21T08:06:51.765537Z",
          "shell.execute_reply": "2024-05-21T08:06:51.764808Z",
          "shell.execute_reply.started": "2024-05-21T08:06:51.755653Z"
        },
        "tags": [],
        "id": "c00a9f60-c303-4916-a2ca-ab754b26989f"
      },
      "outputs": [],
      "source": [
        "NUM_EPOCHS = 40"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "12d00618-214e-41de-ad92-a02fcd1e58b0",
      "metadata": {
        "tags": [],
        "id": "12d00618-214e-41de-ad92-a02fcd1e58b0"
      },
      "outputs": [],
      "source": [
        "wandb.finish()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "db2c310b-1efe-4c76-8ab4-9f3c8f812002",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2024-05-21T07:27:11.073701Z",
          "iopub.status.busy": "2024-05-21T07:27:11.073194Z",
          "iopub.status.idle": "2024-05-21T07:48:59.258137Z",
          "shell.execute_reply": "2024-05-21T07:48:59.257154Z",
          "shell.execute_reply.started": "2024-05-21T07:27:11.073674Z"
        },
        "tags": [],
        "id": "db2c310b-1efe-4c76-8ab4-9f3c8f812002",
        "outputId": "9bca06be-6828-4f38-d50f-650e789e87f5"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "wandb: Tracking run with wandb version 0.17.0\n",
            "wandb: Run data is saved locally in /home/jupyter/work/resources/Segmentation Tests/wandb/run-20240521_072711-hrfc4wz1\n",
            "wandb: Run `wandb offline` to turn off syncing.\n",
            "wandb: Syncing run misunderstood-sun-9\n",
            "wandb: ⭐️ View project at https://wandb.ai/danilaaniva/Segmentation%20Infection%20DICEloss\n",
            "wandb: 🚀 View run at https://wandb.ai/danilaaniva/Segmentation%20Infection%20DICEloss/runs/hrfc4wz1\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1: Начальный Learning Rate: 0.001\n",
            "117/117 [====================] - 70s 597ms/step - loss: 0.5054 - dice: 0.7318 - jaccard: 0.6730 - val_loss: 0.3031 - val_dice: 0.8725 - val_jaccard: 0.8142\n",
            "Epoch 1: Current Learning Rate: 0.001\n",
            "117/117 [====================] - 56s 481ms/step - loss: 0.2654 - dice: 0.8600 - jaccard: 0.8059 - val_loss: 0.2387 - val_dice: 0.8781 - val_jaccard: 0.8240\n",
            "Epoch 2: Current Learning Rate: 0.001\n",
            "117/117 [====================] - 56s 480ms/step - loss: 0.2308 - dice: 0.8734 - jaccard: 0.8220 - val_loss: 0.2420 - val_dice: 0.8797 - val_jaccard: 0.8277\n",
            "Epoch 3: Current Learning Rate: 0.001\n",
            "117/117 [====================] - 55s 474ms/step - loss: 0.2015 - dice: 0.8855 - jaccard: 0.8366 - val_loss: 0.2643 - val_dice: 0.8610 - val_jaccard: 0.8087\n",
            "Epoch 4: Current Learning Rate: 0.001\n",
            "117/117 [====================] - 56s 481ms/step - loss: 0.1973 - dice: 0.8819 - jaccard: 0.8344 - val_loss: 0.2574 - val_dice: 0.7641 - val_jaccard: 0.7114\n",
            "Epoch 5: Current Learning Rate: 0.001\n",
            "117/117 [====================] - 56s 481ms/step - loss: 0.1769 - dice: 0.8944 - jaccard: 0.8481 - val_loss: 0.2289 - val_dice: 0.8679 - val_jaccard: 0.8162\n",
            "Epoch 6: Current Learning Rate: 0.001\n",
            "117/117 [====================] - 56s 475ms/step - loss: 0.1638 - dice: 0.8992 - jaccard: 0.8547 - val_loss: 0.2129 - val_dice: 0.8960 - val_jaccard: 0.8455\n",
            "Epoch 7: Current Learning Rate: 0.001\n",
            "117/117 [====================] - 56s 481ms/step - loss: 0.1469 - dice: 0.9137 - jaccard: 0.8706 - val_loss: 0.2328 - val_dice: 0.8857 - val_jaccard: 0.8354\n",
            "Epoch 8: Current Learning Rate: 0.001\n",
            "117/117 [====================] - 56s 482ms/step - loss: 0.1392 - dice: 0.9178 - jaccard: 0.8757 - val_loss: 0.2232 - val_dice: 0.8971 - val_jaccard: 0.8465\n",
            "Epoch 9: Current Learning Rate: 0.001\n",
            "117/117 [====================] - 56s 481ms/step - loss: 0.1239 - dice: 0.9251 - jaccard: 0.8851 - val_loss: 0.2447 - val_dice: 0.8942 - val_jaccard: 0.8447\n",
            "Epoch 10: Current Learning Rate: 0.001\n",
            "117/117 [====================] - 56s 475ms/step - loss: 0.1257 - dice: 0.9230 - jaccard: 0.8830 - val_loss: 0.2317 - val_dice: 0.8919 - val_jaccard: 0.8384\n",
            "Epoch 11: Current Learning Rate: 0.001\n",
            "117/117 [====================] - 56s 480ms/step - loss: 0.1103 - dice: 0.9335 - jaccard: 0.8956 - val_loss: 0.2133 - val_dice: 0.8958 - val_jaccard: 0.8457\n",
            "Epoch 12: Current Learning Rate: 0.0002\n",
            "117/117 [====================] - 57s 484ms/step - loss: 0.0956 - dice: 0.9453 - jaccard: 0.9105 - val_loss: 0.2067 - val_dice: 0.8968 - val_jaccard: 0.8469\n",
            "Epoch 13: Current Learning Rate: 0.0002\n",
            "117/117 [====================] - 55s 474ms/step - loss: 0.0881 - dice: 0.9485 - jaccard: 0.9149 - val_loss: 0.2107 - val_dice: 0.8965 - val_jaccard: 0.8467\n",
            "Epoch 14: Current Learning Rate: 0.0002\n",
            "117/117 [====================] - 56s 480ms/step - loss: 0.0832 - dice: 0.9527 - jaccard: 0.9207 - val_loss: 0.2278 - val_dice: 0.8887 - val_jaccard: 0.8373\n",
            "Epoch 15: Current Learning Rate: 0.0002\n",
            "117/117 [====================] - 56s 481ms/step - loss: 0.0799 - dice: 0.9538 - jaccard: 0.9226 - val_loss: 0.2238 - val_dice: 0.8908 - val_jaccard: 0.8388\n",
            "Epoch 16: Current Learning Rate: 0.0002\n",
            "117/117 [====================] - 56s 475ms/step - loss: 0.0763 - dice: 0.9563 - jaccard: 0.9259 - val_loss: 0.2144 - val_dice: 0.8940 - val_jaccard: 0.8441\n",
            "Epoch 17: Current Learning Rate: 0.0002\n",
            "117/117 [====================] - 56s 481ms/step - loss: 0.0728 - dice: 0.9586 - jaccard: 0.9291 - val_loss: 0.2187 - val_dice: 0.8951 - val_jaccard: 0.8445\n",
            "Epoch 18: Current Learning Rate: 4e-05\n",
            "117/117 [====================] - 56s 481ms/step - loss: 0.0698 - dice: 0.9604 - jaccard: 0.9318 - val_loss: 0.2184 - val_dice: 0.8957 - val_jaccard: 0.8441\n",
            "Epoch 19: Current Learning Rate: 4e-05\n",
            "117/117 [====================] - 56s 480ms/step - loss: 0.0681 - dice: 0.9617 - jaccard: 0.9337 - val_loss: 0.2153 - val_dice: 0.8974 - val_jaccard: 0.8472\n",
            "Epoch 20: Current Learning Rate: 4e-05\n",
            "117/117 [====================] - 55s 474ms/step - loss: 0.0690 - dice: 0.9594 - jaccard: 0.9317 - val_loss: 0.2220 - val_dice: 0.8919 - val_jaccard: 0.8389\n",
            "Epoch 21: Current Learning Rate: 4e-05\n",
            "117/117 [====================] - 56s 481ms/step - loss: 0.0665 - dice: 0.9624 - jaccard: 0.9348 - val_loss: 0.2248 - val_dice: 0.8937 - val_jaccard: 0.8421\n",
            "Epoch 22: Current Learning Rate: 4e-05\n",
            "117/117 [====================] - 56s 480ms/step - loss: 0.0658 - dice: 0.9630 - jaccard: 0.9357 - val_loss: 0.2216 - val_dice: 0.8939 - val_jaccard: 0.8423\n",
            "Epoch 23: Current Learning Rate: 8.000000000000001e-06\n",
            "Early stopping after 10 epochs without improvement.\n"
          ]
        }
      ],
      "source": [
        "trained_model = train_model(model, train_loader, val_loader, BCE_Dice, optimizer, scheduler,NUM_EPOCHS,EARLY_STOP_PATIENCE,'EfficientNet-b7, BCE-DICE', 'UnetPlusPlus')"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "cdef33b5-d433-496e-b305-1dfa70f243ff",
      "metadata": {
        "id": "cdef33b5-d433-496e-b305-1dfa70f243ff"
      },
      "source": [
        "### Тренировка с кросс-валидацией"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "a7e88870-bd5f-40e3-8a82-b3aee2de4295",
      "metadata": {
        "id": "a7e88870-bd5f-40e3-8a82-b3aee2de4295"
      },
      "outputs": [],
      "source": [
        "\"--------Scheduler, optimizer-----------\"\n",
        "# from radam import RAdam\n",
        "# from torch.optim import RAdam\n",
        "LEARNING_RATE = 0.001\n",
        "LR_FACTOR = 0.2\n",
        "LR_PATIENCE = 4\n",
        "EARLY_STOP_PATIENCE = 10\n",
        "\n",
        "optimizer = torch.optim.AdamW(model.parameters(), lr=LEARNING_RATE, betas=(0.9, 0.999), weight_decay=0.01)\n",
        "# from torch.optim import RAdam\n",
        "# optimizer = RAdam(model.parameters(), lr=LEARNING_RATE, betas=(0.9, 0.999), weight_decay=0.01)\n",
        "\n",
        "\n",
        "\"Пробую разные learning rate schedulers\"\n",
        "scheduler = ReduceLROnPlateau(optimizer, mode='min', factor=LR_FACTOR, patience=LR_PATIENCE)\n",
        "\"Переменные\"\n",
        "early_stop_counter = 0\n",
        "best_val_loss = float('inf')\n",
        "\n",
        "# Segmentation Infection New\n",
        "\"Тренировка модели. Вариант №1. На вход изображение + инфекция\"\n",
        "\n",
        "def train_model(model, train_loader, val_loader, criterion, optimizer,scheduler, num_epochs, early_stop_patience,encoder_name = 'Unknown', decoder_name = 'U-NET'):\n",
        "    wandb.init(project = 'Segmentation Infection DICEloss', config = {\n",
        "    'learning_rate': LEARNING_RATE,\n",
        "    'epochs': num_epochs,\n",
        "    'batch_size':train_loader.batch_size,\n",
        "    'encoder_name':encoder_name,\n",
        "    'decoder_name':decoder_name})\n",
        "    early_stop_counter = 0\n",
        "    best_val_loss = float('inf')\n",
        "    model.to(DEVICE)\n",
        "    criterion.to(DEVICE)\n",
        "\n",
        "    wandb.watch(model, criterion, log=\"all\", log_freq=10)\n",
        "\n",
        "    train_losses = []\n",
        "    val_losses = []\n",
        "    train_dice_scores_stat, train_iou_scores_stat = [], []\n",
        "    val_dice_scores_stat, val_iou_scores_stat = [], []\n",
        "\n",
        "    optimizer.zero_grad()  # Избежание предупреждения, issue #8\n",
        "    optimizer.step()\n",
        "\n",
        "    for epoch in range(1, num_epochs+1):\n",
        "        model.train()\n",
        "        # scheduler_warmup.step()  # Exponential LR + Warmup\n",
        "        if epoch == 1:\n",
        "          print(f'Epoch {epoch}: Начальный Learning Rate: {optimizer.param_groups[0][\"lr\"]}')\n",
        "        train_loss = 0.0\n",
        "        dice_scores = []\n",
        "        jaccard_scores = []\n",
        "        kbar = pkbar.Kbar(target = len(train_loader), epoch = epoch-1, width = 20, always_stateful=False)\n",
        "        for batch_index, (images, infections) in enumerate(train_loader):\n",
        "            images, infections = images.to(DEVICE), infections.to(DEVICE)\n",
        "            optimizer.zero_grad()\n",
        "            outputs = model(images)\n",
        "            loss = criterion(outputs, infections.float())\n",
        "            loss.backward()\n",
        "            optimizer.step()\n",
        "\n",
        "            train_loss += loss.item()\n",
        "\n",
        "            dice = dice_coef(infections, outputs)\n",
        "            jaccard = iou_coef(infections, outputs)\n",
        "            dice_scores.append(dice.item())\n",
        "            jaccard_scores.append(jaccard.item())\n",
        "\n",
        "            kbar.update(batch_index, values=[(\"loss\", loss.item()), (\"dice\", dice.item()), (\"jaccard\", jaccard.item())])\n",
        "\n",
        "\n",
        "\n",
        "        avg_train_loss = train_loss/len(train_loader)\n",
        "        avg_train_iou = sum(jaccard_scores) / len(jaccard_scores)\n",
        "        avg_train_dice = sum(dice_scores) / len(dice_scores)\n",
        "        train_losses.append(avg_train_loss)\n",
        "        train_dice_scores_stat.append(avg_train_dice)\n",
        "        train_iou_scores_stat.append(avg_train_iou)\n",
        "\n",
        "        wandb.log({\n",
        "            \"epoch\": epoch,\n",
        "            \"train_loss\": avg_train_loss,\n",
        "            \"train_dice\": avg_train_dice,\n",
        "            \"train_iou\": avg_train_iou\n",
        "        })\n",
        "\n",
        "        model.eval()\n",
        "        val_loss = 0.0\n",
        "        val_dice_scores = []\n",
        "        val_iou_scores = []\n",
        "        with torch.no_grad():\n",
        "            for images, infections in val_loader:\n",
        "                images, infections = images.to(DEVICE), infections.to(DEVICE)\n",
        "                outputs = model(images)\n",
        "                loss = criterion(outputs, infections.float())\n",
        "                val_loss += loss.item()\n",
        "                dice = dice_coef(infections, outputs)\n",
        "                iou = iou_coef(infections, outputs)\n",
        "                val_dice_scores.append(dice.item())\n",
        "                val_iou_scores.append(iou.item())\n",
        "        avg_val_loss = val_loss / len(val_loader)\n",
        "        avg_val_dice = sum(val_dice_scores)/len(val_dice_scores)\n",
        "        avg_val_iou = sum(val_iou_scores)/len(val_iou_scores)\n",
        "\n",
        "        val_losses.append(avg_val_loss)\n",
        "        val_dice_scores_stat.append(avg_val_dice)\n",
        "        val_iou_scores_stat.append(avg_val_iou)\n",
        "        # scheduler.step() #Cosine Annealing\n",
        "        scheduler.step(val_loss) #ReduceLROnPlateu\n",
        "        wandb.log({\n",
        "            \"epoch\": epoch,\n",
        "            \"val_loss\": avg_val_loss,\n",
        "            \"val_dice\": avg_val_dice,\n",
        "            \"val_iou\": avg_val_iou\n",
        "        })\n",
        "\n",
        "\n",
        "        # scheduler.step(avg_val_loss) #Обычный\n",
        "        kbar.add(1, values=[(\"loss\", avg_train_loss), (\"dice\", avg_train_dice), (\"jaccard\", avg_train_iou),\n",
        "                            (\"val_loss\", avg_val_loss), (\"val_dice\", avg_val_dice), (\"val_jaccard\", avg_val_iou)])\n",
        "\n",
        "        current_lr = optimizer.param_groups[0]['lr']\n",
        "        print(f'Epoch {epoch}: Current Learning Rate: {current_lr}')\n",
        "\n",
        "        if avg_val_loss < best_val_loss:\n",
        "            best_val_loss = avg_val_loss\n",
        "            torch.save(model.state_dict(), 'best_model.pth')\n",
        "            early_stop_counter = 0\n",
        "        else:\n",
        "            early_stop_counter+=1\n",
        "        if early_stop_counter >= early_stop_patience:\n",
        "            print(f'Early stopping after {early_stop_patience} epochs without improvement.')\n",
        "            break\n",
        "    model.load_state_dict(torch.load('best_model.pth'))\n",
        "    return (model, train_losses, val_losses, train_dice_scores_stat,train_iou_scores_stat, val_dice_scores_stat,val_iou_scores_stat)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "4218c6b6-03cf-43ea-b960-a386753a073a",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2024-05-21T08:25:18.331316Z",
          "iopub.status.busy": "2024-05-21T08:25:18.330218Z",
          "iopub.status.idle": "2024-05-21T08:25:18.356702Z",
          "shell.execute_reply": "2024-05-21T08:25:18.355917Z",
          "shell.execute_reply.started": "2024-05-21T08:25:18.331280Z"
        },
        "tags": [],
        "id": "4218c6b6-03cf-43ea-b960-a386753a073a"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import KFold\n",
        "\n",
        "def train_model_with_cv(model, full_dataset, criterion, optimizer, scheduler, num_epochs, num_splits, early_stop_patience, encoder_name='Unknown', decoder_name='U-NET'):\n",
        "    kf = KFold(n_splits=num_splits, shuffle=True, random_state=42)\n",
        "    all_train_losses = []\n",
        "    all_val_losses = []\n",
        "    all_train_dice_scores = []\n",
        "    all_val_dice_scores = []\n",
        "    all_train_iou_scores = []\n",
        "    all_val_iou_scores = []\n",
        "\n",
        "    for fold, (train_idx, val_idx) in enumerate(kf.split(full_dataset)):\n",
        "        train_subsampler = torch.utils.data.SubsetRandomSampler(train_idx)\n",
        "        val_subsampler = torch.utils.data.SubsetRandomSampler(val_idx)\n",
        "        train_loader = DataLoader(full_dataset, batch_size=BATCH_SIZE, sampler=train_subsampler, num_workers=4)\n",
        "        val_loader = DataLoader(full_dataset, batch_size=BATCH_SIZE, sampler=val_subsampler, num_workers=4)\n",
        "\n",
        "        print(f\"Training fold {fold+1}/{num_splits}\")\n",
        "        trained_model, train_losses, val_losses, train_dice_scores, train_iou_scores, val_dice_scores, val_iou_scores = train_model(\n",
        "            model, train_loader, val_loader, criterion, optimizer, scheduler, num_epochs, early_stop_patience, encoder_name, decoder_name\n",
        "        )\n",
        "        all_train_losses.append(train_losses)\n",
        "        all_val_losses.append(val_losses)\n",
        "        all_train_dice_scores.append(train_dice_scores)\n",
        "        all_train_iou_scores.append(train_iou_scores)\n",
        "        all_val_dice_scores.append(val_dice_scores)\n",
        "        all_val_iou_scores.append(val_iou_scores)\n",
        "\n",
        "    return {\n",
        "        'model': trained_model,\n",
        "        'train_losses': np.mean(all_train_losses, axis=0),\n",
        "        'val_losses': np.mean(all_val_losses, axis=0),\n",
        "        'train_dice_scores': np.mean(all_train_dice_scores, axis=0),\n",
        "        'train_iou_scores': np.mean(all_train_iou_scores, axis=0),\n",
        "        'val_dice_scores': np.mean(all_val_dice_scores, axis=0),\n",
        "        'val_iou_scores': np.mean(all_val_iou_scores, axis=0)\n",
        "    }"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "bab038bb-4fb0-406e-9b96-d46631f28699",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2024-05-21T08:25:19.965851Z",
          "iopub.status.busy": "2024-05-21T08:25:19.964696Z",
          "iopub.status.idle": "2024-05-21T08:25:19.978593Z",
          "shell.execute_reply": "2024-05-21T08:25:19.977880Z",
          "shell.execute_reply.started": "2024-05-21T08:25:19.965816Z"
        },
        "tags": [],
        "id": "bab038bb-4fb0-406e-9b96-d46631f28699"
      },
      "outputs": [],
      "source": [
        "combined_df = pd.concat([train, val, test])\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "b858fb65-e688-4cf2-ae1e-1e5657a693e1",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2024-05-21T08:25:21.025579Z",
          "iopub.status.busy": "2024-05-21T08:25:21.024425Z",
          "iopub.status.idle": "2024-05-21T09:04:31.851536Z",
          "shell.execute_reply": "2024-05-21T09:04:31.850534Z",
          "shell.execute_reply.started": "2024-05-21T08:25:21.025541Z"
        },
        "tags": [],
        "id": "b858fb65-e688-4cf2-ae1e-1e5657a693e1",
        "outputId": "a35203f3-cbf1-4d78-83e1-02ccf27f8406"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Training fold 1/5\n",
            "Epoch 1: Начальный Learning Rate: 0.001\n",
            "146/146 [====================] - 70s 480ms/step - loss: 0.4961 - dice: 0.7615 - jaccard: 0.7034 - val_loss: 0.2851 - val_dice: 0.8700 - val_jaccard: 0.8145\n",
            "Epoch 1: Current Learning Rate: 0.001\n",
            "146/146 [====================] - 71s 483ms/step - loss: 0.2570 - dice: 0.8666 - jaccard: 0.8140 - val_loss: 0.2366 - val_dice: 0.8783 - val_jaccard: 0.8249\n",
            "Epoch 2: Current Learning Rate: 0.001\n",
            "146/146 [====================] - 70s 480ms/step - loss: 0.2255 - dice: 0.8766 - jaccard: 0.8254 - val_loss: 0.2328 - val_dice: 0.8826 - val_jaccard: 0.8298\n",
            "Epoch 3: Current Learning Rate: 0.001\n",
            "146/146 [====================] - 71s 486ms/step - loss: 0.1968 - dice: 0.8920 - jaccard: 0.8434 - val_loss: 0.2323 - val_dice: 0.8722 - val_jaccard: 0.8205\n",
            "Epoch 4: Current Learning Rate: 0.001\n",
            "146/146 [====================] - 71s 484ms/step - loss: 0.1749 - dice: 0.9024 - jaccard: 0.8554 - val_loss: 0.2238 - val_dice: 0.8705 - val_jaccard: 0.8212\n",
            "Epoch 5: Current Learning Rate: 0.001\n",
            "Training fold 2/5\n",
            "Epoch 1: Начальный Learning Rate: 0.001\n",
            "146/146 [====================] - 89s 608ms/step - loss: 0.1912 - dice: 0.8974 - jaccard: 0.8498 - val_loss: 0.1620 - val_dice: 0.8948 - val_jaccard: 0.8490\n",
            "Epoch 1: Current Learning Rate: 0.001\n",
            "146/146 [====================] - 82s 563ms/step - loss: 0.1789 - dice: 0.8973 - jaccard: 0.8516 - val_loss: 0.1670 - val_dice: 0.9049 - val_jaccard: 0.8560\n",
            "Epoch 2: Current Learning Rate: 0.001\n",
            "146/146 [====================] - 81s 557ms/step - loss: 0.1497 - dice: 0.9150 - jaccard: 0.8715 - val_loss: 0.1647 - val_dice: 0.9136 - val_jaccard: 0.8649\n",
            "Epoch 3: Current Learning Rate: 0.001\n",
            "146/146 [====================] - 82s 564ms/step - loss: 0.1406 - dice: 0.9205 - jaccard: 0.8781 - val_loss: 0.1595 - val_dice: 0.9081 - val_jaccard: 0.8604\n",
            "Epoch 4: Current Learning Rate: 0.001\n",
            "146/146 [====================] - 82s 563ms/step - loss: 0.1345 - dice: 0.9220 - jaccard: 0.8809 - val_loss: 0.1801 - val_dice: 0.8960 - val_jaccard: 0.8477\n",
            "Epoch 5: Current Learning Rate: 0.001\n",
            "Training fold 3/5\n",
            "Epoch 1: Начальный Learning Rate: 0.001\n",
            "146/146 [====================] - 93s 638ms/step - loss: 0.1483 - dice: 0.9112 - jaccard: 0.8675 - val_loss: 0.1356 - val_dice: 0.9338 - val_jaccard: 0.8925\n",
            "Epoch 1: Current Learning Rate: 0.001\n",
            "146/146 [====================] - 94s 644ms/step - loss: 0.1356 - dice: 0.9203 - jaccard: 0.8781 - val_loss: 0.1331 - val_dice: 0.9330 - val_jaccard: 0.8921\n",
            "Epoch 2: Current Learning Rate: 0.001\n",
            "146/146 [====================] - 92s 632ms/step - loss: 0.1182 - dice: 0.9310 - jaccard: 0.8913 - val_loss: 0.1277 - val_dice: 0.9333 - val_jaccard: 0.8938\n",
            "Epoch 3: Current Learning Rate: 0.001\n",
            "146/146 [====================] - 94s 647ms/step - loss: 0.1150 - dice: 0.9319 - jaccard: 0.8928 - val_loss: 0.1343 - val_dice: 0.9263 - val_jaccard: 0.8855\n",
            "Epoch 4: Current Learning Rate: 0.001\n",
            "146/146 [====================] - 94s 641ms/step - loss: 0.1151 - dice: 0.9327 - jaccard: 0.8936 - val_loss: 0.1511 - val_dice: 0.9088 - val_jaccard: 0.8651\n",
            "Epoch 5: Current Learning Rate: 0.001\n",
            "Training fold 4/5\n",
            "Epoch 1: Начальный Learning Rate: 0.001\n",
            "146/146 [====================] - 104s 709ms/step - loss: 0.1232 - dice: 0.9279 - jaccard: 0.8879 - val_loss: 0.1120 - val_dice: 0.9333 - val_jaccard: 0.8951\n",
            "Epoch 1: Current Learning Rate: 0.001\n",
            "146/146 [====================] - 106s 723ms/step - loss: 0.1232 - dice: 0.9301 - jaccard: 0.8902 - val_loss: 0.1145 - val_dice: 0.9335 - val_jaccard: 0.8949\n",
            "Epoch 2: Current Learning Rate: 0.001\n",
            "146/146 [====================] - 103s 708ms/step - loss: 0.1169 - dice: 0.9349 - jaccard: 0.8955 - val_loss: 0.1154 - val_dice: 0.9356 - val_jaccard: 0.8960\n",
            "Epoch 3: Current Learning Rate: 0.001\n",
            "146/146 [====================] - 106s 726ms/step - loss: 0.1125 - dice: 0.9345 - jaccard: 0.8964 - val_loss: 0.1119 - val_dice: 0.9348 - val_jaccard: 0.8961\n",
            "Epoch 4: Current Learning Rate: 0.001\n",
            "146/146 [====================] - 106s 726ms/step - loss: 0.1376 - dice: 0.9196 - jaccard: 0.8784 - val_loss: 0.1568 - val_dice: 0.9047 - val_jaccard: 0.8609\n",
            "Epoch 5: Current Learning Rate: 0.001\n",
            "Training fold 5/5\n",
            "Epoch 1: Начальный Learning Rate: 0.001\n",
            "146/146 [====================] - 114s 781ms/step - loss: 0.1080 - dice: 0.9386 - jaccard: 0.9006 - val_loss: 0.0925 - val_dice: 0.9461 - val_jaccard: 0.9131\n",
            "Epoch 1: Current Learning Rate: 0.001\n",
            "146/146 [====================] - 116s 797ms/step - loss: 0.0977 - dice: 0.9432 - jaccard: 0.9074 - val_loss: 0.0903 - val_dice: 0.9453 - val_jaccard: 0.9116\n",
            "Epoch 2: Current Learning Rate: 0.001\n",
            "146/146 [====================] - 115s 785ms/step - loss: 0.0904 - dice: 0.9477 - jaccard: 0.9137 - val_loss: 0.0957 - val_dice: 0.9466 - val_jaccard: 0.9117\n",
            "Epoch 3: Current Learning Rate: 0.001\n",
            "146/146 [====================] - 117s 802ms/step - loss: 0.0863 - dice: 0.9510 - jaccard: 0.9180 - val_loss: 0.0925 - val_dice: 0.9473 - val_jaccard: 0.9129\n",
            "Epoch 4: Current Learning Rate: 0.001\n",
            "146/146 [====================] - 117s 799ms/step - loss: 0.1008 - dice: 0.9391 - jaccard: 0.9036 - val_loss: 0.1042 - val_dice: 0.9346 - val_jaccard: 0.8995\n",
            "Epoch 5: Current Learning Rate: 0.001\n"
          ]
        }
      ],
      "source": [
        "\"По 5 эпох на фолд\"\n",
        "NUM_EPOCHS = 5\n",
        "NUM_SPLITS = 5  # Количество фолдов\n",
        "full_dataset = InfectionData(combined_df, transform=Transform)  # Создание полного датасета\n",
        "\n",
        "results = train_model_with_cv(model, full_dataset, BCE_Dice, optimizer, scheduler, NUM_EPOCHS, NUM_SPLITS, EARLY_STOP_PATIENCE, 'EfficientNet-b4', 'UnetPlusPlus')\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "b308e403-8d1b-4ae0-90e7-490d80bc4e21",
      "metadata": {
        "id": "b308e403-8d1b-4ae0-90e7-490d80bc4e21"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "id": "3fcb1781-bfef-4fe2-ab71-f333417e7ee0",
      "metadata": {
        "id": "3fcb1781-bfef-4fe2-ab71-f333417e7ee0"
      },
      "source": [
        "### Evaluate"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "b6c43010-9d20-4b20-9ec9-027017cac350",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2024-05-21T09:05:03.319951Z",
          "iopub.status.busy": "2024-05-21T09:05:03.318914Z",
          "iopub.status.idle": "2024-05-21T09:05:03.337357Z",
          "shell.execute_reply": "2024-05-21T09:05:03.336509Z",
          "shell.execute_reply.started": "2024-05-21T09:05:03.319909Z"
        },
        "tags": [],
        "id": "b6c43010-9d20-4b20-9ec9-027017cac350"
      },
      "outputs": [],
      "source": [
        "from torch.utils.data import DataLoader\n",
        "import tqdm  # Импортируем tqdm для отображения progress bar\n",
        "test_loader = DataLoader(test_dataset, batch_size=1, shuffle=False, num_workers=0)\n",
        "def evaluate_model(model, test_loader, criterion):\n",
        "    model.eval()\n",
        "    test_loss = 0.0\n",
        "    dice_scores = []\n",
        "    jaccard_scores = []\n",
        "\n",
        "    with torch.no_grad():\n",
        "        # Оборачиваем итератор DataLoader в tqdm для отображения прогресса\n",
        "        progress_bar = tqdm.tqdm(test_loader, total=len(test_loader), desc=\"Evaluating\", leave=True)\n",
        "        for images, masks in progress_bar:\n",
        "            images, masks = images.to(DEVICE), masks.to(DEVICE)\n",
        "            masks = masks.float()  # Убедимся, что маски имеют тип float\n",
        "\n",
        "            outputs = model(images)\n",
        "            loss = criterion(outputs, masks)\n",
        "            test_loss += loss.item()\n",
        "\n",
        "            # Рассчитаем метрики для каждого изображения в батче\n",
        "            dice = dice_coef(masks, outputs)\n",
        "            jaccard = iou_coef(masks, outputs)\n",
        "            dice_scores.append(dice.item())\n",
        "            jaccard_scores.append(jaccard.item())\n",
        "\n",
        "            # Обновляем progress bar с текущими значениями потерь\n",
        "            progress_bar.set_postfix(loss=loss.item(), dice=dice.item(), jaccard=jaccard.item())\n",
        "\n",
        "    avg_test_loss = test_loss / len(test_loader)\n",
        "    avg_dice = sum(dice_scores) / len(dice_scores)\n",
        "    avg_jaccard = sum(jaccard_scores) / len(jaccard_scores)\n",
        "\n",
        "    print(f\"Test Loss: {avg_test_loss:.4f}, Average Dice: {avg_dice:.4f}, Average Jaccard: {avg_jaccard:.4f}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "3eea2991-d29a-4fde-b638-d3b9b5cfcad6",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2024-05-21T09:05:05.447987Z",
          "iopub.status.busy": "2024-05-21T09:05:05.446668Z",
          "iopub.status.idle": "2024-05-21T09:08:57.476476Z",
          "shell.execute_reply": "2024-05-21T09:08:57.475674Z",
          "shell.execute_reply.started": "2024-05-21T09:05:05.447944Z"
        },
        "tags": [],
        "id": "3eea2991-d29a-4fde-b638-d3b9b5cfcad6",
        "outputId": "08998ae3-6e70-4641-e2a3-84404aeb3e15"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Evaluating: 100%|██████████| 1166/1166 [03:51<00:00,  5.03it/s, dice=1, jaccard=1, loss=0]               "
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Test Loss: 0.0616, Average Dice: 0.9517, Average Jaccard: 0.9185\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        }
      ],
      "source": [
        "# Загрузка модели и вызов функции оценки\n",
        "model.load_state_dict(torch.load('best_model.pth', map_location=DEVICE))\n",
        "evaluate_model(model, test_loader, criterion)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "d17ad708-4d9d-47c7-97f1-c09615b95d20",
      "metadata": {
        "id": "d17ad708-4d9d-47c7-97f1-c09615b95d20"
      },
      "outputs": [],
      "source": [
        "plot_train, plot_val, plot_train_dice,plot_train_jaccard, plot_val_dice,plot_val_jaccard = trained_model[1],trained_model[2],trained_model[3],trained_model[4],trained_model[5],trained_model[6]"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "2c8f7b6e-b31c-467f-b674-6077b53670b9",
      "metadata": {
        "id": "2c8f7b6e-b31c-467f-b674-6077b53670b9"
      },
      "source": [
        "### Визуал"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "adf2203a-704a-47be-b0e5-98cb9d6ad3ac",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2024-05-21T09:11:01.239662Z",
          "iopub.status.busy": "2024-05-21T09:11:01.238465Z",
          "iopub.status.idle": "2024-05-21T09:11:04.844777Z",
          "shell.execute_reply": "2024-05-21T09:11:04.843845Z",
          "shell.execute_reply.started": "2024-05-21T09:11:01.239613Z"
        },
        "tags": [],
        "id": "adf2203a-704a-47be-b0e5-98cb9d6ad3ac",
        "outputId": "044b86b2-fb9d-4524-cb02-d2ba5e2715ba"
      },
      "outputs": [
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 1000x2000 with 20 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "def visualize_samples(dataset, model, device, num_samples=3):\n",
        "    model.eval()\n",
        "    figure, ax = plt.subplots(nrows=num_samples, ncols=2, figsize=(10, 2 * num_samples))  # Увеличил ширину для лучшего отображения\n",
        "\n",
        "    for i in range(num_samples):\n",
        "        # Случайно выбираем семпл из данных\n",
        "        sample_id = np.random.randint(0, len(dataset))\n",
        "        images, true_masks = dataset[sample_id]\n",
        "        images = images.unsqueeze(0).to(device)  # Добавляем размерность батча и перемещаем на устройство\n",
        "        true_masks = true_masks.unsqueeze(0).to(device)\n",
        "\n",
        "        with torch.no_grad():\n",
        "            pred_masks = model(images)\n",
        "            pred_masks = torch.sigmoid(pred_masks)  # Применяем сигмоиду для получения вероятностей\n",
        "            pred_masks = (pred_masks > 0.5).float()  # Бинаризуем предсказание\n",
        "\n",
        "        # Считаем метрики\n",
        "        dice = dice_coef(true_masks, pred_masks).item()\n",
        "        jaccard = iou_coef(true_masks, pred_masks).item()\n",
        "\n",
        "        images = images.cpu().squeeze().numpy()\n",
        "        true_masks = true_masks.cpu().squeeze().numpy()\n",
        "        pred_masks = pred_masks.cpu().squeeze().numpy()\n",
        "\n",
        "        # Визуализация истинного изображения с маской\n",
        "        ax[i, 0].imshow(images, cmap='gray')\n",
        "        ax[i, 0].imshow(true_masks, alpha=0.5, cmap='Reds')  # Настоящая маска в красном\n",
        "        ax[i, 0].set_title('Real Mask')\n",
        "        ax[i, 0].axis('off')\n",
        "\n",
        "        # Визуализация изображения с предсказанной маской\n",
        "        ax[i, 1].imshow(images, cmap='gray')\n",
        "        ax[i, 1].imshow(pred_masks, alpha=0.5, cmap='Blues')  # Предсказанная маска в синем\n",
        "        ax[i, 1].set_title(f'Predicted Mask\\nDice: {dice:.4f}, Jaccard: {jaccard:.4f}')\n",
        "        ax[i, 1].axis('off')\n",
        "\n",
        "    plt.tight_layout()\n",
        "    plt.show()\n",
        "\n",
        "# Пример вызова функции\n",
        "visualize_samples(test_dataset, model, DEVICE, num_samples=10)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "eaf7e5eb-417b-4432-a623-971e06ddd892",
      "metadata": {
        "id": "eaf7e5eb-417b-4432-a623-971e06ddd892"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "DataSphere Kernel",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.10.12"
    },
    "notebookId": "b2a0bc15-8841-4d49-8f5f-9a167167dcac",
    "notebookPath": "modeltraining_dedicated_ru.ipynb",
    "colab": {
      "provenance": []
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}