2D_Code.ipynb

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  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "t6mkS2K2_9Lf"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.image as mpimg\n",
        "import torch\n",
        "import os\n",
        "from tqdm.auto import tqdm\n",
        "from glob import glob\n",
        "import cv2\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import PIL \n",
        "import urllib\n",
        "from torch import nn\n",
        "from torch import optim\n",
        "import torch.nn.functional as F\n",
        "from torchvision import datasets, transforms\n",
        "from random import uniform\n",
        "from imgaug import augmenters as iaa\n",
        "\n",
        "%config InlineBackend.figure_format = 'retina'\n",
        "%matplotlib inline"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GZ8g4tZL7F03"
      },
      "source": [
        "import torch.utils.data as td\n",
        "import torchvision as tv\n",
        "from PIL import Image\n",
        "import matplotlib.pyplot as plt\n",
        "import time"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tALkYOd_7MGH",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "82197efa-8fec-4e62-848d-ed81195a4df3"
      },
      "source": [
        "!pip install pydicom"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting pydicom\n",
            "  Downloading pydicom-2.2.0-py3-none-any.whl (1.9 MB)\n",
            "\u001b[K     |████████████████████████████████| 1.9 MB 8.0 MB/s \n",
            "\u001b[?25hInstalling collected packages: pydicom\n",
            "Successfully installed pydicom-2.2.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uCqEeHYC7ISq"
      },
      "source": [
        "import pydicom as dcm"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "V4c7UOjt7ybJ",
        "outputId": "e1d17141-24f1-4cd2-810b-07f8d15b8d7d"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount._DEBUG = True\n",
        "drive.mount('/content/drive', force_remount=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "unset HISTFILE; export PS1=\"root@6a76c0b05337-f280065fa00c46b5b4089f2c4d655d76: \"\n",
            "bash: cannot set terminal process group (-1): Inappropriate ioctl for device\n",
            "bash: no job control in this shell\n",
            "\u001b[01;34m/content\u001b[00m# root@6a76c0b05337-f280065fa00c46b5b4089f2c4d655d76: umount -f /content/drive || umount /content/drive; pkill -9 -x drive\n",
            "umount: /content/drive: no mount point specified.\n",
            "umount: /content/drive: no mount point specified.\n",
            "root@6a76c0b05337-f280065fa00c46b5b4089f2c4d655d76: pkill -9 -f /opt/google/drive/directoryprefetcher_binary\n",
            "root@6a76c0b05337-f280065fa00c46b5b4089f2c4d655d76: ( while `sleep 0.5`; do if [[ -d \"/content/drive\" && \"$(ls -A /content/drive)\" != \"\" ]]; then echo \"google.colab.drive MOUNTED\"; break; fi; done ) &\n",
            "[1] 158\n",
            "root@6a76c0b05337-f280065fa00c46b5b4089f2c4d655d76: cat /tmp/tmpujk_g1_2/drive.fifo | head -1 | ( /opt/google/drive/drive --features=fuse_max_background:1000,max_read_qps:1000,max_write_qps:1000,max_operation_batch_size:15,max_parallel_push_task_instances:10,opendir_timeout_ms:120000,virtual_folders_omit_spaces:true --inet_family=IPV4_ONLY --preferences=trusted_root_certs_file_path:/opt/google/drive/roots.pem,mount_point_path:/content/drive --console_auth 2>&1 | grep --line-buffered -E \"(Go to this URL in a browser: https://.*)$|Drive File Stream encountered a problem and has stopped|Authorization failed|The domain policy has disabled Drive File Stream\"; echo \"drive EXITED\"; ) &\n",
            "[2] 162\n",
            "root@6a76c0b05337-f280065fa00c46b5b4089f2c4d655d76: Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.activity.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fexperimentsandconfigs%20https%3a%2f%2fwww.googleapis.com%2fauth%2fphotos.native&response_type=code\n",
            "google.colab.drive MOUNTED\n",
            "fuser -kw \"/root/.config/Google/DriveFS/Logs/timeouts.txt\" ; rm -rf \"/root/.config/Google/DriveFS/Logs/timeouts.txt\"\n",
            "Specified filename /root/.config/Google/DriveFS/Logs/timeouts.txt does not exist.\n",
            "root@6a76c0b05337-f280065fa00c46b5b4089f2c4d655d76: nohup bash -c 'tail -n +0 -F \"/root/.config/Google/DriveFS/Logs/drive_fs.txt\" | python3 /opt/google/drive/drive-filter.py > \"/root/.config/Google/DriveFS/Logs/timeouts.txt\" ' < /dev/null > /dev/null 2>&1 &\n",
            "[3] 356\n",
            "root@6a76c0b05337-f280065fa00c46b5b4089f2c4d655d76: disown -a\n",
            "root@6a76c0b05337-f280065fa00c46b5b4089f2c4d655d76: exit\n",
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_0LeWnrf7pxM"
      },
      "source": [
        "os.chdir('./drive/MyDrive/body-morphometry-kidney-and-tumor/data/')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BQG8RrO1RPN7"
      },
      "source": [
        "전처리\n",
        "\n",
        "\n",
        "*   https://github.com/tuvovan/Unet-with-EfficientnetB7-Backbone/blob/master/Body%20Morphometry.ipynb\n",
        "\n",
        "\n",
        "모델\n",
        "\n",
        "\n",
        "*   https://github.com/IanTaehoonYoo/semantic-segmentation-pytorch\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OL4PsCAWIlot"
      },
      "source": [
        "from skimage.transform import resize\n",
        "from skimage.io import imread\n",
        "from tensorflow.keras.preprocessing.image import load_img\n",
        "import numpy as np\n",
        "import pydicom\n",
        "\n",
        "def transform_to_hu(medical_image, image):\n",
        "    hu_image = image * medical_image.RescaleSlope + medical_image.RescaleIntercept\n",
        "    hu_image[hu_image < -1024] = -1024\n",
        "    return hu_image\n",
        "\n",
        "def window_image(image, window_center, window_width):\n",
        "    window_image = image.copy()\n",
        "    image_min = window_center - (window_width / 2)\n",
        "    image_max = window_center + (window_width / 2)\n",
        "    window_image[window_image < image_min] = image_min\n",
        "    window_image[window_image > image_max] = image_max\n",
        "    return window_image\n",
        "\n",
        "def resize_normalize(image):\n",
        "    image = np.array(image, dtype=np.float64)\n",
        "    image -= np.min(image)\n",
        "    image /= np.max(image)\n",
        "    return image\n",
        "\n",
        "def read_dicom(image_medical, window_widht, window_level):\n",
        "    image_data = image_medical.pixel_array\n",
        "\n",
        "    image_hu = transform_to_hu(image_medical, image_data)\n",
        "    image_window = window_image(image_hu.copy(), window_level, window_widht)\n",
        "    image_window_norm = resize_normalize(image_window)\n",
        "#     image_window_norm = image_window\n",
        "\n",
        "    image_window_norm = np.expand_dims(image_window_norm, axis=2)   # (512, 512, 1)\n",
        "    image_ths = np.concatenate([image_window_norm, image_window_norm, image_window_norm], axis=2)   # (512, 512, 3)\n",
        "    return image_ths\n",
        "\n",
        "def to_binary(img, lower, upper):\n",
        "    return (lower <= img) & (img <= upper)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 856
        },
        "id": "oWtB2cz20PuZ",
        "outputId": "3e92b138-337e-4d15-e5c7-c7aa624c302f"
      },
      "source": [
        "pip install albumentations==0.3.1"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting albumentations==0.3.1\n",
            "  Downloading albumentations-0.3.1.tar.gz (78 kB)\n",
            "\u001b[?25l\r\u001b[K     |████▏                           | 10 kB 28.7 MB/s eta 0:00:01\r\u001b[K     |████████▍                       | 20 kB 35.0 MB/s eta 0:00:01\r\u001b[K     |████████████▋                   | 30 kB 21.3 MB/s eta 0:00:01\r\u001b[K     |████████████████▊               | 40 kB 17.3 MB/s eta 0:00:01\r\u001b[K     |█████████████████████           | 51 kB 9.2 MB/s eta 0:00:01\r\u001b[K     |█████████████████████████▏      | 61 kB 9.5 MB/s eta 0:00:01\r\u001b[K     |█████████████████████████████▎  | 71 kB 9.1 MB/s eta 0:00:01\r\u001b[K     |████████████████████████████████| 78 kB 4.9 MB/s \n",
            "\u001b[?25hRequirement already satisfied: numpy>=1.11.1 in /usr/local/lib/python3.7/dist-packages (from albumentations==0.3.1) (1.19.5)\n",
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            "Collecting opencv-python-headless\n",
            "  Downloading opencv_python_headless-4.5.3.56-cp37-cp37m-manylinux2014_x86_64.whl (37.1 MB)\n",
            "\u001b[K     |████████████████████████████████| 37.1 MB 75 kB/s \n",
            "\u001b[?25hCollecting imgaug<0.2.7,>=0.2.5\n",
            "  Downloading imgaug-0.2.6.tar.gz (631 kB)\n",
            "\u001b[K     |████████████████████████████████| 631 kB 70.2 MB/s \n",
            "\u001b[?25hRequirement already satisfied: PyYAML in /usr/local/lib/python3.7/dist-packages (from albumentations==0.3.1) (3.13)\n",
            "Requirement already satisfied: scikit-image>=0.11.0 in /usr/local/lib/python3.7/dist-packages (from imgaug<0.2.7,>=0.2.5->albumentations==0.3.1) (0.16.2)\n",
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            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib!=3.0.0,>=2.0.0->scikit-image>=0.11.0->imgaug<0.2.7,>=0.2.5->albumentations==0.3.1) (0.10.0)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib!=3.0.0,>=2.0.0->scikit-image>=0.11.0->imgaug<0.2.7,>=0.2.5->albumentations==0.3.1) (1.3.1)\n",
            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib!=3.0.0,>=2.0.0->scikit-image>=0.11.0->imgaug<0.2.7,>=0.2.5->albumentations==0.3.1) (2.4.7)\n",
            "Building wheels for collected packages: albumentations, imgaug\n",
            "  Building wheel for albumentations (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for albumentations: filename=albumentations-0.3.1-py3-none-any.whl size=47861 sha256=6c916b29ceb6b33926b318c368dc3af5cbb1a2ee784bbb01f131cb07c121b026\n",
            "  Stored in directory: /root/.cache/pip/wheels/db/51/fd/1d5d1bcf5bcecec185bfe177cf132fc87e29a0a9a8d782e0b7\n",
            "  Building wheel for imgaug (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for imgaug: filename=imgaug-0.2.6-py3-none-any.whl size=654020 sha256=d128a5b799f6200fb26e6ec25182f9af4bc7a6fbe812b7a2c0fa4d2b3544d500\n",
            "  Stored in directory: /root/.cache/pip/wheels/89/72/98/3ebfdba1069a9a8eaaa7ae7265cfd67d63ef0197aaee2e5f9c\n",
            "Successfully built albumentations imgaug\n",
            "Installing collected packages: opencv-python-headless, imgaug, albumentations\n",
            "  Attempting uninstall: imgaug\n",
            "    Found existing installation: imgaug 0.2.9\n",
            "    Uninstalling imgaug-0.2.9:\n",
            "      Successfully uninstalled imgaug-0.2.9\n",
            "  Attempting uninstall: albumentations\n",
            "    Found existing installation: albumentations 0.1.12\n",
            "    Uninstalling albumentations-0.1.12:\n",
            "      Successfully uninstalled albumentations-0.1.12\n",
            "Successfully installed albumentations-0.3.1 imgaug-0.2.6 opencv-python-headless-4.5.3.56\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "cv2",
                  "imgaug"
                ]
              }
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "Hw3FLlvlz-tw",
        "outputId": "d0ea8e3f-2e9f-471d-ad9f-652e61033e8f"
      },
      "source": [
        "albumentations.__version__"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'0.1.12'"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J6OT5MsBZkXL"
      },
      "source": [
        "import albumentations as A\n",
        "import albumentations.pytorch\n",
        "\n",
        "aug = A.Compose([\n",
        "                 A.augmentations.transforms.HorizontalFlip(p=0.5,always_apply=False),\n",
        "                 A.OneOf([\n",
        "                          A.RandomContrast(always_apply=False,p=0.2,limit=[-0.1,0.1]),\n",
        "                          A.RandomGamma(always_apply=False,p=0.2,gamma_limit=[80,120]),\n",
        "                          A.RandomBrightness(always_apply=False,p=0.2,limit=[-0.1,0.1])\n",
        "                          ], p=0.5)\n",
        "                #  A.OneOf([\n",
        "                #           A.augmentations.transforms.ElasticTransform(always_apply=False,p=0.2,alpha=10,sigma=6.0,alpha_affine=1.5999999999999996,interpolation=1,border_mode=4,approximate=False),\n",
        "                #           # albumentations.augmentations.transforms.GridDistortion(always_apply=False,p=0.2,num_steps=5,distort_limit=[-0.1,0.1],interpolation=1,border_mode=4),\n",
        "                #           A.augmentations.transforms.OpticalDistortion(always_apply=False,p=0.4,distort_limit=[-2,2],shift_limit=[-0.5,0.5],interpolation=1,border_mode=4),                 \n",
        "                #           ], p=0.3),\n",
        "                          #  albumentations.augmentations.transforms.Cutout(always_apply=False,p=0.5,num_holes=8,max_h_size=50,max_w_size=50)\n",
        "                          #  albumentations.augmentations.transforms.ShiftScaleRotate(always_apply=False,p=0.5,shift_limit=[-0.0625,0.0625],scale_limit=[-0.09999999999999998,0.12000000000000009],rotate_limit=[-25,25],interpolation=1,border_mode=4,value=null,mask_value=null),\n",
        "                          # albumentations.augmentations.transforms.Resize(always_apply=True,p=1,height=512,width=512,interpolation=1)\n",
        "\n",
        "                    ])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ou_vStef7Kut"
      },
      "source": [
        "class MyDataset(torch.utils.data.Dataset):\n",
        "    def __init__(self, x_dir, y_dir, transform=None):\n",
        "        super().__init__()\n",
        "\n",
        "        self.transforms = transform\n",
        "        self.x_img = x_dir\n",
        "        self.y_img = y_dir   \n",
        "\n",
        "    def __len__(self):\n",
        "        return len(self.x_img)\n",
        "\n",
        "    def __getitem__(self, idx):\n",
        "        x_img = self.x_img[idx]\n",
        "        y_img = self.y_img[idx]\n",
        "        # Read an image with OpenCV\n",
        "        x_img = dcm.read_file(x_img)\n",
        "        y_img =  imread(y_img)\n",
        "\n",
        "        x_img=read_dicom(x_img,500,0)\n",
        "        x_img=np.transpose(x_img,(2,0,1))\n",
        "        x_img=x_img.astype(np.float32)\n",
        "\n",
        "        y_img = resize(y_img, (512, 512))*255\n",
        "\n",
        "        \n",
        "        color_im = np.zeros([512, 512, 2])\n",
        "        for i in range(1,3):\n",
        "            encode_ = to_binary(y_img, i*1.0, i*1.0) * 255\n",
        "            color_im[:, :, i-1] = encode_\n",
        "        color_im = np.transpose(color_im,(2,0,1))\n",
        "\n",
        "        # Data Augmentation\n",
        "        if self.transforms:\n",
        "            augmented = self.transforms(image=x_img,mask=color_im)\n",
        "            img = augmented['image']\n",
        "            mask = augmented['mask']\n",
        "            return img, mask,y_img"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B7JE1IDH8iTl"
      },
      "source": [
        "import glob\n",
        "data_path=sorted(glob.glob( \"./train/DICOM/**/*.dcm\",recursive=True ))\n",
        "label_path=sorted(glob.glob(\"./train/Label/**/*.png\",recursive = True))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qhg9hfPv8qsU"
      },
      "source": [
        "data_path = pd.array(data_path)\n",
        "label_path = pd.array(label_path)\n",
        "\n",
        "train_input_files = data_path[0:].to_numpy()\n",
        "train_label_files = label_path[0:].to_numpy()\n",
        "\n",
        "val_input_files = data_path[:12].to_numpy()\n",
        "val_label_files = label_path[:12].to_numpy()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4isFVrb_8weM"
      },
      "source": [
        "train_dataset = MyDataset(train_input_files,train_label_files,aug)\n",
        "train_loader = torch.utils.data.DataLoader(dataset=train_dataset, batch_size=8,shuffle=True)\n",
        "val_dataset = MyDataset(val_input_files,val_label_files,aug)\n",
        "val_loader = torch.utils.data.DataLoader(dataset=val_dataset, batch_size=8,shuffle=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 241
        },
        "id": "1MJmXHLz88_3",
        "outputId": "9745cc4a-4073-454a-de14-a2efb4cd33a5"
      },
      "source": [
        "##input과 label이 맞나 확인\n",
        "origin_img,origin_mask,images,mask = next(iter(train_loader))\n",
        "print(images.shape)\n",
        "print(mask.shape)\n",
        "plt.figure(figsize=(16,18))\n",
        "plt.subplot(1,5,1)\n",
        "plt.imshow(origin_img[0][0])\n",
        "plt.subplot(1,5,2)\n",
        "plt.imshow(origin_mask[0])\n",
        "plt.subplot(1,5,3)\n",
        "plt.imshow(images[0][0])\n",
        "plt.subplot(1,5,4)\n",
        "plt.imshow(mask[0][0])\n",
        "plt.subplot(1,5,5)\n",
        "plt.imshow(mask[0][1])\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "torch.Size([8, 3, 512, 512])\n",
            "torch.Size([8, 2, 512, 512])\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1152x1296 with 5 Axes>"
            ]
          },
          "metadata": {
            "image/png": {
              "width": 933,
              "height": 188
            },
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-RPu17l8trAm"
      },
      "source": [
        "def compute_per_channel_dice(input, target, epsilon=1e-5, ignore_index=None, weight=None):\n",
        "    # assumes that input is a normalized probability\n",
        "    # input and target shapes must match\n",
        "    assert input.size() == target.size(), \"'input' and 'target' must have the same shape\"\n",
        "\n",
        "    # mask ignore_index if present\n",
        "    if ignore_index is not None:\n",
        "        mask = target.clone().ne_(ignore_index)\n",
        "        mask.requires_grad = False\n",
        "\n",
        "        input = input * mask\n",
        "        target = target * mask\n",
        "\n",
        "    input = flatten(input)\n",
        "    target = flatten(target)\n",
        "\n",
        "    # Compute per channel Dice Coefficient\n",
        "    intersect = (input * target).sum(-1)\n",
        "    if weight is not None:\n",
        "        intersect = weight * intersect\n",
        "\n",
        "    denominator = (input + target).sum(-1)\n",
        "    return 2. * intersect / denominator.clamp(min=epsilon)\n",
        "\n",
        "def flatten(tensor):\n",
        "    \"\"\"Flattens a given tensor such that the channel axis is first.\n",
        "    The shapes are transformed as follows:\n",
        "       (N, C, D, H, W) -> (C, N * D * H * W)\n",
        "    \"\"\"\n",
        "    C = tensor.size(1)\n",
        "    # new axis order\n",
        "    axis_order = (1, 0) + tuple(range(2, tensor.dim()))\n",
        "    # Transpose: (N, C, D, H, W) -> (C, N, D, H, W)\n",
        "    transposed = tensor.permute(axis_order).contiguous()\n",
        "    # Flatten: (C, N, D, H, W) -> (C, N * D * H * W)\n",
        "    return transposed.view(C, -1)\n",
        "\n",
        "class DiceLoss(nn.Module):\n",
        "    \"\"\"Computes Dice Loss, which just 1 - DiceCoefficient described above.\n",
        "    Additionally allows per-class weights to be provided.\n",
        "    \"\"\"\n",
        "\n",
        "    def __init__(self, epsilon=1e-5, weight=None, ignore_index=None, sigmoid_normalization=True,\n",
        "                 skip_last_target=False):\n",
        "        super(DiceLoss, self).__init__()\n",
        "        if isinstance(weight, list):\n",
        "            weight = torch.Tensor(weight)\n",
        "            \n",
        "        self.epsilon = epsilon\n",
        "        self.register_buffer('weight', weight)\n",
        "        self.ignore_index = ignore_index\n",
        "\n",
        "        if sigmoid_normalization:\n",
        "            self.normalization = nn.Sigmoid()\n",
        "        else:\n",
        "            self.normalization = nn.Softmax(dim=1)\n",
        "        # if True skip the last channel in the target\n",
        "        self.skip_last_target = skip_last_target\n",
        "\n",
        "    def forward(self, input, target):\n",
        "        # get probabilities from logits\n",
        "\n",
        "        input = self.normalization(input)\n",
        "        if self.weight is not None:\n",
        "            weight = Variable(self.weight, requires_grad=False).to(input.device)\n",
        "        else:\n",
        "            weight = None\n",
        "\n",
        "        if self.skip_last_target:\n",
        "            target = target[:, :-1, ...]\n",
        "\n",
        "        per_channel_dice = compute_per_channel_dice(input, target, epsilon=self.epsilon, ignore_index=self.ignore_index, weight=weight)\n",
        "        # Average the Dice score across all channels/classes\n",
        "        return torch.mean(1. - per_channel_dice)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KVX9u4ZB9GQI",
        "outputId": "8f768432-8620-47f5-c6c6-19a93606b688"
      },
      "source": [
        "!pip install git+https://github.com/qubvel/segmentation_models.pytorch"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting git+https://github.com/qubvel/segmentation_models.pytorch\n",
            "  Cloning https://github.com/qubvel/segmentation_models.pytorch to /tmp/pip-req-build-xxltftm6\n",
            "  Running command git clone -q https://github.com/qubvel/segmentation_models.pytorch /tmp/pip-req-build-xxltftm6\n",
            "Requirement already satisfied: torchvision>=0.5.0 in /usr/local/lib/python3.7/dist-packages (from segmentation-models-pytorch==0.2.0) (0.10.0+cu102)\n",
            "Collecting pretrainedmodels==0.7.4\n",
            "  Downloading pretrainedmodels-0.7.4.tar.gz (58 kB)\n",
            "\u001b[K     |████████████████████████████████| 58 kB 4.5 MB/s \n",
            "\u001b[?25hCollecting efficientnet-pytorch==0.6.3\n",
            "  Downloading efficientnet_pytorch-0.6.3.tar.gz (16 kB)\n",
            "Collecting timm==0.4.12\n",
            "  Downloading timm-0.4.12-py3-none-any.whl (376 kB)\n",
            "\u001b[K     |████████████████████████████████| 376 kB 15.6 MB/s \n",
            "\u001b[?25hRequirement already satisfied: torch in /usr/local/lib/python3.7/dist-packages (from efficientnet-pytorch==0.6.3->segmentation-models-pytorch==0.2.0) (1.9.0+cu102)\n",
            "Collecting munch\n",
            "  Downloading munch-2.5.0-py2.py3-none-any.whl (10 kB)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from pretrainedmodels==0.7.4->segmentation-models-pytorch==0.2.0) (4.62.0)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from torch->efficientnet-pytorch==0.6.3->segmentation-models-pytorch==0.2.0) (3.7.4.3)\n",
            "Requirement already satisfied: pillow>=5.3.0 in /usr/local/lib/python3.7/dist-packages (from torchvision>=0.5.0->segmentation-models-pytorch==0.2.0) (7.1.2)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from torchvision>=0.5.0->segmentation-models-pytorch==0.2.0) (1.19.5)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from munch->pretrainedmodels==0.7.4->segmentation-models-pytorch==0.2.0) (1.15.0)\n",
            "Building wheels for collected packages: segmentation-models-pytorch, efficientnet-pytorch, pretrainedmodels\n",
            "  Building wheel for segmentation-models-pytorch (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for segmentation-models-pytorch: filename=segmentation_models_pytorch-0.2.0-py3-none-any.whl size=88675 sha256=e685e86b66570111f979367e75d2ba75ae7cec14f4334aae158d21551954cd58\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-hf9wspkh/wheels/fa/c5/a8/1e8af6cb04a0974db8a4a156ebd2fdd1d99ad2558d3fce49d4\n",
            "  Building wheel for efficientnet-pytorch (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for efficientnet-pytorch: filename=efficientnet_pytorch-0.6.3-py3-none-any.whl size=12421 sha256=f5a95ffbb32c495f4b564c626fb64c16b0c9c643327a84873b6356c94c485e78\n",
            "  Stored in directory: /root/.cache/pip/wheels/90/6b/0c/f0ad36d00310e65390b0d4c9218ae6250ac579c92540c9097a\n",
            "  Building wheel for pretrainedmodels (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for pretrainedmodels: filename=pretrainedmodels-0.7.4-py3-none-any.whl size=60965 sha256=b85d56a26ae6662fe56e85f7d1c648c212f873c8aeb300d90e8ea378053723c6\n",
            "  Stored in directory: /root/.cache/pip/wheels/ed/27/e8/9543d42de2740d3544db96aefef63bda3f2c1761b3334f4873\n",
            "Successfully built segmentation-models-pytorch efficientnet-pytorch pretrainedmodels\n",
            "Installing collected packages: munch, timm, pretrainedmodels, efficientnet-pytorch, segmentation-models-pytorch\n",
            "Successfully installed efficientnet-pytorch-0.6.3 munch-2.5.0 pretrainedmodels-0.7.4 segmentation-models-pytorch-0.2.0 timm-0.4.12\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67,
          "referenced_widgets": [
            "4eaaa8a8634e4855af26fcbd2b33fc13",
            "6789286f54574f06bc63ebf382d0849e",
            "d0d0b99d7104438ea6140885de07e980",
            "465a9a4174784212a0cad05011dd0228",
            "b43fe78c07ce435c8eb0d2c21f054e68",
            "7eeb64ecb13c4d2291cfc0e3a6df6160",
            "3d3dc35ab73a4d1f9f1bb7eea0b51151",
            "c7f9730dc7d44bd89ebc48f2ff491fdf",
            "7c7d97f427c946738d2a9b5481cd845a",
            "dbc57f4a57fa4d86a7e17a2165381a51",
            "ee307ee53b994985b872600ba039750f"
          ]
        },
        "id": "18PpAJOD9GkJ",
        "outputId": "9c6f0445-e4b7-4bbc-e266-09d8db2f7f44"
      },
      "source": [
        "import segmentation_models_pytorch as smp\n",
        "model = smp.FPN(\n",
        "    encoder_name=\"resnext101_32x8d\",# choose encoder, e.g. mobilenet_v2 or efficientnet-b7 resnext101_32x8d,timm-res2net101_26w_4s     # use `imagenet` pre-trained weights for encoder initialization \n",
        "    encoder_weights=\"imagenet\",\n",
        "    in_channels=3,\n",
        "    # model input channels (1 for gray-scale images, 3 for RGB, etc.)\n",
        "    classes=2,                      # model output channels (number of classes in your dataset)\n",
        ")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading: \"https://download.pytorch.org/models/resnext101_32x8d-8ba56ff5.pth\" to /root/.cache/torch/hub/checkpoints/resnext101_32x8d-8ba56ff5.pth\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "4eaaa8a8634e4855af26fcbd2b33fc13",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0.00/340M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TiRaw4B39P1e"
      },
      "source": [
        "import torch.optim as optim\n",
        "criterion =  DiceLoss(sigmoid_normalization=True)\n",
        "optimizer = optim.Adam(model.parameters(),lr=0.00001)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UPG8JSqIOaAZ",
        "outputId": "0aa6aec5-670f-47f1-ae5e-4b994642f62a"
      },
      "source": [
        "sum([param.nelement() for param in model.parameters()])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "89350466"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "EmiTRmsl9PzI",
        "outputId": "b6b347f1-a818-4028-9c45-a17ccaa7c204"
      },
      "source": [
        "device = 'cpu'\n",
        "if torch.cuda.is_available():\n",
        "    device = 'cuda'\n",
        "    \n",
        "device"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'cuda'"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BxFM0Rno9xtV"
      },
      "source": [
        "n_epochs = 50\n",
        "cnt =0\n",
        "valid_loss_min = np.inf # track change in validation loss\n",
        "\n",
        "# keep track of training and validation loss\n",
        "train_loss = torch.zeros(n_epochs)\n",
        "valid_loss = torch.zeros(n_epochs)\n",
        "\n",
        "model.to(device)\n",
        "for e in range(0, n_epochs):\n",
        "\n",
        "   \n",
        "    ###################\n",
        "    # train the model #\n",
        "    ###################\n",
        "    model.train()\n",
        "    for data, labels,a in tqdm(train_loader):\n",
        "        # move tensors to GPU if CUDA is available\n",
        "        data, labels = data.to(device), labels.to(device) #cpu에 있는 데이터를 gpu에 보냄\n",
        "        # clear the gradients of all optimized variables\n",
        "        optimizer.zero_grad()\n",
        "        # forward pass: compute predicted outputs by passing inputs to the model\n",
        "        logits = model(data)\n",
        "        # calculate the batch loss\n",
        "        loss = criterion(logits, labels)\n",
        "        # backward pass: compute gradient of the loss with respect to model parameters\n",
        "        loss.backward()\n",
        "        # perform a single optimization step (parameter update)\n",
        "        optimizer.step()\n",
        "        # update training loss\n",
        "        train_loss[e] += loss.item()\n",
        "\n",
        "        logits = logits.sigmoid()\n",
        "        logits = logits.detach().cpu()\n",
        "\n",
        "        cnt = cnt+1\n",
        "        \n",
        "        if cnt %100==0:\n",
        "            \n",
        "            y=logits[0].detach().cpu().numpy()\n",
        "            x=labels[0].detach().cpu().numpy()\n",
        "            plt.figure(figsize=(16,18))\n",
        "            plt.subplot(1,5,1)\n",
        "            plt.imshow(x[0])\n",
        "            plt.subplot(1,5,2)\n",
        "            plt.imshow(x[1])\n",
        "            plt.subplot(1,5,3)\n",
        "            plt.imshow(y[0])\n",
        "            plt.subplot(1,5,4)\n",
        "            plt.imshow(y[1])\n",
        "            plt.subplot(1,5,5)\n",
        "            plt.imshow(a[0])\n",
        "            plt.show()\n",
        "        \n",
        "        \n",
        "        \n",
        "    \n",
        "    train_loss[e] /= len(train_loader)\n",
        "        \n",
        "        \n",
        "    ######################    \n",
        "    # validate the model #\n",
        "    ######################\n",
        "    with torch.no_grad(): \n",
        "        model.eval()\n",
        "        for data, labels,a in tqdm(val_loader):\n",
        "            # move tensors to GPU if CUDA is available\n",
        "            data, labels = data.to(device), labels.to(device)\n",
        "            # forward pass: compute predicted outputs by passing inputs to the model\n",
        "            logits = model(data)\n",
        "            # calculate the batch loss\n",
        "            loss = criterion(logits, labels)\n",
        "            # update average validation loss \n",
        "            valid_loss[e] += loss.item()\n",
        "\n",
        "    \n",
        "    # calculate average losses\n",
        "    valid_loss[e] /= len(val_loader)\n",
        "    \n",
        "    # print training/validation statistics \n",
        "    print('Epoch: {} \\tTraining Loss: {:.6f} \\tValidation Loss: {:.6f}'.format(\n",
        "        e, train_loss[e], valid_loss[e]))\n",
        "    \n",
        "    # save model if validation loss has decreased\n",
        "    if valid_loss[e] <= valid_loss_min:\n",
        "        print('Validation loss decreased ({:.6f} --> {:.6f}).  Saving model ...'.format(\n",
        "        valid_loss_min,\n",
        "        valid_loss[e]))\n",
        "        torch.save(model.state_dict(), 'model_simpleAlbu.pt')\n",
        "        valid_loss_min = valid_loss[e]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-kyvSyrk_gP7"
      },
      "source": [
        "model.load_state_dict(torch.load('model_simpleAlbu.pt'))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K-aiH1HO_3fI"
      },
      "source": [
        "class TestMyDataset(torch.utils.data.Dataset):\n",
        "    def __init__(self, x_dir,transform=None):\n",
        "        super().__init__()\n",
        "        self.transform = transform\n",
        "        self.x_img = x_dir\n",
        "     \n",
        "\n",
        "    def __len__(self):\n",
        "        return len(self.x_img)\n",
        "\n",
        "    def __getitem__(self, idx):\n",
        "        x_img = self.x_img[idx]\n",
        "        # Read an image with OpenCV\n",
        "        x_img = dcm.dcmread(x_img)\n",
        "        x_img=read_dicom(x_img,400,50)\n",
        "        x_img=np.transpose(x_img,(2,0,1))\n",
        "        x_img=x_img.astype(np.float32)\n",
        "        \n",
        "       \n",
        "        return x_img"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K3ukk_-U4V7-"
      },
      "source": [
        "test_path=sorted(glob.glob( \"./test/DICOM/**/*.dcm\",recursive=True ))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IuULlvWU4Hd-"
      },
      "source": [
        "test_dataset = TestMyDataset(test_path)\n",
        "test_loader = torch.utils.data.DataLoader(dataset=test_dataset, batch_size=1,shuffle=False)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XRaYDT2eW67b"
      },
      "source": [
        "def mask_binarization(mask, threshold=None):\n",
        "    if threshold is None:\n",
        "        threshold = 0.5\n",
        "\n",
        "    if isinstance(mask, np.ndarray):\n",
        "        mask_binarized = (mask > threshold).astype(np.uint8)\n",
        "    \n",
        "    elif isinstance(mask, torch.Tensor):\n",
        "        zeros = torch.zeros_like(mask)\n",
        "        ones = torch.ones_like(mask)\n",
        "        \n",
        "        mask_binarized = torch.where(mask > threshold, ones, zeros)\n",
        "    \n",
        "    return mask_binarized"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U5_k6uijAIzT"
      },
      "source": [
        "def rle_encode(mask_image):\n",
        "    pixels = mask_image.flatten()\n",
        "    pixels[0] = 0\n",
        "    pixels[-1] = 0\n",
        "    runs = np.where(pixels[1:] != pixels[:-1])[0] + 2\n",
        "    runs[1::2] = runs[1::2] - runs[:-1:2]\n",
        "    return runs\n",
        "\n",
        "def rle_to_string(runs):\n",
        "    return ' '.join(str(x) for x in runs)\n",
        "\n",
        "\n",
        "preds_string=[]\n",
        "preds = []\n",
        "model.to(device)\n",
        "cnt=0\n",
        "t=0\n",
        "for data in tqdm(test_loader):\n",
        "    \n",
        "\n",
        "    data= data.to(device)\n",
        "    # forward pass: compute predicted outputs by passing inputs to the model\n",
        "    pred = model(data)\n",
        "    pred = pred.sigmoid()\n",
        "    pred = mask_binarization(pred.detach().cpu(), 0.5)\n",
        "    preds.append(pred.detach().cpu())\n",
        "    \n",
        "    if cnt%32==0:\n",
        "        y=preds[cnt][0]\n",
        "        y=y.numpy()\n",
        "        x=data[0].detach().cpu().numpy()\n",
        "        plt.figure(figsize=(16,18))\n",
        "        plt.subplot(1,3,1)\n",
        "        plt.imshow(x[0],cmap='gray')\n",
        "        plt.subplot(1,3,2)\n",
        "        plt.imshow(y[0])\n",
        "        plt.subplot(1,3,3)\n",
        "        plt.imshow(y[1])\n",
        "        plt.show()\n",
        "    cnt= cnt+1\n",
        "    \n",
        "for i in tqdm(range(0, len(preds),64)):\n",
        "  sample = preds[i:i+64]\n",
        "  for c in range(2):\n",
        "    print(c)\n",
        "    for label_code in [1]:\n",
        "      tmp=[]\n",
        "      for s in sample:\n",
        "        s = np.equal(s[0][c], label_code).flatten()*1\n",
        "        tmp+=s.tolist()\n",
        "      enc = rle_to_string(rle_encode(np.array(tmp)))\n",
        "      preds_string.append(enc)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JAJeVdobBCXu"
      },
      "source": [
        "sample_submission = pd.read_csv('./sample_submission.csv')\n",
        "sample_submission['EncodedPixels'] = preds_string\n",
        "sample_submission.to_csv('submission.csv', index=False)"
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}