LLDProgressionClassification.ipynb

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  "cells": [
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      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/goodaycoder/LLDprogression/blob/master/LLDProgressionClassification.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install unzip\n",
        "!pip install torchio\n",
        "!pip install einops\n",
        "!pip install monai==0.8.1\n",
        "!pip install kaggle"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xuYRJMsWifxQ",
        "outputId": "0e44f060-2a6e-4ef4-fc7d-88ffeb143a56"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting unzip\n",
            "  Downloading unzip-1.0.0.tar.gz (704 bytes)\n",
            "Building wheels for collected packages: unzip\n",
            "  Building wheel for unzip (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for unzip: filename=unzip-1.0.0-py3-none-any.whl size=1322 sha256=334ce4fc2327b03693fe88750760cb9f4404ccad5dd304860f01f43681d6c344\n",
            "  Stored in directory: /root/.cache/pip/wheels/90/bd/0f/a2797a7e90de1cdd91548fba3b16a7c2560e14a268bb137101\n",
            "Successfully built unzip\n",
            "Installing collected packages: unzip\n",
            "Successfully installed unzip-1.0.0\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting torchio\n",
            "  Downloading torchio-0.18.86-py2.py3-none-any.whl (172 kB)\n",
            "\u001b[K     |████████████████████████████████| 172 kB 11.7 MB/s \n",
            "\u001b[?25hRequirement already satisfied: numpy>=1.15 in /usr/local/lib/python3.8/dist-packages (from torchio) (1.21.6)\n",
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            "Requirement already satisfied: scipy in /usr/local/lib/python3.8/dist-packages (from torchio) (1.7.3)\n",
            "Requirement already satisfied: torch>=1.1 in /usr/local/lib/python3.8/dist-packages (from torchio) (1.12.1+cu113)\n",
            "Collecting SimpleITK!=2.0.*,!=2.1.1.1\n",
            "  Downloading SimpleITK-2.2.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (52.8 MB)\n",
            "\u001b[K     |████████████████████████████████| 52.8 MB 281 kB/s \n",
            "\u001b[?25hCollecting Deprecated\n",
            "  Downloading Deprecated-1.2.13-py2.py3-none-any.whl (9.6 kB)\n",
            "Requirement already satisfied: humanize in /usr/local/lib/python3.8/dist-packages (from torchio) (0.5.1)\n",
            "Requirement already satisfied: typer[all] in /usr/local/lib/python3.8/dist-packages (from torchio) (0.7.0)\n",
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            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.8/dist-packages (from torch>=1.1->torchio) (4.1.1)\n",
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            "Requirement already satisfied: click<9.0.0,>=7.1.1 in /usr/local/lib/python3.8/dist-packages (from typer[all]->torchio) (7.1.2)\n",
            "Collecting colorama<0.5.0,>=0.4.3\n",
            "  Downloading colorama-0.4.6-py2.py3-none-any.whl (25 kB)\n",
            "Collecting shellingham<2.0.0,>=1.3.0\n",
            "  Downloading shellingham-1.5.0-py2.py3-none-any.whl (9.3 kB)\n",
            "Collecting rich<13.0.0,>=10.11.0\n",
            "  Downloading rich-12.6.0-py3-none-any.whl (237 kB)\n",
            "\u001b[K     |████████████████████████████████| 237 kB 60.6 MB/s \n",
            "\u001b[?25hCollecting commonmark<0.10.0,>=0.9.0\n",
            "  Downloading commonmark-0.9.1-py2.py3-none-any.whl (51 kB)\n",
            "\u001b[K     |████████████████████████████████| 51 kB 5.5 MB/s \n",
            "\u001b[?25hRequirement already satisfied: pygments<3.0.0,>=2.6.0 in /usr/local/lib/python3.8/dist-packages (from rich<13.0.0,>=10.11.0->typer[all]->torchio) (2.6.1)\n",
            "Installing collected packages: commonmark, shellingham, rich, colorama, SimpleITK, Deprecated, torchio\n",
            "Successfully installed Deprecated-1.2.13 SimpleITK-2.2.0 colorama-0.4.6 commonmark-0.9.1 rich-12.6.0 shellingham-1.5.0 torchio-0.18.86\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting einops\n",
            "  Downloading einops-0.6.0-py3-none-any.whl (41 kB)\n",
            "\u001b[K     |████████████████████████████████| 41 kB 566 kB/s \n",
            "\u001b[?25hInstalling collected packages: einops\n",
            "Successfully installed einops-0.6.0\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting monai\n",
            "  Downloading monai-1.0.1-202210241233-py3-none-any.whl (1.1 MB)\n",
            "\u001b[K     |████████████████████████████████| 1.1 MB 28.8 MB/s \n",
            "\u001b[?25hRequirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.8/dist-packages (from monai) (1.21.6)\n",
            "Requirement already satisfied: torch>=1.7 in /usr/local/lib/python3.8/dist-packages (from monai) (1.12.1+cu113)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.8/dist-packages (from torch>=1.7->monai) (4.1.1)\n",
            "Installing collected packages: monai\n",
            "Successfully installed monai-1.0.1\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: kaggle in /usr/local/lib/python3.8/dist-packages (1.5.12)\n",
            "Requirement already satisfied: urllib3 in /usr/local/lib/python3.8/dist-packages (from kaggle) (1.24.3)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.8/dist-packages (from kaggle) (4.64.1)\n",
            "Requirement already satisfied: python-slugify in /usr/local/lib/python3.8/dist-packages (from kaggle) (7.0.0)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.8/dist-packages (from kaggle) (2.23.0)\n",
            "Requirement already satisfied: certifi in /usr/local/lib/python3.8/dist-packages (from kaggle) (2022.9.24)\n",
            "Requirement already satisfied: six>=1.10 in /usr/local/lib/python3.8/dist-packages (from kaggle) (1.15.0)\n",
            "Requirement already satisfied: python-dateutil in /usr/local/lib/python3.8/dist-packages (from kaggle) (2.8.2)\n",
            "Requirement already satisfied: text-unidecode>=1.3 in /usr/local/lib/python3.8/dist-packages (from python-slugify->kaggle) (1.3)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.8/dist-packages (from requests->kaggle) (3.0.4)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.8/dist-packages (from requests->kaggle) (2.10)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "0AC8qXCoXnAh"
      },
      "outputs": [],
      "source": [
        "import os, sys, argparse, json\n",
        "import enum\n",
        "import time\n",
        "import datetime\n",
        "import random\n",
        "import multiprocessing\n",
        "import os.path as osp\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "from pathlib import Path\n",
        "from scipy import stats\n",
        "from IPython import display\n",
        "from tqdm.notebook import tqdm\n",
        "\n",
        "import torch\n",
        "import torchvision\n",
        "import torch.nn.functional as F\n",
        "import torch.nn as nn\n",
        "from torch.utils.tensorboard import SummaryWriter\n",
        "from torch.utils.data import DataLoader\n",
        "from sklearn.metrics import confusion_matrix, multilabel_confusion_matrix, roc_auc_score, matthews_corrcoef\n",
        "from sklearn.metrics import classification_report\n",
        "from sklearn.model_selection import StratifiedKFold, train_test_split, KFold\n",
        "from sklearn.preprocessing import normalize\n",
        "\n",
        "from torchvision.models.feature_extraction import get_graph_node_names\n",
        "from torchvision.models.feature_extraction import create_feature_extractor\n",
        "\n",
        "import torchio as tio\n",
        "import monai\n",
        "from google.colab import drive\n",
        "#drive.mount('/content/drive')\n",
        "\n",
        "from typing import Sequence, Union\n",
        "\n",
        "import re\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "from torchsummary import summary\n",
        "\n",
        "from monai.networks.blocks.patchembedding import PatchEmbeddingBlock\n",
        "from monai.networks.blocks.transformerblock import TransformerBlock\n",
        "from monai.networks.nets import resnet18, resnet34, resnet50, ViT\n",
        "from einops.layers.torch import Rearrange"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "token = {\"username\":\"lintaozhang\",\"key\":\"f9a90779bb577e5ffc820484fa0b94ce\"}\n",
        "with open('/content/kaggle.json', 'w') as file:\n",
        "  json.dump(token, file)\n",
        "!mkdir -p ~/.kaggle\n",
        "!cp /content/kaggle.json ~/.kaggle/\n",
        "!chmod 600 ~/.kaggle/kaggle.json\n",
        "!kaggle config set -n path -v /content\n",
        "!kaggle datasets download -d lintaozhang/llddemo"
      ],
      "metadata": {
        "id": "Gp373RtvA1Pm",
        "outputId": "449fb8b5-d429-40d5-9027-b962dcf8fa73",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "- path is now set to: /content\n",
            "Downloading llddemo.zip to /content/datasets/lintaozhang/llddemo\n",
            "100% 1.64G/1.64G [01:26<00:00, 24.4MB/s]\n",
            "100% 1.64G/1.64G [01:26<00:00, 20.4MB/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "os.chdir('/content/datasets/lintaozhang/llddemo/')\n",
        "!unzip llddemo.zip -d /content/datasets/"
      ],
      "metadata": {
        "id": "XoN63L7sEUpd",
        "outputId": "1a65df50-cd32-4cb1-e5cd-30312387cc9b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Archive:  llddemo.zip\n",
            "  inflating: /content/datasets/files/2category_Fold1_test_list.txt  \n",
            "  inflating: /content/datasets/files/2category_Fold1_train_list.txt  \n",
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            "  inflating: /content/datasets/images/Demo_LLD2category_features_dict.npy  \n",
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            "  inflating: /content/datasets/pretrained/resnet_18_23dataset.pth  \n",
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            "  inflating: /content/datasets/pretrained/resnet_50_23dataset.pth  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#cur_dir = os.getcwd()\n",
        "#pkg_rootdir = os.path.dirname(os.path.dirname(cur_dir))\n",
        "#if pkg_rootdir not in sys.path:\n",
        "#    sys.path.append(pkg_rootdir)\n",
        "LLDdir = '/content/datasets'\n",
        "sys.path.append(LLDdir)\n",
        "os.chdir(LLDdir)\n",
        "#! wget \n",
        "#! gdown\n",
        "#from Model_HRL import HRL34_v1, HRL34_v2\n",
        "#!mv Images/ images/"
      ],
      "metadata": {
        "id": "B-BdzShiBbql"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Model Define"
      ],
      "metadata": {
        "id": "v2Oa_mHHp8D3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from typing import Sequence, Type, Union\n",
        "from functools import partial\n",
        "from typing import Any, Callable, List, Optional, Tuple, Type, Union\n",
        "\n",
        "import numpy as np\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "from torch.nn import LayerNorm\n",
        "\n",
        "#from monai.networks.layers import trunc_normal_\n",
        "from monai.networks.layers.factories import Conv, Norm, Pool\n",
        "from monai.networks.layers.utils import get_pool_layer\n",
        "from monai.utils import ensure_tuple_rep, optional_import\n",
        "from monai.utils.module import look_up_option\n",
        "\n",
        "\n",
        "from monai.networks.blocks.patchembedding import PatchEmbeddingBlock\n",
        "from monai.networks.blocks.transformerblock import TransformerBlock\n",
        "from monai.networks.nets.resnet import ResNetBlock, ResNetBottleneck, ResNet\n",
        "class ResNetBackbone(nn.Module):\n",
        "    def __init__(\n",
        "        self,\n",
        "        block: Union[Type[Union[ResNetBlock, ResNetBottleneck]], str],\n",
        "        layers: List[int],\n",
        "        block_inplanes: List[int],\n",
        "        spatial_dims: int = 3,\n",
        "        n_input_channels: int = 1,\n",
        "        conv1_t_size: Union[Tuple[int], int] = 7,\n",
        "        conv1_t_stride: Union[Tuple[int], int] = 1,\n",
        "        no_max_pool: bool = False,\n",
        "        shortcut_type: str = \"B\",\n",
        "        widen_factor: float = 1.0,\n",
        "        num_classes: int = 3,\n",
        "        feed_forward: bool = True,\n",
        "    ) -> None:\n",
        "\n",
        "        super().__init__()\n",
        "\n",
        "        if isinstance(block, str):\n",
        "            if block == \"basic\":\n",
        "                block = ResNetBlock\n",
        "            elif block == \"bottleneck\":\n",
        "                block = ResNetBottleneck\n",
        "            else:\n",
        "                raise ValueError(\"Unknown block '%s', use basic or bottleneck\" % block)\n",
        "\n",
        "        conv_type: Type[Union[nn.Conv1d, nn.Conv2d, nn.Conv3d]] = Conv[Conv.CONV, spatial_dims]\n",
        "        norm_type: Type[Union[nn.BatchNorm1d, nn.BatchNorm2d, nn.BatchNorm3d]] = Norm[Norm.BATCH, spatial_dims]\n",
        "        pool_type: Type[Union[nn.MaxPool1d, nn.MaxPool2d, nn.MaxPool3d]] = Pool[Pool.MAX, spatial_dims]\n",
        "        avgp_type: Type[Union[nn.AdaptiveAvgPool1d, nn.AdaptiveAvgPool2d, nn.AdaptiveAvgPool3d]] = Pool[\n",
        "            Pool.ADAPTIVEAVG, spatial_dims\n",
        "        ]\n",
        "\n",
        "        #block_avgpool = get_avgpool()\n",
        "        block_inplanes = [int(x * widen_factor) for x in block_inplanes]\n",
        "\n",
        "        self.in_planes = block_inplanes[0]\n",
        "        self.no_max_pool = no_max_pool\n",
        "\n",
        "        conv1_kernel_size = ensure_tuple_rep(conv1_t_size, spatial_dims)\n",
        "        conv1_stride = ensure_tuple_rep(conv1_t_stride, spatial_dims)\n",
        "\n",
        "        self.conv1 = conv_type(\n",
        "            n_input_channels,\n",
        "            self.in_planes,\n",
        "            kernel_size=conv1_kernel_size,  # type: ignore\n",
        "            stride=conv1_stride,  # type: ignore\n",
        "            padding=tuple(k // 2 for k in conv1_kernel_size),  # type: ignore\n",
        "            bias=False,\n",
        "        )\n",
        "        self.bn1 = norm_type(self.in_planes)\n",
        "        self.relu = nn.ReLU(inplace=True)\n",
        "        self.maxpool = pool_type(kernel_size=3, stride=2, padding=1)\n",
        "        self.layer1 = self._make_layer(block, block_inplanes[0], layers[0], spatial_dims, shortcut_type)\n",
        "        self.layer2 = self._make_layer(block, block_inplanes[1], layers[1], spatial_dims, shortcut_type, stride=2)\n",
        "        self.layer3 = self._make_layer(block, block_inplanes[2], layers[2], spatial_dims, shortcut_type, stride=2)\n",
        "        self.layer4 = self._make_layer(block, block_inplanes[3], layers[3], spatial_dims, shortcut_type, stride=2)\n",
        "        #self.avgpool = avgp_type(block_avgpool[spatial_dims])\n",
        "        #self.fc = nn.Linear(block_inplanes[3] * block.expansion, num_classes) if feed_forward else None\n",
        "\n",
        "        for m in self.modules():\n",
        "            if isinstance(m, conv_type):\n",
        "                nn.init.kaiming_normal_(torch.as_tensor(m.weight), mode=\"fan_out\", nonlinearity=\"relu\")\n",
        "            elif isinstance(m, norm_type):\n",
        "                nn.init.constant_(torch.as_tensor(m.weight), 1)\n",
        "                nn.init.constant_(torch.as_tensor(m.bias), 0)\n",
        "            elif isinstance(m, nn.Linear):\n",
        "                nn.init.constant_(torch.as_tensor(m.bias), 0)\n",
        "\n",
        "    def _downsample_basic_block(self, x: torch.Tensor, planes: int, stride: int, spatial_dims: int = 3) -> torch.Tensor:\n",
        "        out: torch.Tensor = get_pool_layer((\"avg\", {\"kernel_size\": 1, \"stride\": stride}), spatial_dims=spatial_dims)(x)\n",
        "        zero_pads = torch.zeros(out.size(0), planes - out.size(1), *out.shape[2:], dtype=out.dtype, device=out.device)\n",
        "        out = torch.cat([out.data, zero_pads], dim=1)\n",
        "        return out\n",
        "\n",
        "    def _make_layer(\n",
        "        self,\n",
        "        block: Type[Union[ResNetBlock, ResNetBottleneck]],\n",
        "        planes: int,\n",
        "        blocks: int,\n",
        "        spatial_dims: int,\n",
        "        shortcut_type: str,\n",
        "        stride: int = 1,\n",
        "    ) -> nn.Sequential:\n",
        "\n",
        "        conv_type: Callable = Conv[Conv.CONV, spatial_dims]\n",
        "        norm_type: Callable = Norm[Norm.BATCH, spatial_dims]\n",
        "\n",
        "        downsample: Union[nn.Module, partial, None] = None\n",
        "        if stride != 1 or self.in_planes != planes * block.expansion:\n",
        "            if look_up_option(shortcut_type, {\"A\", \"B\"}) == \"A\":\n",
        "                downsample = partial(\n",
        "                    self._downsample_basic_block,\n",
        "                    planes=planes * block.expansion,\n",
        "                    stride=stride,\n",
        "                    spatial_dims=spatial_dims,\n",
        "                )\n",
        "            else:\n",
        "                downsample = nn.Sequential(\n",
        "                    conv_type(self.in_planes, planes * block.expansion, kernel_size=1, stride=stride),\n",
        "                    norm_type(planes * block.expansion),\n",
        "                )\n",
        "\n",
        "        layers = [\n",
        "            block(\n",
        "                in_planes=self.in_planes, planes=planes, spatial_dims=spatial_dims, stride=stride, downsample=downsample\n",
        "            )\n",
        "        ]\n",
        "\n",
        "        self.in_planes = planes * block.expansion\n",
        "        for _i in range(1, blocks):\n",
        "            layers.append(block(self.in_planes, planes, spatial_dims=spatial_dims))\n",
        "\n",
        "        return nn.Sequential(*layers)\n",
        "\n",
        "    def forward(self, x: torch.Tensor) -> torch.Tensor:\n",
        "        x = self.conv1(x)\n",
        "        x = self.bn1(x)\n",
        "        x = self.relu(x)\n",
        "        if not self.no_max_pool:\n",
        "            x = self.maxpool(x)\n",
        "\n",
        "        x = self.layer1(x)\n",
        "        x = self.layer2(x)\n",
        "        x = self.layer3(x)\n",
        "        x = self.layer4(x)\n",
        "        return x\n",
        "\n",
        "\n",
        "    \n",
        "\n",
        "class HRL34_v1(nn.Module):\n",
        "    def __init__(\n",
        "        self,\n",
        "        in_channels: int,\n",
        "        img_size: Union[Sequence[int], int],\n",
        "        patch_size: Union[Sequence[int], int],\n",
        "        hidden_size: int = 128,\n",
        "        mlp_dim: int = 512,\n",
        "        num_layers: int = 1,\n",
        "        num_heads: int = 16,\n",
        "        pool = 'mean',\n",
        "        pos_embed: str = \"conv\",\n",
        "        classification: bool = True,\n",
        "        num_classes: int = 2,\n",
        "        dropout_rate: float = 0.0,\n",
        "        spatial_dims: int = 3,\n",
        "        post_activation=\"Tanh\",\n",
        "    ) -> None:\n",
        "        \"\"\"\n",
        "        Args:\n",
        "            in_channels: dimension of input channels.\n",
        "            img_size: dimension of input image.\n",
        "            patch_size: dimension of patch size.\n",
        "            hidden_size: dimension of hidden layer.\n",
        "            mlp_dim: dimension of feedforward layer.\n",
        "            num_layers: number of transformer blocks.\n",
        "            num_heads: number of attention heads.\n",
        "            pos_embed: position embedding layer type.\n",
        "            classification: bool argument to determine if classification is used.\n",
        "            num_classes: number of classes if classification is used.\n",
        "            dropout_rate: faction of the input units to drop.\n",
        "            spatial_dims: number of spatial dimensions.\n",
        "            post_activation: add a final acivation function to the classification head when `classification` is True.\n",
        "                Default to \"Tanh\" for `nn.Tanh()`. Set to other values to remove this function.\n",
        "\n",
        "        \"\"\"\n",
        "\n",
        "        super().__init__()\n",
        "\n",
        "        if not (0 <= dropout_rate <= 1):\n",
        "            raise ValueError(\"dropout_rate should be between 0 and 1.\")\n",
        "\n",
        "        if hidden_size % num_heads != 0:\n",
        "            raise ValueError(\"hidden_size should be divisible by num_heads.\")\n",
        "\n",
        "        self.ResNetBack = ResNetBackbone(ResNetBlock, [3, 4, 6, 3], [64, 128, 256, 512],conv1_t_stride=2)#resnet34\n",
        "        #self.ResNetBack = ResNetBackbone(ResNetBlock, [3, 4, 6, 3], [64, 128, 256, 512])#resnet34\n",
        "\n",
        "        self.pool = pool\n",
        "        self.classification = classification\n",
        "        self.in_channels = in_channels\n",
        "        self.hidden_size = hidden_size\n",
        "        self.patch_embedding1 = PatchEmbeddingBlock(\n",
        "            in_channels=in_channels,\n",
        "            img_size=img_size,\n",
        "            patch_size=patch_size,\n",
        "            hidden_size=hidden_size,\n",
        "            num_heads=num_heads,\n",
        "            pos_embed=pos_embed,\n",
        "            dropout_rate=dropout_rate,\n",
        "            spatial_dims=spatial_dims,\n",
        "        )\n",
        "        self.blocks1 = nn.ModuleList(\n",
        "            [TransformerBlock(hidden_size, mlp_dim, num_heads, dropout_rate) for i in range(num_layers)]\n",
        "        )\n",
        "        self.norm1 = nn.LayerNorm(hidden_size)\n",
        "        if self.classification:\n",
        "            self.cls_token1 = nn.Parameter(torch.zeros(1, 1, hidden_size))\n",
        "            if post_activation == \"Tanh\":\n",
        "                self.classification_head = nn.Sequential(nn.Linear(hidden_size, num_classes), nn.Tanh())\n",
        "            else:\n",
        "                self.classification_head = nn.Linear(hidden_size, num_classes)  # type: ignore\n",
        "\n",
        "\n",
        "    def forward(self, x1):\n",
        "        x1 = self.ResNetBack(x1)\n",
        "        #x1 = x1.reshape(x1.shape[0],x1.shape[1],-1)# batchsize*512*flatten_size\n",
        "        if self.in_channels == 1:\n",
        "            x1 = x1.reshape(x1.shape[0],1,-1,x1.shape[3],x1.shape[4])\n",
        "        x1 = self.patch_embedding1(x1)\n",
        "        x1 = x1.reshape(x1.shape[0],-1,self.hidden_size)\n",
        "        \n",
        "        if hasattr(self, \"cls_token\"):\n",
        "            cls_token1 = self.cls_token1.expand(x1.shape[0], -1, -1)\n",
        "            x1 = torch.cat((cls_token1, x1), dim=1)\n",
        "            #cls_token2 = self.cls_token2.expand(x2.shape[0], -1, -1)\n",
        "            #x2 = torch.cat((cls_token2, x2), dim=1)\n",
        "        hidden_states_out1 = []\n",
        "        for blk in self.blocks1:\n",
        "            x1 = blk(x1)\n",
        "            hidden_states_out1.append(x1)\n",
        "        x1 = self.norm1(x1)\n",
        "\n",
        "        \n",
        "        if hasattr(self, \"classification_head\"):\n",
        "            x1 = x1.mean(dim=1) if self.pool == 'mean' else x1[:, 0]\n",
        "            #x2 = x2.mean(dim=1) if self.pool == 'mean' else x2[:, 0]\n",
        "            #x = self.classification_head(torch.cat((x1, x2), 1))\n",
        "            x1 = self.classification_head(x1)\n",
        "        return x1, hidden_states_out1\n",
        "\n",
        "class HRL34_v2(nn.Module):\n",
        "    def __init__(\n",
        "        self,\n",
        "        in_channels: int,\n",
        "        img_size: Union[Sequence[int], int],\n",
        "        patch_size: Union[Sequence[int], int],\n",
        "        hidden_size1: int = 128,\n",
        "        hidden_size2: int = 128,\n",
        "        mlp_dim: int = 512,\n",
        "        num_layers: int = 1,\n",
        "        num_heads: int = 16,\n",
        "        pool = 'mean',\n",
        "        pos_embed: str = \"conv\",\n",
        "        classification: bool = True,\n",
        "        num_classes: int = 2,\n",
        "        dropout_rate: float = 0.0,\n",
        "        spatial_dims: int = 3,\n",
        "        post_activation=\"Tanh\",\n",
        "    ) -> None:\n",
        "        \"\"\"\n",
        "        Args:\n",
        "            in_channels: dimension of input channels.\n",
        "            img_size: dimension of input image.\n",
        "            patch_size: dimension of patch size.\n",
        "            hidden_size: dimension of hidden layer.\n",
        "            mlp_dim: dimension of feedforward layer.\n",
        "            num_layers: number of transformer blocks.\n",
        "            num_heads: number of attention heads.\n",
        "            pos_embed: position embedding layer type.\n",
        "            classification: bool argument to determine if classification is used.\n",
        "            num_classes: number of classes if classification is used.\n",
        "            dropout_rate: faction of the input units to drop.\n",
        "            spatial_dims: number of spatial dimensions.\n",
        "            post_activation: add a final acivation function to the classification head when `classification` is True.\n",
        "                Default to \"Tanh\" for `nn.Tanh()`. Set to other values to remove this function.\n",
        "\n",
        "        \"\"\"\n",
        "\n",
        "        super().__init__()\n",
        "\n",
        "        if not (0 <= dropout_rate <= 1):\n",
        "            raise ValueError(\"dropout_rate should be between 0 and 1.\")\n",
        "\n",
        "        if hidden_size1 % num_heads != 0:\n",
        "            raise ValueError(\"hidden_size should be divisible by num_heads.\")\n",
        "\n",
        "        self.ResNetBack = ResNetBackbone(ResNetBlock, [3, 4, 6, 3], [64, 128, 256, 512],conv1_t_stride=2)#resnet34\n",
        "        #self.ResNetBack = ResNetBackbone(ResNetBlock, [3, 4, 6, 3], [64, 128, 256, 512])#resnet34\n",
        "\n",
        "        self.pool = pool\n",
        "        self.classification = classification\n",
        "        self.in_channels = in_channels\n",
        "        self.hidden_size1 = hidden_size1\n",
        "        self.hidden_size2 = hidden_size2\n",
        "        self.patch_embedding1 = PatchEmbeddingBlock(\n",
        "            in_channels=in_channels,\n",
        "            img_size=img_size,\n",
        "            patch_size=patch_size,\n",
        "            hidden_size=hidden_size1,\n",
        "            num_heads=num_heads,\n",
        "            pos_embed=pos_embed,\n",
        "            dropout_rate=dropout_rate,\n",
        "            spatial_dims=spatial_dims,\n",
        "        )\n",
        "        self.blocks1 = nn.ModuleList(\n",
        "            [TransformerBlock(hidden_size2, mlp_dim, num_heads, dropout_rate) for i in range(num_layers)]\n",
        "        )\n",
        "        self.norm1 = nn.LayerNorm(hidden_size2)\n",
        "        if self.classification:\n",
        "            self.cls_token1 = nn.Parameter(torch.zeros(1, 1, hidden_size2))\n",
        "            if post_activation == \"Tanh\":\n",
        "                self.classification_head = nn.Sequential(nn.Linear(hidden_size2, num_classes), nn.Tanh())\n",
        "            else:\n",
        "                self.classification_head = nn.Linear(hidden_size2, num_classes)  # type: ignore\n",
        "\n",
        "\n",
        "    def forward(self, x1, x2):\n",
        "        x1 = self.ResNetBack(x1)\n",
        "        #x1 = x1.reshape(x1.shape[0],x1.shape[1],-1)# batchsize*512*flatten_size\n",
        "        if self.in_channels == 1:\n",
        "            x1 = x1.reshape(x1.shape[0],1,-1,x1.shape[3],x1.shape[4])\n",
        "        x1 = self.patch_embedding1(x1)\n",
        "        x1 = x1.reshape(x1.shape[0],-1,self.hidden_size2)\n",
        "        x2 = x2.reshape(x2.shape[0],-1,self.hidden_size2)\n",
        "        #print(x1.shape)\n",
        "        #print(x2.shape)\n",
        "        x1 = torch.cat((x1, x2), dim=1)\n",
        "        \n",
        "        if hasattr(self, \"cls_token\"):\n",
        "            cls_token1 = self.cls_token1.expand(x1.shape[0], -1, -1)\n",
        "            x1 = torch.cat((cls_token1, x1), dim=1)\n",
        "            #cls_token2 = self.cls_token2.expand(x2.shape[0], -1, -1)\n",
        "            #x2 = torch.cat((cls_token2, x2), dim=1)\n",
        "        hidden_states_out1 = []\n",
        "        for blk in self.blocks1:\n",
        "            x1 = blk(x1)\n",
        "            hidden_states_out1.append(x1)\n",
        "        x1 = self.norm1(x1)\n",
        "\n",
        "        \n",
        "        if hasattr(self, \"classification_head\"):\n",
        "            x1 = x1.mean(dim=1) if self.pool == 'mean' else x1[:, 0]\n",
        "            #x2 = x2.mean(dim=1) if self.pool == 'mean' else x2[:, 0]\n",
        "            #x = self.classification_head(torch.cat((x1, x2), 1))\n",
        "            x1 = self.classification_head(x1)\n",
        "        return x1, hidden_states_out1\n"
      ],
      "metadata": {
        "id": "Hn8dPGJCpj1V"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Parameters Setting"
      ],
      "metadata": {
        "id": "PJDlykdGqE4p"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "key = '_HRLdemo'\n",
        "rootpath = LLDdir\n",
        "images_dir = rootpath+'/images'\n",
        "\n",
        "modelname = 'HRL34';#HRL18,HRL34,HRL50\n",
        "max_epochs = 300\n",
        "pretrained = True\n",
        "pretrained_model_path = rootpath+'/pretrained/'\n",
        "trained = False\n",
        "categories = 2\n",
        "imagesize = (176, 208, 176)\n",
        "usefreesurfer = False\n",
        "\n",
        "downsampled = False\n",
        "samplespace = 1\n",
        "if downsampled:\n",
        "    samplespace = 2 #2,4,8\n",
        "\n",
        "best_model_names = []\n",
        "savename='_CELoss'+'_downsample'+str(samplespace)+key\n",
        "if trained:\n",
        "    pretrained = False\n",
        "    savedir = rootpath+'/models/LLD'+str(categories)+'_trained_'\n",
        "elif pretrained:\n",
        "    savedir = rootpath+'/models/LLD'+str(categories)+'_pretrained_'\n",
        "else:\n",
        "    savedir = rootpath+'/models/LLD'+str(categories)+'_scratch_'\n",
        "\n",
        "\n",
        "pin_memory = torch.cuda.is_available()\n",
        "device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')"
      ],
      "metadata": {
        "id": "1vY-hZaOXp_A"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Dataset and Data loader"
      ],
      "metadata": {
        "id": "7zLYG12BJd2c"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class MDD_Dataset(torch.utils.data.Dataset):\n",
        "    def __init__(self, image_paths, labels, augment=False):\n",
        "        subjects = []            \n",
        "        for (image_path,label) in zip(image_paths,labels):\n",
        "            subject = tio.Subject(\n",
        "                mri=tio.ScalarImage(image_path),\n",
        "                labels=int(label),\n",
        "            )\n",
        "            subjects.append(subject)\n",
        "        self.transform()\n",
        "        if augment:\n",
        "            self.dataset = tio.SubjectsDataset(subjects, transform=self.aug_transform)\n",
        "        else:\n",
        "            self.dataset = tio.SubjectsDataset(subjects, transform=self.preproc_transform)\n",
        "            \n",
        "    def transform(self):\n",
        "        get_foreground = tio.ZNormalization.mean\n",
        "        LI_LANDMARKS = \"0 8.06305571158 15.5085721044 18.7007018006 21.5032879029 26.1413278906 29.9862059045 33.8384058795 38.1891334787 40.7217966068 44.0109152758 58.3906435207 100.0\"\n",
        "        LI_LANDMARKS = np.array([float(n) for n in LI_LANDMARKS.split()])\n",
        "        landmarks_dict = {'mri': LI_LANDMARKS}\n",
        "        preprocess = tio.Compose([\n",
        "            tio.ToCanonical(),\n",
        "            tio.Resample((samplespace, samplespace, samplespace)),                  # to MNI space (which is RAS+)\n",
        "            #tio.CropOrPad((181, 220, 181)),                                              # tight crop around brain\n",
        "            tio.HistogramStandardization(landmarks_dict, masking_method=get_foreground),  # standard histogram\n",
        "            tio.ZNormalization(masking_method=get_foreground),                            # zero mean and unit std\n",
        "            tio.RescaleIntensity(percentiles=(0.5,99.5), out_min_max=(0, 1), in_min_max=(-5, 5))\n",
        "        ])\n",
        "        augment = tio.Compose([\n",
        "            tio.RandomAffine(scales=0.1,degrees=10,translation=5,isotropic=True,center='image'),       # random affine\n",
        "            #tio.RandomAffine(scales=0.2,degrees=20,translation=10,isotropic=True,center='image'),       # random affine\n",
        "        ])\n",
        "\n",
        "        self.aug_transform = tio.Compose([preprocess, augment])\n",
        "        self.preproc_transform = preprocess\n",
        "\n",
        "def get_loader(imagepaths,label, batch_size = 1, augment=False):\n",
        "    dataset = MDD_Dataset(image_paths=imagepaths, labels=label, augment=augment)\n",
        "    if augment:\n",
        "        loader = DataLoader(dataset.dataset,batch_size=batch_size,num_workers=batch_size,shuffle=False,pin_memory=pin_memory,drop_last=False)\n",
        "    else:\n",
        "        loader = DataLoader(dataset.dataset,batch_size=batch_size,pin_memory=pin_memory)\n",
        "    return loader\n"
      ],
      "metadata": {
        "id": "XG9vz8wRXqSe"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Pretrained Models"
      ],
      "metadata": {
        "id": "FhnvG-XVI4jc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "Resnet_model_names = []\n",
        "if pretrained:\n",
        "    if modelname == 'HRL18' and len(Resnet_model_names) == 0:\n",
        "        Resnet_model_names.append(rootpath+'/pretrained/resnet_18_23dataset.pth')\n",
        "    if modelname == 'HRL34' and len(Resnet_model_names) == 0:\n",
        "        Resnet_model_names.append(rootpath+'/pretrained/resnet_34_23dataset.pth')\n",
        "    if modelname == 'HRL50' and len(Resnet_model_names) == 0:\n",
        "        Resnet_model_names.append(rootpath+'/pretrained/resnet_50_23dataset.pth')\n"
      ],
      "metadata": {
        "id": "qABwHAKd-esA"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Model train"
      ],
      "metadata": {
        "id": "99zB3135XtNH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "kfold = 5\n",
        "fold = 0\n",
        "batchsize = 2\n",
        "modelname2 = key +'_head16hidden128dorp0.0'\n",
        "features_dict = np.load(rootpath+'/images/Demo_LLD'+str(categories)+'category_features_dict.npy',allow_pickle=True).item()\n",
        "IDs = np.array(features_dict['demo_IDs'])\n",
        "All_labels = np.array(features_dict['demo_labels'])\n",
        "Freesurfer_features = features_dict['demo_features']\n",
        "for idx in range(kfold):\n",
        "    fold = fold+1\n",
        "    filename1=rootpath+'/files/'+str(categories)+'category_Fold'+str(fold)+'_train_list.txt'\n",
        "    labels0  = np.genfromtxt(filename1,dtype=str)\n",
        "    image_paths = labels0[...,0].tolist()\n",
        "\n",
        "    train_image_paths = []\n",
        "    index = []\n",
        "    for imagepath in image_paths:\n",
        "        train_ID = re.findall(r'\\d\\d\\d',imagepath)[0]\n",
        "        idx1 = np.where(str(train_ID) == IDs)[0]\n",
        "        if idx1.size>0:\n",
        "            #imagepath.replace('nii.gz','nii')\n",
        "            train_image_paths.append(rootpath+'/'+imagepath.replace('nii.gz','nii'))\n",
        "            index.append(idx1[0])\n",
        "    index = np.array(index)\n",
        "    train_features = Freesurfer_features[index]\n",
        "    train_features = normalize(train_features, axis=0, norm='max')\n",
        "\n",
        "    train_labels = All_labels[index].tolist()\n",
        "\n",
        "    train_loader = get_loader(train_image_paths,train_labels, batch_size = batchsize, augment=True)\n",
        "\n",
        "    model = HRL34_v2(in_channels=512, img_size=(3,4,3), patch_size=(3,4,3),num_heads=16,hidden_size1=128,hidden_size2=128,dropout_rate= 0.0, num_layers=1, pos_embed='conv', classification=True, num_classes=categories,spatial_dims=3)\n",
        "\n",
        "    if pretrained:\n",
        "        path = Resnet_model_names[0]\n",
        "        save_model = torch.load(path)\n",
        "        save_dict = save_model.items()\n",
        "\n",
        "        model_dict = model.state_dict()\n",
        "        resnet_state_dict = {'ResNetBack.'+k:v for k,v in save_model.items() if 'ResNetBack.'+k in model_dict.keys()}\n",
        "        model.load_state_dict(resnet_state_dict, strict = False)\n",
        "        #for name, value in model_ResViT.named_parameters():\n",
        "            #if name.startswith('ResNetBack'):\n",
        "                #value.requires_grad = False\n",
        "\n",
        "    model.to(device)    \n",
        "    optimizer = torch.optim.Adam(model.parameters(), lr=1e-4)\n",
        "    loss_function = torch.nn.CrossEntropyLoss()\n",
        "\n",
        "    min_v_loss = np.inf\n",
        "    best_metric = -1\n",
        "    best_metric_epoch = -1\n",
        "    epoch_loss_values = []\n",
        "    metric_values = []\n",
        "\n",
        "    threshhold = 0.79 \n",
        "    for epoch in range(max_epochs):\n",
        "        print(\"-\" * 10)\n",
        "        print(f\"Fold{fold} epoch {epoch + 1}/{max_epochs}\")\n",
        "        epoch_loss = 0\n",
        "        step = 0\n",
        "        train_pred  = []\n",
        "        train_prob  = []\n",
        "        train_label = []\n",
        "        model.train()\n",
        "        for batch_data in tqdm(train_loader):\n",
        "            inputs1 = batch_data['mri'][tio.DATA].to(torch.float32).to(device)\n",
        "            labels = batch_data['labels'].to(device)\n",
        "\n",
        "            #inputs2 = train_features[step*batchsize:(step+1)*batchsize,0:1664]\n",
        "            inputs2 = train_features[step*batchsize:(step+1)*batchsize,0:896]\n",
        "            inputs2 = torch.from_numpy(inputs2).to(torch.float32).to(device)\n",
        "            \n",
        "            step += 1\n",
        "\n",
        "            optimizer.zero_grad()\n",
        "            outputs = model(inputs1,inputs2)\n",
        "            loss = loss_function(outputs[0], labels)\n",
        "            loss.backward()\n",
        "            optimizer.step()\n",
        "                \n",
        "            train_pred.append(F.softmax(outputs[0], dim=1).max(dim=1)[1])\n",
        "            train_prob.append(F.softmax(outputs[0], dim=1))\n",
        "            train_label.append(labels)\n",
        "\n",
        "            epoch_loss += loss.item()\n",
        "            epoch_len = len(train_image_paths) // train_loader.batch_size\n",
        "        epoch_loss /= step\n",
        "        epoch_loss_values.append(epoch_loss)\n",
        "        print(f\"epoch {epoch + 1} average loss: {epoch_loss:.4f}\")\n",
        "\n",
        "        y_prob = torch.cat(train_prob, dim=0).cpu().detach().numpy()\n",
        "        y_pred = torch.cat(train_pred, dim=0).cpu().detach().numpy()\n",
        "        y_true = torch.cat(train_label, dim=0).cpu().detach().numpy()\n",
        "\n",
        "        epoch_report = classification_report(y_true, y_pred, output_dict = True,target_names=['CN_N', 'CN_D'],zero_division=0)\n",
        "        metric = epoch_report['accuracy']\n",
        "\n",
        "        if (metric > best_metric) and (metric > threshhold):\n",
        "            threshhold = min(threshhold+0.05,0.95)\n",
        "            best_metric = metric\n",
        "            best_metric_epoch = epoch + 1\n",
        "            best_model_name = savedir+modelname2+savename+'_KFold%02i'% (fold)+'_epoch%02i_acc%.2f.pth' % (best_metric_epoch,best_metric)\n",
        "            torch.save(model.state_dict(), best_model_name)\n",
        "            print(\"saved new best metric model\")\n",
        "        if (metric > 0.95):\n",
        "            break\n",
        "        print(f\"Current epoch: {epoch+1} current accuracy: {metric:.4f} \")\n",
        "        print(f\"Best accuracy: {best_metric:.4f} at epoch {best_metric_epoch}\")\n",
        "\n",
        "    best_model_names.append(best_model_name)\n",
        "    print(f\"Training completed, best_metric: {best_metric:.4f} at epoch: {best_metric_epoch}\")\n",
        "print('Training finish!')\n"
      ],
      "metadata": {
        "id": "aUrsD8JaEUaX",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
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        "outputId": "dae00d2b-dbc8-4e7f-8ef3-63583b88d71e"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "----------\n",
            "Fold1 epoch 1/300\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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          "text": [
            "epoch 1 average loss: 0.7927\n",
            "Current epoch: 1 current accuracy: 0.2500 \n",
            "Best accuracy: -1.0000 at epoch -1\n",
            "----------\n",
            "Fold1 epoch 2/300\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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          "text": [
            "epoch 2 average loss: 0.6038\n",
            "Current epoch: 2 current accuracy: 0.5000 \n",
            "Best accuracy: -1.0000 at epoch -1\n",
            "----------\n",
            "Fold1 epoch 3/300\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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          "text": [
            "epoch 3 average loss: 0.5240\n",
            "Current epoch: 3 current accuracy: 0.7500 \n",
            "Best accuracy: -1.0000 at epoch -1\n",
            "----------\n",
            "Fold1 epoch 4/300\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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            "application/vnd.jupyter.widget-view+json": {
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        {
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          "text": [
            "epoch 4 average loss: 0.3901\n",
            "saved new best metric model\n",
            "Training completed, best_metric: 1.0000 at epoch: 4\n",
            "----------\n",
            "Fold2 epoch 1/300\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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          "name": "stdout",
          "text": [
            "epoch 1 average loss: 0.7690\n",
            "Current epoch: 1 current accuracy: 0.7500 \n",
            "Best accuracy: -1.0000 at epoch -1\n",
            "----------\n",
            "Fold2 epoch 2/300\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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            "application/vnd.jupyter.widget-view+json": {
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          "text": [
            "epoch 2 average loss: 0.6058\n",
            "Current epoch: 2 current accuracy: 0.7500 \n",
            "Best accuracy: -1.0000 at epoch -1\n",
            "----------\n",
            "Fold2 epoch 3/300\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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        {
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          "name": "stdout",
          "text": [
            "epoch 3 average loss: 0.5838\n",
            "Current epoch: 3 current accuracy: 0.7500 \n",
            "Best accuracy: -1.0000 at epoch -1\n",
            "----------\n",
            "Fold2 epoch 4/300\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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            "application/vnd.jupyter.widget-view+json": {
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          "name": "stdout",
          "text": [
            "epoch 4 average loss: 0.5387\n",
            "Current epoch: 4 current accuracy: 0.7500 \n",
            "Best accuracy: -1.0000 at epoch -1\n",
            "----------\n",
            "Fold2 epoch 5/300\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 5 average loss: 0.4609\n",
            "Current epoch: 5 current accuracy: 0.7500 \n",
            "Best accuracy: -1.0000 at epoch -1\n",
            "----------\n",
            "Fold2 epoch 6/300\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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            "application/vnd.jupyter.widget-view+json": {
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        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 6 average loss: 0.3858\n",
            "Current epoch: 6 current accuracy: 0.7500 \n",
            "Best accuracy: -1.0000 at epoch -1\n",
            "----------\n",
            "Fold2 epoch 7/300\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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            "application/vnd.jupyter.widget-view+json": {
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              "model_id": "5d2bbf37ab6b43849b6e01f3544f9757"
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        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 7 average loss: 0.3645\n",
            "Current epoch: 7 current accuracy: 0.7500 \n",
            "Best accuracy: -1.0000 at epoch -1\n",
            "----------\n",
            "Fold2 epoch 8/300\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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            ],
            "application/vnd.jupyter.widget-view+json": {
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              "model_id": "61841773de644625b85dd58178aff8d8"
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          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 8 average loss: 0.3623\n",
            "Current epoch: 8 current accuracy: 0.7500 \n",
            "Best accuracy: -1.0000 at epoch -1\n",
            "----------\n",
            "Fold2 epoch 9/300\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/2 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
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              "model_id": "3b65eb23daeb4cb68489d505d3df8331"
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          "metadata": {}
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        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 9 average loss: 0.3520\n",
            "Current epoch: 9 current accuracy: 0.7500 \n",
            "Best accuracy: -1.0000 at epoch -1\n",
            "----------\n",
            "Fold2 epoch 10/300\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/2 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
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              "model_id": "7d91acb1eb5142a6b41de0779e04f137"
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        {
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          "name": "stdout",
          "text": [
            "epoch 10 average loss: 0.3265\n",
            "Current epoch: 10 current accuracy: 0.7500 \n",
            "Best accuracy: -1.0000 at epoch -1\n",
            "----------\n",
            "Fold2 epoch 11/300\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
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              "model_id": "13eb0c3eef6b40c8be08b9c0999dc8d9"
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        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 11 average loss: 0.3057\n",
            "Current epoch: 11 current accuracy: 0.7500 \n",
            "Best accuracy: -1.0000 at epoch -1\n",
            "----------\n",
            "Fold2 epoch 12/300\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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            ],
            "application/vnd.jupyter.widget-view+json": {
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              "model_id": "2b21d309c90740e8b5fa5009e456f97c"
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        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 12 average loss: 0.2923\n",
            "Current epoch: 12 current accuracy: 0.7500 \n",
            "Best accuracy: -1.0000 at epoch -1\n",
            "----------\n",
            "Fold2 epoch 13/300\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
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              "model_id": "26d06ea4d6e14d079e6beb97bf2adbab"
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        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 13 average loss: 0.2860\n",
            "Current epoch: 13 current accuracy: 0.7500 \n",
            "Best accuracy: -1.0000 at epoch -1\n",
            "----------\n",
            "Fold2 epoch 14/300\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/2 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
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              "model_id": "6d315c3101c9498d89b23281e848e966"
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          "metadata": {}
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        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 14 average loss: 0.2826\n",
            "Current epoch: 14 current accuracy: 0.7500 \n",
            "Best accuracy: -1.0000 at epoch -1\n",
            "----------\n",
            "Fold2 epoch 15/300\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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            ],
            "application/vnd.jupyter.widget-view+json": {
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              "version_minor": 0,
              "model_id": "cecb2d198837407493479c75bdd627e3"
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          "metadata": {}
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        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 15 average loss: 0.2775\n",
            "Current epoch: 15 current accuracy: 0.7500 \n",
            "Best accuracy: -1.0000 at epoch -1\n",
            "----------\n",
            "Fold2 epoch 16/300\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/2 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
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              "model_id": "b32b5f2aaf0d4dc2b8b35409001edf25"
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          "metadata": {}
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        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 16 average loss: 0.2744\n",
            "Current epoch: 16 current accuracy: 0.7500 \n",
            "Best accuracy: -1.0000 at epoch -1\n",
            "----------\n",
            "Fold2 epoch 17/300\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/2 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "1ada247facd34010b0f74fe8103e5e12"
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          "metadata": {}
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        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 17 average loss: 0.2724\n",
            "Current epoch: 17 current accuracy: 0.7500 \n",
            "Best accuracy: -1.0000 at epoch -1\n",
            "----------\n",
            "Fold2 epoch 18/300\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/2 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
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              "model_id": "d407da67273c4ef8abb97294c9ae5fbd"
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          "metadata": {}
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        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 18 average loss: 0.2706\n",
            "saved new best metric model\n",
            "Training completed, best_metric: 1.0000 at epoch: 18\n",
            "----------\n",
            "Fold3 epoch 1/300\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
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              "model_id": "2680c8b89260445faf256a095cbebe06"
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          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 1 average loss: 0.8965\n",
            "Current epoch: 1 current accuracy: 0.5000 \n",
            "Best accuracy: -1.0000 at epoch -1\n",
            "----------\n",
            "Fold3 epoch 2/300\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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            "application/vnd.jupyter.widget-view+json": {
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              "version_minor": 0,
              "model_id": "1d2b711ef9bd4b8987a600fc1d1ec202"
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          "name": "stdout",
          "text": [
            "epoch 2 average loss: 0.8621\n",
            "Current epoch: 2 current accuracy: 0.5000 \n",
            "Best accuracy: -1.0000 at epoch -1\n",
            "----------\n",
            "Fold3 epoch 3/300\n"
          ]
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          "data": {
            "text/plain": [
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a790bf6b8af54c11b59b8eb312c7ea5d"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 3 average loss: 0.6411\n",
            "saved new best metric model\n",
            "Training completed, best_metric: 1.0000 at epoch: 3\n",
            "----------\n",
            "Fold4 epoch 1/300\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/2 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "cdf936f2c2c84b1582f76ef4f40211ed"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 1 average loss: 0.7533\n",
            "Current epoch: 1 current accuracy: 0.7500 \n",
            "Best accuracy: -1.0000 at epoch -1\n",
            "----------\n",
            "Fold4 epoch 2/300\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/2 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ec822b9a1b1a41f7a3a614f9ecf7bafc"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 2 average loss: 0.5928\n",
            "Current epoch: 2 current accuracy: 0.7500 \n",
            "Best accuracy: -1.0000 at epoch -1\n",
            "----------\n",
            "Fold4 epoch 3/300\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/2 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "01a5dbc5ad9040a69d602854478fb85b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 3 average loss: 0.5852\n",
            "Current epoch: 3 current accuracy: 0.7500 \n",
            "Best accuracy: -1.0000 at epoch -1\n",
            "----------\n",
            "Fold4 epoch 4/300\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/2 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "5c0ad03f09aa4e278e080ae304f89210"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 4 average loss: 0.5655\n",
            "Current epoch: 4 current accuracy: 0.7500 \n",
            "Best accuracy: -1.0000 at epoch -1\n",
            "----------\n",
            "Fold4 epoch 5/300\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/2 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a2c5c38628314e7183b50cf98427cda7"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 5 average loss: 0.5292\n",
            "Current epoch: 5 current accuracy: 0.7500 \n",
            "Best accuracy: -1.0000 at epoch -1\n",
            "----------\n",
            "Fold4 epoch 6/300\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/2 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8c13e2e6f5b146c2ad3ea5cdecce0ba1"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 6 average loss: 0.4819\n",
            "Current epoch: 6 current accuracy: 0.7500 \n",
            "Best accuracy: -1.0000 at epoch -1\n",
            "----------\n",
            "Fold4 epoch 7/300\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/2 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6d7beb7956044ebebe2c4e9c30368fb3"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 7 average loss: 0.4501\n",
            "Current epoch: 7 current accuracy: 0.7500 \n",
            "Best accuracy: -1.0000 at epoch -1\n",
            "----------\n",
            "Fold4 epoch 8/300\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/2 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "9dd6cf498b934d87803253a096cf3b96"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 8 average loss: 0.4260\n",
            "Current epoch: 8 current accuracy: 0.7500 \n",
            "Best accuracy: -1.0000 at epoch -1\n",
            "----------\n",
            "Fold4 epoch 9/300\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/2 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d5b004b2dc83419b85c551673c25ee5f"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 9 average loss: 0.4021\n",
            "Current epoch: 9 current accuracy: 0.7500 \n",
            "Best accuracy: -1.0000 at epoch -1\n",
            "----------\n",
            "Fold4 epoch 10/300\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/2 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ee9656dbdc0b46cdbb2ce92acb0124b3"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 10 average loss: 0.3754\n",
            "Current epoch: 10 current accuracy: 0.7500 \n",
            "Best accuracy: -1.0000 at epoch -1\n",
            "----------\n",
            "Fold4 epoch 11/300\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/2 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "5c7758804e5d406187af33c94e31800b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 11 average loss: 0.3513\n",
            "Current epoch: 11 current accuracy: 0.7500 \n",
            "Best accuracy: -1.0000 at epoch -1\n",
            "----------\n",
            "Fold4 epoch 12/300\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/2 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "4188460e5bce4481bb3bca4fe7c01515"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 12 average loss: 0.3345\n",
            "Current epoch: 12 current accuracy: 0.7500 \n",
            "Best accuracy: -1.0000 at epoch -1\n",
            "----------\n",
            "Fold4 epoch 13/300\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/2 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "9772cc593d2545b68c78bcd30abca62c"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 13 average loss: 0.3197\n",
            "Current epoch: 13 current accuracy: 0.7500 \n",
            "Best accuracy: -1.0000 at epoch -1\n",
            "----------\n",
            "Fold4 epoch 14/300\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/2 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "27e3d86cb5774d4b9c7ffe99c7a9239a"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 14 average loss: 0.3146\n",
            "Current epoch: 14 current accuracy: 0.7500 \n",
            "Best accuracy: -1.0000 at epoch -1\n",
            "----------\n",
            "Fold4 epoch 15/300\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/2 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "42715d3a744547f8900df12b855a60c3"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 15 average loss: 0.3025\n",
            "Current epoch: 15 current accuracy: 0.7500 \n",
            "Best accuracy: -1.0000 at epoch -1\n",
            "----------\n",
            "Fold4 epoch 16/300\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/2 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "087934447012432f90e12ac41f0eb28a"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 16 average loss: 0.2959\n",
            "Current epoch: 16 current accuracy: 0.7500 \n",
            "Best accuracy: -1.0000 at epoch -1\n",
            "----------\n",
            "Fold4 epoch 17/300\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/2 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "99bd3ad53b7c4bfda6e15b757a06f210"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 17 average loss: 0.2897\n",
            "Current epoch: 17 current accuracy: 0.7500 \n",
            "Best accuracy: -1.0000 at epoch -1\n",
            "----------\n",
            "Fold4 epoch 18/300\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/2 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "9774dcd56ccb4989aa995bb75633c2d3"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 18 average loss: 0.2847\n",
            "Current epoch: 18 current accuracy: 0.7500 \n",
            "Best accuracy: -1.0000 at epoch -1\n",
            "----------\n",
            "Fold4 epoch 19/300\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/2 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "859b3fff92a2457483f57006c838bf6a"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 19 average loss: 0.2818\n",
            "Current epoch: 19 current accuracy: 0.7500 \n",
            "Best accuracy: -1.0000 at epoch -1\n",
            "----------\n",
            "Fold4 epoch 20/300\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/2 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "672bc284a595483db169c758d04be0ac"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 20 average loss: 0.2791\n",
            "Current epoch: 20 current accuracy: 0.7500 \n",
            "Best accuracy: -1.0000 at epoch -1\n",
            "----------\n",
            "Fold4 epoch 21/300\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/2 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "4cc6134f82d048d09a2f10ca1cd1625b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 21 average loss: 0.2772\n",
            "saved new best metric model\n",
            "Training completed, best_metric: 1.0000 at epoch: 21\n",
            "----------\n",
            "Fold5 epoch 1/300\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/2 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "270520453ceb4e0785642a84f5fb1d62"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 1 average loss: 0.9339\n",
            "Current epoch: 1 current accuracy: 0.5000 \n",
            "Best accuracy: -1.0000 at epoch -1\n",
            "----------\n",
            "Fold5 epoch 2/300\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/2 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "926afd4c8c884fb981a1a10287e57fff"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 2 average loss: 0.5587\n",
            "Current epoch: 2 current accuracy: 0.7500 \n",
            "Best accuracy: -1.0000 at epoch -1\n",
            "----------\n",
            "Fold5 epoch 3/300\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/2 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d220252713ad49128ad943efc6c064a0"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 3 average loss: 0.5193\n",
            "saved new best metric model\n",
            "Training completed, best_metric: 1.0000 at epoch: 3\n",
            "Training finish!\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Model test"
      ],
      "metadata": {
        "id": "TohMsHBDXr5v"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "trained models"
      ],
      "metadata": {
        "id": "1KlM6htCL-gm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "trained_model_names = []\n",
        "if pretrained:\n",
        "    if modelname == 'HRL34' and len(trained_model_names) == 0:\n",
        "        trained_model_names.append(rootpath+'/models/LLD2_scratch_HRLv2_head16hidden128dorp0.0_CELoss_downsample1_KFold01.pth')\n",
        "        trained_model_names.append(rootpath+'/models/LLD2_scratch_HRLv2_head16hidden128dorp0.0_CELoss_downsample1_KFold02.pth')\n",
        "        trained_model_names.append(rootpath+'/models/LLD2_scratch_HRLv2_head16hidden128dorp0.0_CELoss_downsample1_KFold03.pth')\n",
        "        trained_model_names.append(rootpath+'/models/LLD2_scratch_HRLv2_head16hidden128dorp0.0_CELoss_downsample1_KFold04.pth')\n",
        "        trained_model_names.append(rootpath+'/models/LLD2_scratch_HRLv2_head16hidden128dorp0.0_CELoss_downsample1_KFold05.pth')\n"
      ],
      "metadata": {
        "id": "_4y3jqb7MA9C"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print('Testing start!')\n",
        "test_CN_N = []\n",
        "test_CN_D = []\n",
        "test_CI = []\n",
        "\n",
        "test_AUC =[]\n",
        "test_ACC =[]\n",
        "test_PRE =[]\n",
        "test_SEN =[]\n",
        "test_SPE =[]\n",
        "test_F1s =[]\n",
        "test_MCC =[]\n",
        "test_report = []\n",
        "test_confus_mtrx = []\n",
        "\n",
        "features_dict = np.load(rootpath+'/images/Demo_LLD'+str(categories)+'category_features_dict.npy',allow_pickle=True).item()\n",
        "IDs = np.array(features_dict['demo_IDs'])\n",
        "All_labels = np.array(features_dict['demo_labels'])\n",
        "Freesurfer_features = features_dict['demo_features']\n",
        "\n",
        "kfold = 5\n",
        "fold = 0\n",
        "batchsize = 1\n",
        "for idx in range(kfold):\n",
        "    fold+=1\n",
        "    filename1=rootpath+'/files/'+str(categories)+'category_Fold'+str(fold)+'_test_list.txt'\n",
        "    labels0  = np.genfromtxt(filename1,dtype=str)\n",
        "    image_paths = labels0[...,0].tolist()\n",
        "    test_image_paths = []\n",
        "    index = []\n",
        "\n",
        "    for imagepath in image_paths:\n",
        "        test_ID = re.findall(r'\\d\\d\\d',imagepath)[0]\n",
        "        idx1 = np.where(str(test_ID) == IDs)[0]\n",
        "        if idx1.size>0:\n",
        "            test_image_paths.append(rootpath+'/'+imagepath.replace('nii.gz','nii'))\n",
        "            index.append(idx1[0])\n",
        "    test_features = Freesurfer_features[np.array(index)]\n",
        "    test_features = normalize(test_features, axis=0, norm='max')\n",
        "    test_labels   = All_labels[np.array(index)].tolist()\n",
        "\n",
        "    loader = get_loader(test_image_paths,test_labels, batch_size = batchsize)\n",
        "\n",
        "\n",
        "    trained_model = trained_model_names[idx]\n",
        "    print(trained_model)\n",
        "    model.load_state_dict(torch.load(trained_model), strict = False)\n",
        "       \n",
        "    model.to(device)\n",
        "    min_v_loss = np.inf\n",
        "    best_metric = -1\n",
        "    best_metric_epoch = -1\n",
        "    epoch_loss_values = []\n",
        "    metric_values = []\n",
        "\n",
        "    print(\"-\" * 10)\n",
        "    print(f\"Fold{fold}\")\n",
        "    model.eval()\n",
        "    \n",
        "    pred = []\n",
        "    prob = []\n",
        "    label = []\n",
        "    predict= []\n",
        "    step  = 0\n",
        "    num_correct = 0.0\n",
        "    metric_count = 0\n",
        "    for test_data in tqdm(loader):\n",
        "        inputs1 = test_data['mri'][tio.DATA].to(torch.float32).to(device)\n",
        "        test_labels = test_data['labels']\n",
        "        inputs2 = test_features[step*batchsize:(step+1)*batchsize,0:896]\n",
        "        inputs2 = torch.from_numpy(inputs2).to(torch.float32).to(device)\n",
        "        \n",
        "        step += 1\n",
        "\n",
        "        with torch.no_grad():\n",
        "            test_outputs = model(inputs1, inputs2)\n",
        "\n",
        "        outprob = F.softmax(test_outputs[0], dim=1)\n",
        "        prob.append(outprob)\n",
        "        pred.append(outprob.max(dim=1)[1])\n",
        "        predict.append(torch.where(outprob>0.5,torch.ones_like(outprob),torch.zeros_like(outprob)))\n",
        "        label.append(test_labels)\n",
        "\n",
        "    y_prob = torch.cat(prob, dim=0).cpu().detach().numpy()\n",
        "    y_true = torch.cat(label, dim=0).cpu().detach().numpy()\n",
        "    y_pred =  np.array(torch.cat(predict, dim=0).cpu().detach().numpy()[...,1],dtype=int)\n",
        "    \n",
        "    AUC = roc_auc_score(y_true,y_pred) \n",
        "    MCC = matthews_corrcoef(y_true, y_pred)\n",
        "\n",
        "    confus_mtrx = confusion_matrix(y_true, y_pred).ravel()\n",
        "    #SPE  = confus_mtrx[0]/(confus_mtrx[0]+confus_mtrx[2])\n",
        "    epoch_report = classification_report(y_true, y_pred, output_dict = True,target_names=['CN_N', 'CN_D'],zero_division=0)\n",
        "    SPE = epoch_report['CN_N']['recall']\n",
        "    PRE = epoch_report['CN_D']['precision']\n",
        "    SEN = epoch_report['CN_D']['recall']\n",
        "    F1s = epoch_report['CN_D']['f1-score']\n",
        "    \n",
        "    test_AUC.append(AUC)\n",
        "    test_ACC.append(epoch_report['accuracy'])\n",
        "    test_PRE.append(PRE)\n",
        "    test_SEN.append(SEN)\n",
        "    test_SPE.append(SPE)\n",
        "    test_F1s.append(F1s)\n",
        "    test_MCC.append(MCC)\n",
        "    \n",
        "    print('%02i Fold testing finish!'% (fold))\n",
        " \n",
        "mean_AUC =round(np.mean(test_AUC)*100,2)\n",
        "mean_ACC =round(np.mean(test_ACC)*100,2)\n",
        "mean_PRE =round(np.mean(test_PRE)*100,2)\n",
        "mean_SEN =round(np.mean(test_SEN)*100,2)\n",
        "mean_SPE =round(np.mean(test_SPE)*100,2)\n",
        "mean_F1s =round(np.mean(test_F1s)*100,2)\n",
        "mean_MCC =round(np.mean(test_MCC)*100,2)\n",
        "\n",
        "std_AUC =round(np.std(test_AUC,ddof=1)*100,2)\n",
        "std_ACC =round(np.std(test_ACC,ddof=1)*100,2)\n",
        "std_PRE =round(np.std(test_PRE,ddof=1)*100,2)\n",
        "std_SEN =round(np.std(test_SEN,ddof=1)*100,2)\n",
        "std_SPE =round(np.std(test_SPE,ddof=1)*100,2)\n",
        "std_F1s =round(np.std(test_F1s,ddof=1)*100,2)\n",
        "std_MCC =round(np.std(test_MCC,ddof=1)*100,2)\n",
        "\n",
        "print(f'AUC:{mean_AUC}\\xB1{std_AUC}, ACC:{mean_ACC}\\xB1{std_ACC}, PRE:{mean_PRE}\\xB1{std_PRE}, SEN:{mean_SEN}\\xB1{std_SEN}, SPE:{mean_SPE}\\xB1{std_SPE}, F1S:{mean_F1s}\\xB1{std_F1s}, MCC:{mean_MCC}\\xB1{std_MCC}')\n",
        "print('Testing finish!')"
      ],
      "metadata": {
        "id": "tyu_fRrKHIhD",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 588,
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          ]
        },
        "outputId": "b4182c16-aa36-468d-bf71-c0b41a8a1cab"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Testing start!\n",
            "/content/datasets/models/LLD2_scratch_HRLv2_head16hidden128dorp0.0_CELoss_downsample1_KFold01.pth\n",
            "----------\n",
            "Fold1\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/5 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "4524f4bea94448078aa2c4ed62c9c0c1"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "01 Fold testing finish!\n",
            "/content/datasets/models/LLD2_scratch_HRLv2_head16hidden128dorp0.0_CELoss_downsample1_KFold02.pth\n",
            "----------\n",
            "Fold2\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/5 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8636849c9bdd46c19657571ddfd35068"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "02 Fold testing finish!\n",
            "/content/datasets/models/LLD2_scratch_HRLv2_head16hidden128dorp0.0_CELoss_downsample1_KFold03.pth\n",
            "----------\n",
            "Fold3\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/5 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f9df3101d6d34a6ca98492240f089226"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "03 Fold testing finish!\n",
            "/content/datasets/models/LLD2_scratch_HRLv2_head16hidden128dorp0.0_CELoss_downsample1_KFold04.pth\n",
            "----------\n",
            "Fold4\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/5 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e3cd7c45245a498b8b08451f3ef4a856"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "04 Fold testing finish!\n",
            "/content/datasets/models/LLD2_scratch_HRLv2_head16hidden128dorp0.0_CELoss_downsample1_KFold05.pth\n",
            "----------\n",
            "Fold5\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/5 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "01f3f29abd014082a262ada7346b7e8c"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "05 Fold testing finish!\n",
            "AUC:50.0±0.0, ACC:40.0±0.0, PRE:40.0±0.0, SEN:100.0±0.0, SPE:0.0±0.0, F1S:57.14±0.0, MCC:0.0±0.0\n",
            "Testing finish!\n"
          ]
        }
      ]
    }
  ]
}