1stplace_pt1_mine.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "98dc4b56-73d2-42dd-9faa-63f1f18ee691",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    }
   ],
   "source": [
    "!pip install timm\n",
    "!pip install albumentations\n",
    "!pip install segmentation_models_pytorch\n",
    "!pip install monai\n",
    "!pip install imageio\n",
    "!pip install pydicom\n",
    "!pip install pandas\n",
    "!pip install nibabel\n",
    "!pip install matplotlib\n",
    "!pip install torch\n",
    "!pip install cv\n",
    "!pip install pylibjpeg==1.4.0\n",
    "!pip install python_gdcm==3.0.17.1\n",
    "!pip install pylibjpeg-libjpeg\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e2535053-c077-4557-aeae-3483d0d37171",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jovyan/MLDS_project/myenv/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import sys\n",
    "import gc\n",
    "import ast\n",
    "import cv2\n",
    "import imageio\n",
    "import time\n",
    "import timm\n",
    "import pickle\n",
    "import random\n",
    "import pydicom\n",
    "import argparse\n",
    "import warnings\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from glob import glob\n",
    "import nibabel as nib\n",
    "from PIL import Image\n",
    "from tqdm import tqdm\n",
    "import albumentations\n",
    "from pylab import rcParams\n",
    "import matplotlib.pyplot as plt\n",
    "import segmentation_models_pytorch as smp\n",
    "from sklearn.model_selection import KFold, StratifiedKFold\n",
    "\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "import torch.cuda.amp as amp\n",
    "import torch.nn.functional as F\n",
    "from torch.utils.data import DataLoader, Dataset\n",
    "\n",
    "from monai.transforms import Resize\n",
    "import  monai.transforms as transforms\n",
    "\n",
    "%matplotlib inline\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6717b5c5-d7a6-4bbf-9def-953bc0fa2615",
   "metadata": {},
   "outputs": [],
   "source": [
    "rcParams['figure.figsize'] = 20, 8\n",
    "device = torch.device('cuda')\n",
    "torch.backends.cudnn.benchmark = True\n",
    "kernel_type = 'timm3d_manet_unet4b_128_128_128_dsv2_flip12_shift333p7_gd1p5_bs4_lr3e4_20x50ep'\n",
    "load_kernel = None\n",
    "load_last = True\n",
    "n_blocks = 4\n",
    "n_folds = 5\n",
    "backbone = 'resnet18d'\n",
    "\n",
    "DEBUG = False\n",
    "image_sizes = [128, 128, 128]\n",
    "R = Resize(image_sizes)\n",
    "\n",
    "init_lr = 3e-4\n",
    "batch_size = 4\n",
    "drop_rate = 0.\n",
    "drop_path_rate = 0.\n",
    "loss_weights = [1, 1]\n",
    "p_mixup = 0.1\n",
    "\n",
    "data_dir = './Dataset/'\n",
    "use_amp = True\n",
    "num_workers = 4\n",
    "out_dim = 7\n",
    "\n",
    "n_epochs = 10\n",
    "\n",
    "log_dir = './logs'\n",
    "model_dir = './my_models'\n",
    "os.makedirs(log_dir, exist_ok=True)\n",
    "os.makedirs(model_dir, exist_ok=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "097b95fc-c378-410b-adb5-7373b385ceaf",
   "metadata": {},
   "outputs": [],
   "source": [
    "transforms_train = transforms.Compose([\n",
    "    transforms.RandFlipd(keys=[\"image\", \"mask\"], prob=0.5, spatial_axis=1),\n",
    "    transforms.RandFlipd(keys=[\"image\", \"mask\"], prob=0.5, spatial_axis=2),\n",
    "    transforms.RandAffined(keys=[\"image\", \"mask\"], translate_range=[int(x*y) for x, y in zip(image_sizes, [0.3, 0.3, 0.3])], padding_mode='zeros', prob=0.7),\n",
    "    transforms.RandGridDistortiond(keys=(\"image\", \"mask\"), prob=0.5, distort_limit=(-0.01, 0.01), mode=\"nearest\"),    \n",
    "])\n",
    "\n",
    "transforms_valid = transforms.Compose([\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6ebf5c89-e6b5-43ba-9120-d7edffe6e2ac",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_5028/2212733997.py:11: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n",
      "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n",
      "\n",
      "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n",
      "\n",
      "\n",
      "  df['mask_file'].fillna('', inplace=True)\n"
     ]
    },
    {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>StudyInstanceUID</th>\n",
       "      <th>patient_overall</th>\n",
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       "      <th>mask_file</th>\n",
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      ],
      "text/plain": [
       "             StudyInstanceUID  patient_overall  C1  C2  C3  C4  C5  C6  C7  \\\n",
       "82  1.2.826.0.1.3680043.32071                1   0   1   0   1   0   1   1   \n",
       "83  1.2.826.0.1.3680043.30524                1   0   0   0   0   0   1   1   \n",
       "84  1.2.826.0.1.3680043.28025                0   0   0   0   0   0   0   0   \n",
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       "86  1.2.826.0.1.3680043.26990                1   0   0   0   0   1   1   1   \n",
       "\n",
       "                                            mask_file  \\\n",
       "82  ./Dataset/segmentations/1.2.826.0.1.3680043.32...   \n",
       "83  ./Dataset/segmentations/1.2.826.0.1.3680043.30...   \n",
       "84  ./Dataset/segmentations/1.2.826.0.1.3680043.28...   \n",
       "85  ./Dataset/segmentations/1.2.826.0.1.3680043.21...   \n",
       "86  ./Dataset/segmentations/1.2.826.0.1.3680043.26...   \n",
       "\n",
       "                                        image_folder  fold  \n",
       "82  ./Dataset/train_images/1.2.826.0.1.3680043.32071     4  \n",
       "83  ./Dataset/train_images/1.2.826.0.1.3680043.30524     4  \n",
       "84  ./Dataset/train_images/1.2.826.0.1.3680043.28025     4  \n",
       "85  ./Dataset/train_images/1.2.826.0.1.3680043.21321     4  \n",
       "86  ./Dataset/train_images/1.2.826.0.1.3680043.26990     4  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train = pd.read_csv(os.path.join(data_dir, 'train.csv'))\n",
    "\n",
    "mask_files = os.listdir(f'{data_dir}/segmentations')\n",
    "df_mask = pd.DataFrame({\n",
    "    'mask_file': mask_files,\n",
    "})\n",
    "df_mask['StudyInstanceUID'] = df_mask['mask_file'].apply(lambda x: x[:-4])\n",
    "df_mask['mask_file'] = df_mask['mask_file'].apply(lambda x: os.path.join(data_dir, 'segmentations', x))\n",
    "df = df_train.merge(df_mask, on='StudyInstanceUID', how='left')\n",
    "df['image_folder'] = df['StudyInstanceUID'].apply(lambda x: os.path.join(data_dir, 'train_images', x))\n",
    "df['mask_file'].fillna('', inplace=True)\n",
    "\n",
    "df_seg = df.query('mask_file != \"\"').reset_index(drop=True)\n",
    "\n",
    "kf = KFold(5)\n",
    "df_seg['fold'] = -1\n",
    "for fold, (train_idx, valid_idx) in enumerate(kf.split(df_seg, df_seg)):\n",
    "    df_seg.loc[valid_idx, 'fold'] = fold\n",
    "\n",
    "df_seg.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "60e6634d-629e-4caa-b51d-b58fe90f34cb",
   "metadata": {},
   "outputs": [
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       "  </tbody>\n",
       "</table>\n",
       "<p>87 rows × 12 columns</p>\n",
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      ],
      "text/plain": [
       "             StudyInstanceUID  patient_overall  C1  C2  C3  C4  C5  C6  C7  \\\n",
       "0    1.2.826.0.1.3680043.1363                1   0   0   0   0   1   0   0   \n",
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       "3   1.2.826.0.1.3680043.31077                1   0   0   1   1   1   1   0   \n",
       "4   1.2.826.0.1.3680043.17960                0   0   0   0   0   0   0   0   \n",
       "..                        ...              ...  ..  ..  ..  ..  ..  ..  ..   \n",
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       "86  1.2.826.0.1.3680043.26990                1   0   0   0   0   1   1   1   \n",
       "\n",
       "                                            mask_file  \\\n",
       "0   ./Dataset/segmentations/1.2.826.0.1.3680043.13...   \n",
       "1   ./Dataset/segmentations/1.2.826.0.1.3680043.25...   \n",
       "2   ./Dataset/segmentations/1.2.826.0.1.3680043.20...   \n",
       "3   ./Dataset/segmentations/1.2.826.0.1.3680043.31...   \n",
       "4   ./Dataset/segmentations/1.2.826.0.1.3680043.17...   \n",
       "..                                                ...   \n",
       "82  ./Dataset/segmentations/1.2.826.0.1.3680043.32...   \n",
       "83  ./Dataset/segmentations/1.2.826.0.1.3680043.30...   \n",
       "84  ./Dataset/segmentations/1.2.826.0.1.3680043.28...   \n",
       "85  ./Dataset/segmentations/1.2.826.0.1.3680043.21...   \n",
       "86  ./Dataset/segmentations/1.2.826.0.1.3680043.26...   \n",
       "\n",
       "                                        image_folder  fold  \n",
       "0    ./Dataset/train_images/1.2.826.0.1.3680043.1363     0  \n",
       "1   ./Dataset/train_images/1.2.826.0.1.3680043.25704     0  \n",
       "2   ./Dataset/train_images/1.2.826.0.1.3680043.20647     0  \n",
       "3   ./Dataset/train_images/1.2.826.0.1.3680043.31077     0  \n",
       "4   ./Dataset/train_images/1.2.826.0.1.3680043.17960     0  \n",
       "..                                               ...   ...  \n",
       "82  ./Dataset/train_images/1.2.826.0.1.3680043.32071     4  \n",
       "83  ./Dataset/train_images/1.2.826.0.1.3680043.30524     4  \n",
       "84  ./Dataset/train_images/1.2.826.0.1.3680043.28025     4  \n",
       "85  ./Dataset/train_images/1.2.826.0.1.3680043.21321     4  \n",
       "86  ./Dataset/train_images/1.2.826.0.1.3680043.26990     4  \n",
       "\n",
       "[87 rows x 12 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_seg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d72e95b0-0701-49da-b655-33c5ed01776b",
   "metadata": {},
   "outputs": [],
   "source": [
    "revert_list = [\n",
    "    '1.2.826.0.1.3680043.1363',\n",
    "    '1.2.826.0.1.3680043.20120',\n",
    "    '1.2.826.0.1.3680043.2243',\n",
    "    '1.2.826.0.1.3680043.24606',\n",
    "    '1.2.826.0.1.3680043.32071'\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "81a0c6cb-951f-4e1a-b1d1-8b8876366d45",
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_dicom(path):\n",
    "    dicom = pydicom.read_file(path)\n",
    "    data = dicom.pixel_array\n",
    "    data = cv2.resize(data, (image_sizes[0], image_sizes[1]), interpolation = cv2.INTER_LINEAR)\n",
    "    return data\n",
    "\n",
    "\n",
    "def load_dicom_line_par(path):\n",
    "\n",
    "    t_paths = sorted(glob(os.path.join(path, \"*\")),\n",
    "       key=lambda x: int(x.split('/')[-1].split(\".\")[0]))\n",
    "\n",
    "    n_scans = len(t_paths)\n",
    "    indices = np.quantile(list(range(n_scans)), np.linspace(0., 1., image_sizes[2])).round().astype(int)\n",
    "    t_paths = [t_paths[i] for i in indices]\n",
    "\n",
    "    images = []\n",
    "    for filename in t_paths:\n",
    "        images.append(load_dicom(filename))\n",
    "    images = np.stack(images, -1)\n",
    "    \n",
    "    images = images - np.min(images)\n",
    "    images = images / (np.max(images) + 1e-4)\n",
    "    images = (images * 255).astype(np.uint8)\n",
    "\n",
    "    return images\n",
    "\n",
    "\n",
    "def load_sample(row, has_mask=True):\n",
    "\n",
    "    image = load_dicom_line_par(row.image_folder)\n",
    "    if image.ndim < 4:\n",
    "        image = np.expand_dims(image, 0).repeat(3, 0)  # to 3ch\n",
    "\n",
    "    if has_mask:\n",
    "        mask_org = nib.load(row.mask_file).get_fdata()\n",
    "        shape = mask_org.shape\n",
    "        mask_org = mask_org.transpose(1, 0, 2)[::-1, :, ::-1]  # (d, w, h)\n",
    "        mask = np.zeros((7, shape[0], shape[1], shape[2]))\n",
    "        for cid in range(7):\n",
    "            mask[cid] = (mask_org == (cid+1))\n",
    "        mask = mask.astype(np.uint8) * 255\n",
    "        mask = R(mask).numpy()\n",
    "        \n",
    "        return image, mask\n",
    "    else:\n",
    "        return image\n",
    "\n",
    "\n",
    "\n",
    "class SEGDataset(Dataset):\n",
    "    def __init__(self, df, mode, transform):\n",
    "\n",
    "        self.df = df.reset_index()\n",
    "        self.mode = mode\n",
    "        self.transform = transform\n",
    "\n",
    "    def __len__(self):\n",
    "        return self.df.shape[0]\n",
    "\n",
    "    def __getitem__(self, index):\n",
    "        row = self.df.iloc[index]\n",
    "        \n",
    "        \n",
    "        ### using local cache\n",
    "#         image_file = os.path.join(data_dir, f'{row.StudyInstanceUID}.npy')\n",
    "#         mask_file = os.path.join(data_dir, f'{row.StudyInstanceUID}_mask.npy')\n",
    "#         image = np.load(image_file).astype(np.float32)\n",
    "#         mask = np.load(mask_file).astype(np.float32)\n",
    "\n",
    "        image, mask = load_sample(row, has_mask=True)\n",
    "    \n",
    "        if row.StudyInstanceUID in revert_list:\n",
    "            mask = mask[:, :, :, ::-1]\n",
    "\n",
    "        res = self.transform({'image':image, 'mask':mask})\n",
    "        image = res['image'] / 255.\n",
    "        mask = res['mask']\n",
    "        mask = (mask > 127).astype(np.float32)\n",
    "\n",
    "        image, mask = torch.tensor(image).float(), torch.tensor(mask).float()\n",
    "\n",
    "        return image, mask"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "06dd629b-d956-448f-9b4e-c04bf384ae4f",
   "metadata": {},
   "outputs": [],
   "source": [
    "rcParams['figure.figsize'] = 20,8\n",
    "\n",
    "df_show = df_seg\n",
    "dataset_show = SEGDataset(df_show, 'train', transform=transforms_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "7913826a-f1ca-4ef1-9a63-18c6f0d3d780",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_893/234965088.py:81: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
      "  image, mask = torch.tensor(image).float(), torch.tensor(mask).float()\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x800 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x800 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in range(2):\n",
    "    f, axarr = plt.subplots(1,4)\n",
    "    for p in range(4):\n",
    "        idx = i*4+p\n",
    "        img, mask = dataset_show[idx]\n",
    "        img = img[:, :, :, 60]\n",
    "        mask = mask[:, :, :, 60]\n",
    "        mask[0] = mask[0] + mask[3] + mask[6]\n",
    "        mask[1] = mask[1] + mask[4]\n",
    "        mask[2] = mask[2] + mask[5]\n",
    "        mask = mask[:3]\n",
    "        img = img * 0.7 + mask * 0.3\n",
    "        axarr[p].imshow(img.transpose(0, 1).transpose(1,2).squeeze())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7add52a8-3ee2-4dc0-89f3-71c720f6bd70",
   "metadata": {},
   "source": [
    "<h2> Model</h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "bab4cd7a-bac9-4a5a-92f6-2d20b5678a5e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "\n",
    "from segmentation_models_pytorch.base import modules as md\n",
    "\n",
    "\n",
    "class PAB(nn.Module):\n",
    "    def __init__(self, in_channels, out_channels, pab_channels=64):\n",
    "        super(PAB, self).__init__()\n",
    "        # Series of 1x1 conv to generate attention feature maps\n",
    "        self.pab_channels = pab_channels\n",
    "        self.in_channels = in_channels\n",
    "        self.top_conv = nn.Conv2d(in_channels, pab_channels, kernel_size=1)\n",
    "        self.center_conv = nn.Conv2d(in_channels, pab_channels, kernel_size=1)\n",
    "        self.bottom_conv = nn.Conv2d(in_channels, in_channels, kernel_size=3, padding=1)\n",
    "        self.map_softmax = nn.Softmax(dim=1)\n",
    "        self.out_conv = nn.Conv2d(in_channels, in_channels, kernel_size=3, padding=1)\n",
    "\n",
    "    def forward(self, x):\n",
    "        bsize = x.size()[0]\n",
    "        h = x.size()[2]\n",
    "        w = x.size()[3]\n",
    "        d = x.size()[4]\n",
    "        x_top = self.top_conv(x)\n",
    "        x_center = self.center_conv(x)\n",
    "        x_bottom = self.bottom_conv(x)\n",
    "\n",
    "        x_top = x_top.flatten(2)\n",
    "        x_center = x_center.flatten(2).transpose(1, 2)\n",
    "        x_bottom = x_bottom.flatten(2).transpose(1, 2)\n",
    "        sp_map = torch.matmul(x_center, x_top)\n",
    "        sp_map = self.map_softmax(sp_map.view(bsize, -1)).view(bsize, h*w*d, h*w*d)\n",
    "        sp_map = torch.matmul(sp_map, x_bottom)\n",
    "        sp_map = sp_map.reshape(bsize, self.in_channels, h, w, d)\n",
    "        x = x + sp_map\n",
    "        x = self.out_conv(x)\n",
    "        # print('x_top',x_top.shape,'x_center',x_center.shape,'x_bottom',x_bottom.shape,'x',x.shape,'sp_map',sp_map.shape)\n",
    "        return x\n",
    "\n",
    "\n",
    "class MFAB(nn.Module):\n",
    "    def __init__(self, in_channels, skip_channels, out_channels, use_batchnorm=True, reduction=16):\n",
    "        # MFAB is just a modified version of SE-blocks, one for skip, one for input\n",
    "        super(MFAB, self).__init__()\n",
    "        self.hl_conv = nn.Sequential(\n",
    "            md.Conv2dReLU(\n",
    "                in_channels,\n",
    "                in_channels,\n",
    "                kernel_size=3,\n",
    "                padding=1,\n",
    "                use_batchnorm=use_batchnorm,\n",
    "            ),\n",
    "            md.Conv2dReLU(\n",
    "                in_channels,\n",
    "                skip_channels,\n",
    "                kernel_size=1,\n",
    "                use_batchnorm=use_batchnorm,\n",
    "            ),\n",
    "        )\n",
    "        reduced_channels = max(1, skip_channels // reduction)\n",
    "        self.SE_ll = nn.Sequential(\n",
    "            nn.AdaptiveAvgPool2d(1),\n",
    "            nn.Conv2d(skip_channels, reduced_channels, 1),\n",
    "            nn.ReLU(inplace=True),\n",
    "            nn.Conv2d(reduced_channels, skip_channels, 1),\n",
    "            nn.Sigmoid(),\n",
    "        )\n",
    "        self.SE_hl = nn.Sequential(\n",
    "            nn.AdaptiveAvgPool2d(1),\n",
    "            nn.Conv2d(skip_channels, reduced_channels, 1),\n",
    "            nn.ReLU(inplace=True),\n",
    "            nn.Conv2d(reduced_channels, skip_channels, 1),\n",
    "            nn.Sigmoid(),\n",
    "        )\n",
    "        self.conv1 = md.Conv2dReLU(\n",
    "            skip_channels + skip_channels,  # we transform C-prime form high level to C from skip connection\n",
    "            out_channels,\n",
    "            kernel_size=3,\n",
    "            padding=1,\n",
    "            use_batchnorm=use_batchnorm,\n",
    "        )\n",
    "        self.conv2 = md.Conv2dReLU(\n",
    "            out_channels,\n",
    "            out_channels,\n",
    "            kernel_size=3,\n",
    "            padding=1,\n",
    "            use_batchnorm=use_batchnorm,\n",
    "        )\n",
    "\n",
    "    def forward(self, x, skip=None):\n",
    "        x = self.hl_conv(x)\n",
    "        x = F.interpolate(x, scale_factor=2, mode=\"nearest\")\n",
    "        attention_hl = self.SE_hl(x)\n",
    "        if skip is not None:\n",
    "            attention_ll = self.SE_ll(skip)\n",
    "            attention_hl = attention_hl + attention_ll\n",
    "            x = x * attention_hl\n",
    "            x = torch.cat([x, skip], dim=1)\n",
    "        x = self.conv1(x)\n",
    "        x = self.conv2(x)\n",
    "        return x\n",
    "\n",
    "\n",
    "class DecoderBlock(nn.Module):\n",
    "    def __init__(self, in_channels, skip_channels, out_channels, use_batchnorm=True):\n",
    "        super().__init__()\n",
    "        self.conv1 = md.Conv2dReLU(\n",
    "            in_channels + skip_channels,\n",
    "            out_channels,\n",
    "            kernel_size=3,\n",
    "            padding=1,\n",
    "            use_batchnorm=use_batchnorm,\n",
    "        )\n",
    "        self.conv2 = md.Conv2dReLU(\n",
    "            out_channels,\n",
    "            out_channels,\n",
    "            kernel_size=3,\n",
    "            padding=1,\n",
    "            use_batchnorm=use_batchnorm,\n",
    "        )\n",
    "\n",
    "    def forward(self, x, skip=None):\n",
    "        x = F.interpolate(x, scale_factor=2, mode=\"nearest\")\n",
    "        if skip is not None:\n",
    "            x = torch.cat([x, skip], dim=1)\n",
    "        x = self.conv1(x)\n",
    "        x = self.conv2(x)\n",
    "        return x\n",
    "\n",
    "\n",
    "class MAnetDecoder(nn.Module):\n",
    "    def __init__(\n",
    "        self,\n",
    "        encoder_channels,\n",
    "        decoder_channels,\n",
    "        n_blocks=5,\n",
    "        reduction=16,\n",
    "        use_batchnorm=True,\n",
    "        pab_channels=64,\n",
    "    ):\n",
    "        super().__init__()\n",
    "\n",
    "        if n_blocks != len(decoder_channels):\n",
    "            raise ValueError(\n",
    "                \"Model depth is {}, but you provide `decoder_channels` for {} blocks.\".format(\n",
    "                    n_blocks, len(decoder_channels)\n",
    "                )\n",
    "            )\n",
    "\n",
    "        # remove first skip with same spatial resolution\n",
    "        encoder_channels = encoder_channels[1:]\n",
    "\n",
    "        # reverse channels to start from head of encoder\n",
    "        encoder_channels = encoder_channels[::-1]\n",
    "\n",
    "        # computing blocks input and output channels\n",
    "        head_channels = encoder_channels[0]\n",
    "        in_channels = [head_channels] + list(decoder_channels[:-1])\n",
    "        skip_channels = list(encoder_channels[1:]) + [0]\n",
    "        out_channels = decoder_channels\n",
    "\n",
    "        self.center = PAB(head_channels, head_channels, pab_channels=pab_channels)\n",
    "\n",
    "        # combine decoder keyword arguments\n",
    "        kwargs = dict(use_batchnorm=use_batchnorm)  # no attention type here\n",
    "        blocks = [\n",
    "            MFAB(in_ch, skip_ch, out_ch, reduction=reduction, **kwargs)\n",
    "            if skip_ch > 0\n",
    "            else DecoderBlock(in_ch, skip_ch, out_ch, **kwargs)\n",
    "            for in_ch, skip_ch, out_ch in zip(in_channels, skip_channels, out_channels)\n",
    "        ]\n",
    "        # for the last we dont have skip connection -> use simple decoder block\n",
    "        self.blocks = nn.ModuleList(blocks)\n",
    "\n",
    "    def forward(self, *features):\n",
    "\n",
    "        features = features[1:]  # remove first skip with same spatial resolution\n",
    "        features = features[::-1]  # reverse channels to start from head of encoder\n",
    "\n",
    "        head = features[0]\n",
    "        skips = features[1:]\n",
    "\n",
    "        x = self.center(head)\n",
    "        for i, decoder_block in enumerate(self.blocks):\n",
    "            skip = skips[i] if i < len(skips) else None\n",
    "            x = decoder_block(x, skip)\n",
    "\n",
    "        return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "893e9a26-ae4a-437d-89d1-9e6bfbad8e8b",
   "metadata": {},
   "outputs": [],
   "source": [
    "class TimmSegModel(nn.Module):\n",
    "    def __init__(self, backbone, segtype='manet', pretrained=False):\n",
    "        super(TimmSegModel, self).__init__()\n",
    "\n",
    "        self.encoder = timm.create_model(\n",
    "            backbone,\n",
    "            in_chans=3,\n",
    "            features_only=True,\n",
    "            drop_rate=drop_rate,\n",
    "            drop_path_rate=drop_path_rate,\n",
    "            pretrained=pretrained\n",
    "        )\n",
    "        g = self.encoder(torch.rand(1, 3, 64, 64))\n",
    "        encoder_channels = [1] + [_.shape[1] for _ in g]\n",
    "        decoder_channels = [256, 128, 64, 32, 16]\n",
    "        if segtype == 'manet':\n",
    "            self.decoder = MAnetDecoder(\n",
    "                encoder_channels = encoder_channels[:n_blocks+1],\n",
    "                decoder_channels=decoder_channels[:n_blocks],\n",
    "                n_blocks=n_blocks,\n",
    "            )\n",
    "\n",
    "        self.segmentation_head = nn.Conv2d(decoder_channels[n_blocks-1], out_dim, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
    "\n",
    "    def forward(self,x):\n",
    "        global_features = [0] + self.encoder(x)[:n_blocks]\n",
    "        seg_features = self.decoder(*global_features)\n",
    "        seg_features = self.segmentation_head(seg_features)\n",
    "        return seg_features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "13772805-c54a-4d55-bbf4-6e1e3e92ac86",
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\" Conv3d w/ Same Padding\n",
    "modified from:\n",
    "https://github.com/rwightman/pytorch-image-models/blob/a2727c1bf78ba0d7b5727f5f95e37fb7f8866b1f/timm/models/layers/conv2d_same.py\n",
    "https://github.com/rwightman/pytorch-image-models/blob/a2727c1bf78ba0d7b5727f5f95e37fb7f8866b1f/timm/models/layers/padding.py\n",
    "\"\"\"\n",
    "import math\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "from typing import Tuple, Optional, List\n",
    "\n",
    "\n",
    "# Calculate symmetric padding for a convolution\n",
    "def get_padding(kernel_size: int, stride: int = 1, dilation: int = 1, **_) -> int:\n",
    "    padding = ((stride - 1) + dilation * (kernel_size - 1)) // 2\n",
    "    return padding\n",
    "\n",
    "\n",
    "# Calculate asymmetric TensorFlow-like 'SAME' padding for a convolution\n",
    "def get_same_padding(x: int, k: int, s: int, d: int):\n",
    "    return max((math.ceil(x / s) - 1) * s + (k - 1) * d + 1 - x, 0)\n",
    "\n",
    "\n",
    "# Can SAME padding for given args be done statically?\n",
    "def is_static_pad(kernel_size: int, stride: int = 1, dilation: int = 1, **_):\n",
    "    return stride == 1 and (dilation * (kernel_size - 1)) % 2 == 0\n",
    "\n",
    "\n",
    "# Dynamically pad input x with 'SAME' padding for conv with specified args\n",
    "def pad_same(x, k: List[int], s: List[int], d: List[int] = (1, 1, 1), value: float = 0):\n",
    "    ih, iw, iz = x.size()[-3:]\n",
    "    pad_h = get_same_padding(ih, k[0], s[0], d[0])\n",
    "    pad_w = get_same_padding(iw, k[1], s[1], d[1])\n",
    "    pad_z = get_same_padding(iz, k[2], s[2], d[2])\n",
    "    if pad_h > 0 or pad_w > 0 or pad_z > 0:\n",
    "        x = F.pad(x, [pad_w // 2, pad_w - pad_w // 2, pad_h // 2, pad_h - pad_h // 2, pad_z // 2, pad_z - pad_z // 2], value=value)\n",
    "    return x\n",
    "\n",
    "\n",
    "def get_padding_value(padding, kernel_size, **kwargs) -> Tuple[Tuple, bool]:\n",
    "    dynamic = False\n",
    "    if isinstance(padding, str):\n",
    "        # for any string padding, the padding will be calculated for you, one of three ways\n",
    "        padding = padding.lower()\n",
    "        if padding == 'same':\n",
    "            # TF compatible 'SAME' padding, has a performance and GPU memory allocation impact\n",
    "            if is_static_pad(kernel_size, **kwargs):\n",
    "                # static case, no extra overhead\n",
    "                padding = get_padding(kernel_size, **kwargs)\n",
    "            else:\n",
    "                # dynamic 'SAME' padding, has runtime/GPU memory overhead\n",
    "                padding = 0\n",
    "                dynamic = True\n",
    "        elif padding == 'valid':\n",
    "            # 'VALID' padding, same as padding=0\n",
    "            padding = 0\n",
    "        else:\n",
    "            # Default to PyTorch style 'same'-ish symmetric padding\n",
    "            padding = get_padding(kernel_size, **kwargs)\n",
    "    return padding, dynamic\n",
    "\n",
    "\n",
    "def conv3d_same(\n",
    "        x, weight: torch.Tensor, bias: Optional[torch.Tensor] = None, stride: Tuple[int, int, int] = (1, 1, 1),\n",
    "        padding: Tuple[int, int, int] = (0, 0, 0), dilation: Tuple[int, int, int] = (1, 1, 1), groups: int = 1):\n",
    "    x = pad_same(x, weight.shape[-3:], stride, dilation)\n",
    "    return F.conv3d(x, weight, bias, stride, (0, 0, 0), dilation, groups)\n",
    "\n",
    "\n",
    "class Conv3dSame(nn.Conv3d):\n",
    "    \"\"\" Tensorflow like 'SAME' convolution wrapper for 3d convolutions\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(self, in_channels, out_channels, kernel_size, stride=1,\n",
    "                 padding=0, dilation=1, groups=1, bias=True):\n",
    "        super(Conv3dSame, self).__init__(\n",
    "            in_channels, out_channels, kernel_size, stride, 0, dilation, groups, bias)\n",
    "\n",
    "    def forward(self, x):\n",
    "        return conv3d_same(x, self.weight, self.bias, self.stride, self.padding, self.dilation, self.groups)\n",
    "\n",
    "\n",
    "def create_conv3d_pad(in_chs, out_chs, kernel_size, **kwargs):\n",
    "    padding = kwargs.pop('padding', '')\n",
    "    kwargs.setdefault('bias', False)\n",
    "    padding, is_dynamic = get_padding_value(padding, kernel_size, **kwargs)\n",
    "    if is_dynamic:\n",
    "        return Conv3dSame(in_chs, out_chs, kernel_size, **kwargs)\n",
    "    else:\n",
    "        return nn.Conv3d(in_chs, out_chs, kernel_size, padding=padding, **kwargs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "c5e2eef3-b8ce-41db-abff-29b9f57a6c4b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from timm.layers.conv2d_same import Conv2dSame, conv2d_same\n",
    "# from conv3d_same import Conv3dSame\n",
    "\n",
    "\n",
    "def convert_3d(module):\n",
    "\n",
    "    module_output = module\n",
    "    if isinstance(module, torch.nn.BatchNorm2d):\n",
    "        module_output = torch.nn.BatchNorm3d(\n",
    "            module.num_features,\n",
    "            module.eps,\n",
    "            module.momentum,\n",
    "            module.affine,\n",
    "            module.track_running_stats,\n",
    "        )\n",
    "        if module.affine:\n",
    "            with torch.no_grad():\n",
    "                module_output.weight = module.weight\n",
    "                module_output.bias = module.bias\n",
    "        module_output.running_mean = module.running_mean\n",
    "        module_output.running_var = module.running_var\n",
    "        module_output.num_batches_tracked = module.num_batches_tracked\n",
    "        if hasattr(module, \"qconfig\"):\n",
    "            module_output.qconfig = module.qconfig\n",
    "            \n",
    "    elif isinstance(module, Conv2dSame):\n",
    "        module_output = Conv3dSame(\n",
    "            in_channels=module.in_channels,\n",
    "            out_channels=module.out_channels,\n",
    "            kernel_size=module.kernel_size[0],\n",
    "            stride=module.stride[0],\n",
    "            padding=module.padding[0],\n",
    "            dilation=module.dilation[0],\n",
    "            groups=module.groups,\n",
    "            bias=module.bias is not None,\n",
    "        )\n",
    "        module_output.weight = torch.nn.Parameter(module.weight.unsqueeze(-1).repeat(1,1,1,1,module.kernel_size[0]))\n",
    "\n",
    "    elif isinstance(module, torch.nn.Conv2d):\n",
    "        module_output = torch.nn.Conv3d(\n",
    "            in_channels=module.in_channels,\n",
    "            out_channels=module.out_channels,\n",
    "            kernel_size=module.kernel_size[0],\n",
    "            stride=module.stride[0],\n",
    "            padding=module.padding[0],\n",
    "            dilation=module.dilation[0],\n",
    "            groups=module.groups,\n",
    "            bias=module.bias is not None,\n",
    "            padding_mode=module.padding_mode\n",
    "        )\n",
    "        module_output.weight = torch.nn.Parameter(module.weight.unsqueeze(-1).repeat(1,1,1,1,module.kernel_size[0]))\n",
    "\n",
    "    elif isinstance(module, torch.nn.MaxPool2d):\n",
    "        module_output = torch.nn.MaxPool3d(\n",
    "            kernel_size=module.kernel_size,\n",
    "            stride=module.stride,\n",
    "            padding=module.padding,\n",
    "            dilation=module.dilation,\n",
    "            ceil_mode=module.ceil_mode,\n",
    "        )\n",
    "    elif isinstance(module, torch.nn.AvgPool2d):\n",
    "        module_output = torch.nn.AvgPool3d(\n",
    "            kernel_size=module.kernel_size,\n",
    "            stride=module.stride,\n",
    "            padding=module.padding,\n",
    "            ceil_mode=module.ceil_mode,\n",
    "        )\n",
    "\n",
    "    for name, child in module.named_children():\n",
    "        module_output.add_module(\n",
    "            name, convert_3d(child)\n",
    "        )\n",
    "    del module\n",
    "\n",
    "    return module_output\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "9f5d9bf9-20b2-4737-9aa2-15744bf6a711",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([1, 7, 128, 128, 128])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m = TimmSegModel(backbone)\n",
    "m = convert_3d(m)\n",
    "m(torch.rand(1, 3, 128,128,128)).shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f3a64097-560d-425e-91c0-4b8feaf38fe0",
   "metadata": {},
   "source": [
    "<h2>Loss & Metric</h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "fee57759-7646-4ce4-aabf-8541d4914be7",
   "metadata": {},
   "outputs": [],
   "source": [
    "from typing import Any, Dict, Optional\n",
    "\n",
    "\n",
    "def binary_dice_score(\n",
    "    y_pred: torch.Tensor,\n",
    "    y_true: torch.Tensor,\n",
    "    threshold: Optional[float] = None,\n",
    "    nan_score_on_empty=False,\n",
    "    eps: float = 1e-7,\n",
    ") -> float:\n",
    "\n",
    "    if threshold is not None:\n",
    "        y_pred = (y_pred > threshold).to(y_true.dtype)\n",
    "\n",
    "    intersection = torch.sum(y_pred * y_true).item()\n",
    "    cardinality = (torch.sum(y_pred) + torch.sum(y_true)).item()\n",
    "\n",
    "    score = (2.0 * intersection) / (cardinality + eps)\n",
    "\n",
    "    has_targets = torch.sum(y_true) > 0\n",
    "    has_predicted = torch.sum(y_pred) > 0\n",
    "\n",
    "    if not has_targets:\n",
    "        if nan_score_on_empty:\n",
    "            score = np.nan\n",
    "        else:\n",
    "            score = float(not has_predicted)\n",
    "    return score\n",
    "\n",
    "\n",
    "def multilabel_dice_score(\n",
    "    y_true: torch.Tensor,\n",
    "    y_pred: torch.Tensor,\n",
    "    threshold=None,\n",
    "    eps=1e-7,\n",
    "    nan_score_on_empty=False,\n",
    "):\n",
    "    ious = []\n",
    "    num_classes = y_pred.size(0)\n",
    "    for class_index in range(num_classes):\n",
    "        iou = binary_dice_score(\n",
    "            y_pred=y_pred[class_index],\n",
    "            y_true=y_true[class_index],\n",
    "            threshold=threshold,\n",
    "            nan_score_on_empty=nan_score_on_empty,\n",
    "            eps=eps,\n",
    "        )\n",
    "        ious.append(iou)\n",
    "\n",
    "    return ious\n",
    "\n",
    "\n",
    "def dice_loss(input, target):\n",
    "    input = torch.sigmoid(input)\n",
    "    smooth = 1.0\n",
    "    iflat = input.view(-1)\n",
    "    tflat = target.view(-1)\n",
    "    intersection = (iflat * tflat).sum()\n",
    "    return 1 - ((2.0 * intersection + smooth) / (iflat.sum() + tflat.sum() + smooth))\n",
    "\n",
    "\n",
    "def bce_dice(input, target, loss_weights=loss_weights):\n",
    "    loss1 = loss_weights[0] * nn.BCEWithLogitsLoss()(input, target)\n",
    "    loss2 = loss_weights[1] * dice_loss(input, target)\n",
    "    return (loss1 + loss2) / sum(loss_weights)\n",
    "\n",
    "criterion = bce_dice"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "020e34fb-4b69-49b6-8f18-4af64625cdcd",
   "metadata": {},
   "source": [
    "<h2>Train & Valid func</h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "6f2cb742-7151-4bff-b066-166df5694118",
   "metadata": {},
   "outputs": [],
   "source": [
    "def mixup(input, truth, clip=[0, 1]):\n",
    "    indices = torch.randperm(input.size(0))\n",
    "    shuffled_input = input[indices]\n",
    "    shuffled_labels = truth[indices]\n",
    "\n",
    "    lam = np.random.uniform(clip[0], clip[1])\n",
    "    input = input * lam + shuffled_input * (1 - lam)\n",
    "    return input, truth, shuffled_labels, lam\n",
    "\n",
    "\n",
    "def train_func(model, loader_train, optimizer, scaler=None):\n",
    "    model.train()\n",
    "    train_loss = []\n",
    "    bar = tqdm(loader_train)\n",
    "    for images, gt_masks in bar:\n",
    "        optimizer.zero_grad()\n",
    "        images = images.cuda()\n",
    "        gt_masks = gt_masks.cuda()\n",
    "\n",
    "        do_mixup = False\n",
    "        if random.random() < p_mixup:\n",
    "            do_mixup = True\n",
    "            images, gt_masks, gt_masks_sfl, lam = mixup(images, gt_masks)\n",
    "\n",
    "        with amp.autocast():\n",
    "            logits = model(images)\n",
    "            loss = criterion(logits, gt_masks)\n",
    "            if do_mixup:\n",
    "                loss2 = criterion(logits, gt_masks_sfl)\n",
    "                loss = loss * lam  + loss2 * (1 - lam)\n",
    "\n",
    "        train_loss.append(loss.item())\n",
    "        scaler.scale(loss).backward()\n",
    "        scaler.step(optimizer)\n",
    "        scaler.update()\n",
    "\n",
    "        bar.set_description(f'smth:{np.mean(train_loss[-30:]):.4f}')\n",
    "\n",
    "    return np.mean(train_loss)\n",
    "\n",
    "\n",
    "def valid_func(model, loader_valid):\n",
    "    model.eval()\n",
    "    valid_loss = []\n",
    "    outputs = []\n",
    "    ths = [0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8]\n",
    "    batch_metrics = [[]] * 7\n",
    "    bar = tqdm(loader_valid)\n",
    "    with torch.no_grad():\n",
    "        for images, gt_masks in bar:\n",
    "            images = images.cuda()\n",
    "            gt_masks = gt_masks.cuda()\n",
    "\n",
    "            logits = model(images)\n",
    "            loss = criterion(logits, gt_masks)\n",
    "            valid_loss.append(loss.item())\n",
    "            for thi, th in enumerate(ths):\n",
    "                pred = (logits.sigmoid() > th).float().detach()\n",
    "                for i in range(logits.shape[0]):\n",
    "                    tmp = multilabel_dice_score(\n",
    "                        y_pred=logits[i].sigmoid().cpu(),\n",
    "                        y_true=gt_masks[i].cpu(),\n",
    "                        threshold=0.5,\n",
    "                    )\n",
    "                    batch_metrics[thi].extend(tmp)\n",
    "            bar.set_description(f'smth:{np.mean(valid_loss[-30:]):.4f}')\n",
    "            \n",
    "    metrics = [np.mean(this_metric) for this_metric in batch_metrics]\n",
    "    print('best th:', ths[np.argmax(metrics)], 'best dc:', np.max(metrics))\n",
    "\n",
    "    return np.mean(valid_loss), np.max(metrics)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "820d6198-627e-4e80-901a-e2863690007f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jovyan/MLDS_project/myenv/lib/python3.11/site-packages/torch/optim/lr_scheduler.py:143: UserWarning: Detected call of `lr_scheduler.step()` before `optimizer.step()`. In PyTorch 1.1.0 and later, you should call them in the opposite order: `optimizer.step()` before `lr_scheduler.step()`.  Failure to do this will result in PyTorch skipping the first value of the learning rate schedule. See more details at https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate\n",
      "  warnings.warn(\"Detected call of `lr_scheduler.step()` before `optimizer.step()`. \"\n",
      "/home/jovyan/MLDS_project/myenv/lib/python3.11/site-packages/torch/optim/lr_scheduler.py:156: UserWarning: The epoch parameter in `scheduler.step()` was not necessary and is being deprecated where possible. Please use `scheduler.step()` to step the scheduler. During the deprecation, if epoch is different from None, the closed form is used instead of the new chainable form, where available. Please open an issue if you are unable to replicate your use case: https://github.com/pytorch/pytorch/issues/new/choose.\n",
      "  warnings.warn(EPOCH_DEPRECATION_WARNING, UserWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7ff709059f90>]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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      "text/plain": [
       "<Figure size 2000x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rcParams['figure.figsize'] = 20, 2\n",
    "optimizer = optim.AdamW(m.parameters(), lr=init_lr)\n",
    "scheduler_cosine = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, 1000)\n",
    "lrs = []\n",
    "for epoch in range(1, 1000+1):\n",
    "    scheduler_cosine.step(epoch-1)\n",
    "    lrs.append(optimizer.param_groups[0][\"lr\"])\n",
    "plt.plot(range(len(lrs)), lrs)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f0c0ca58-07c7-43e4-b000-69d12fc8bdd4",
   "metadata": {},
   "source": [
    "<h2>Training</h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "28ca905a-0e81-44ad-ab88-92043d8b4f68",
   "metadata": {},
   "outputs": [],
   "source": [
    "def run(fold):\n",
    "\n",
    "    log_file = os.path.join(log_dir, f'{kernel_type}.txt')\n",
    "    model_file = os.path.join(model_dir, f'{kernel_type}_fold{fold}_best.pth')\n",
    "\n",
    "    train_ = df_seg[df_seg['fold'] != fold].reset_index(drop=True)\n",
    "    valid_ = df_seg[df_seg['fold'] == fold].reset_index(drop=True)\n",
    "    dataset_train = SEGDataset(train_, 'train', transform=transforms_train)\n",
    "    dataset_valid = SEGDataset(valid_, 'valid', transform=transforms_valid)\n",
    "    loader_train = torch.utils.data.DataLoader(dataset_train, batch_size=batch_size, shuffle=True, num_workers=num_workers)\n",
    "    loader_valid = torch.utils.data.DataLoader(dataset_valid, batch_size=batch_size, shuffle=False, num_workers=num_workers)\n",
    "\n",
    "    model = TimmSegModel(backbone, pretrained=True)\n",
    "    model = convert_3d(model)\n",
    "    model = model.to(device)\n",
    "\n",
    "    optimizer = optim.AdamW(model.parameters(), lr=init_lr)\n",
    "    scaler = torch.cuda.amp.GradScaler()\n",
    "    from_epoch = 0\n",
    "    metric_best = 0.\n",
    "    loss_min = np.inf\n",
    "\n",
    "    scheduler_cosine = torch.optim.lr_scheduler.CosineAnnealingWarmRestarts(optimizer, n_epochs)\n",
    "\n",
    "    print(len(dataset_train), len(dataset_valid))\n",
    "\n",
    "    train_losses = []\n",
    "    valid_losses = []\n",
    "    metrics = []\n",
    "\n",
    "    for epoch in range(1, n_epochs+1):\n",
    "        scheduler_cosine.step(epoch-1)\n",
    "\n",
    "        print(time.ctime(), 'Epoch:', epoch)\n",
    "\n",
    "        train_loss = train_func(model, loader_train, optimizer, scaler)\n",
    "        valid_loss, metric = valid_func(model, loader_valid)\n",
    "\n",
    "        train_losses.append(train_loss)\n",
    "        valid_losses.append(valid_loss)\n",
    "        metrics.append(metric)\n",
    "\n",
    "        content = time.ctime() + ' ' + f'Fold {fold}, Epoch {epoch}, lr: {optimizer.param_groups[0][\"lr\"]:.7f}, train loss: {train_loss:.5f}, valid loss: {valid_loss:.5f}, metric: {(metric):.6f}.'\n",
    "        print(content)\n",
    "        with open(log_file, 'a') as appender:\n",
    "            appender.write(content + '\\n')\n",
    "\n",
    "        if metric > metric_best:\n",
    "            print(f'metric_best ({metric_best:.6f} --> {metric:.6f}). Saving model ...')\n",
    "            torch.save(model.state_dict(), model_file)\n",
    "            metric_best = metric\n",
    "\n",
    "        # Save Last\n",
    "        if not DEBUG:\n",
    "            torch.save(\n",
    "                {\n",
    "                    'epoch': epoch,\n",
    "                    'model_state_dict': model.state_dict(),\n",
    "                    'optimizer_state_dict': optimizer.state_dict(),\n",
    "                    'scaler_state_dict': scaler.state_dict() if scaler else None,\n",
    "                    'score_best': metric_best,\n",
    "                },\n",
    "                model_file.replace('_best', '_last')\n",
    "            )\n",
    "    \n",
    "    # Plot losses\n",
    "    # Plot losses and metrics\n",
    "    plt.figure(figsize=(12, 6))\n",
    "    \n",
    "    plt.subplot(1, 2, 1)\n",
    "    plt.plot(train_losses, label='Train Loss')\n",
    "    plt.plot(valid_losses, label='Valid Loss')\n",
    "    plt.title(f'Fold {fold} Losses')\n",
    "    plt.xlabel('Epochs')\n",
    "    plt.ylabel('Loss')\n",
    "    plt.legend()\n",
    "\n",
    "    plt.subplot(1, 2, 2)\n",
    "    plt.plot(metrics, label='Validation Metric')\n",
    "    plt.title(f'Fold {fold} Metric')\n",
    "    plt.xlabel('Epochs')\n",
    "    plt.ylabel('Metric')\n",
    "    plt.legend()\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.savefig(f'losses_and_metrics_fold_{fold}.png')\n",
    "    plt.show()\n",
    "\n",
    "    del model\n",
    "    torch.cuda.empty_cache()\n",
    "    gc.collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "69ed4630-a607-4c7e-bc96-34263346afeb",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# run(0)\n",
    "# run(1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "69f1491e-0b47-4c91-ba3e-9976b42ee02b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:timm.models._builder:Loading pretrained weights from Hugging Face hub (timm/resnet18d.ra2_in1k)\n",
      "INFO:timm.models._hub:[timm/resnet18d.ra2_in1k] Safe alternative available for 'pytorch_model.bin' (as 'model.safetensors'). Loading weights using safetensors.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "70 17\n",
      "Sat May 18 12:42:34 2024 Epoch: 1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/18 [00:00<?, ?it/s]/tmp/ipykernel_5028/234965088.py:81: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
      "  image, mask = torch.tensor(image).float(), torch.tensor(mask).float()\n",
      "/tmp/ipykernel_5028/234965088.py:81: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
      "  image, mask = torch.tensor(image).float(), torch.tensor(mask).float()\n",
      "/tmp/ipykernel_5028/234965088.py:81: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
      "  image, mask = torch.tensor(image).float(), torch.tensor(mask).float()\n",
      "/tmp/ipykernel_5028/234965088.py:81: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
      "  image, mask = torch.tensor(image).float(), torch.tensor(mask).float()\n",
      "smth:0.6570: 100%|██████████| 18/18 [22:53<00:00, 76.30s/it] \n",
      "smth:0.5372: 100%|██████████| 5/5 [23:55<00:00, 287.06s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "best th: 0.2 best dc: 9.401636134435131e-05\n",
      "Sat May 18 13:29:22 2024 Fold 2, Epoch 1, lr: 0.0003000, train loss: 0.65702, valid loss: 0.53722, metric: 0.000094.\n",
      "metric_best (0.000000 --> 0.000094). Saving model ...\n",
      "Sat May 18 13:29:23 2024 Epoch: 2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/18 [00:00<?, ?it/s]/tmp/ipykernel_5028/234965088.py:81: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
      "  image, mask = torch.tensor(image).float(), torch.tensor(mask).float()\n",
      "/tmp/ipykernel_5028/234965088.py:81: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
      "  image, mask = torch.tensor(image).float(), torch.tensor(mask).float()\n",
      "/tmp/ipykernel_5028/234965088.py:81: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
      "  image, mask = torch.tensor(image).float(), torch.tensor(mask).float()\n",
      "/tmp/ipykernel_5028/234965088.py:81: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
      "  image, mask = torch.tensor(image).float(), torch.tensor(mask).float()\n",
      "smth:0.5268: 100%|██████████| 18/18 [23:53<00:00, 79.64s/it]  \n",
      "smth:0.4989: 100%|██████████| 5/5 [22:53<00:00, 274.69s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "best th: 0.2 best dc: 0.035424031948031884\n",
      "Sat May 18 14:16:10 2024 Fold 2, Epoch 2, lr: 0.0002927, train loss: 0.52678, valid loss: 0.49886, metric: 0.035424.\n",
      "metric_best (0.000094 --> 0.035424). Saving model ...\n",
      "Sat May 18 14:16:11 2024 Epoch: 3\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/18 [00:00<?, ?it/s]/tmp/ipykernel_5028/234965088.py:81: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
      "  image, mask = torch.tensor(image).float(), torch.tensor(mask).float()\n",
      "/tmp/ipykernel_5028/234965088.py:81: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
      "  image, mask = torch.tensor(image).float(), torch.tensor(mask).float()\n",
      "/tmp/ipykernel_5028/234965088.py:81: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
      "  image, mask = torch.tensor(image).float(), torch.tensor(mask).float()\n",
      "/tmp/ipykernel_5028/234965088.py:81: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
      "  image, mask = torch.tensor(image).float(), torch.tensor(mask).float()\n",
      "smth:0.4909: 100%|██████████| 18/18 [19:37<00:00, 65.40s/it]  \n",
      "smth:0.4862: 100%|██████████| 5/5 [23:18<00:00, 279.66s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "best th: 0.2 best dc: 0.09388088800506664\n",
      "Sat May 18 14:59:06 2024 Fold 2, Epoch 3, lr: 0.0002714, train loss: 0.49092, valid loss: 0.48618, metric: 0.093881.\n",
      "metric_best (0.035424 --> 0.093881). Saving model ...\n",
      "Sat May 18 14:59:07 2024 Epoch: 4\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/18 [00:00<?, ?it/s]/tmp/ipykernel_5028/234965088.py:81: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
      "  image, mask = torch.tensor(image).float(), torch.tensor(mask).float()\n",
      "/tmp/ipykernel_5028/234965088.py:81: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
      "  image, mask = torch.tensor(image).float(), torch.tensor(mask).float()\n",
      "/tmp/ipykernel_5028/234965088.py:81: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
      "  image, mask = torch.tensor(image).float(), torch.tensor(mask).float()\n",
      "/tmp/ipykernel_5028/234965088.py:81: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
      "  image, mask = torch.tensor(image).float(), torch.tensor(mask).float()\n",
      "smth:0.4718: 100%|██████████| 18/18 [14:52<00:00, 49.57s/it]  \n",
      "smth:0.4797: 100%|██████████| 5/5 [22:29<00:00, 269.92s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "best th: 0.2 best dc: 0.15198926878676322\n",
      "Sat May 18 15:36:29 2024 Fold 2, Epoch 4, lr: 0.0002382, train loss: 0.47184, valid loss: 0.47970, metric: 0.151989.\n",
      "metric_best (0.093881 --> 0.151989). Saving model ...\n",
      "Sat May 18 15:36:29 2024 Epoch: 5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/18 [00:00<?, ?it/s]/tmp/ipykernel_5028/234965088.py:81: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
      "  image, mask = torch.tensor(image).float(), torch.tensor(mask).float()\n",
      "/tmp/ipykernel_5028/234965088.py:81: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
      "  image, mask = torch.tensor(image).float(), torch.tensor(mask).float()\n",
      "/tmp/ipykernel_5028/234965088.py:81: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
      "  image, mask = torch.tensor(image).float(), torch.tensor(mask).float()\n",
      "/tmp/ipykernel_5028/234965088.py:81: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
      "  image, mask = torch.tensor(image).float(), torch.tensor(mask).float()\n",
      "smth:0.4515: 100%|██████████| 18/18 [23:07<00:00, 77.08s/it]  \n",
      "smth:0.4464: 100%|██████████| 5/5 [23:25<00:00, 281.03s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "best th: 0.2 best dc: 0.1670779642948692\n",
      "Sat May 18 16:23:02 2024 Fold 2, Epoch 5, lr: 0.0001964, train loss: 0.45155, valid loss: 0.44645, metric: 0.167078.\n",
      "metric_best (0.151989 --> 0.167078). Saving model ...\n",
      "Sat May 18 16:23:02 2024 Epoch: 6\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/18 [00:00<?, ?it/s]/tmp/ipykernel_5028/234965088.py:81: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
      "  image, mask = torch.tensor(image).float(), torch.tensor(mask).float()\n",
      "/tmp/ipykernel_5028/234965088.py:81: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
      "  image, mask = torch.tensor(image).float(), torch.tensor(mask).float()\n",
      "/tmp/ipykernel_5028/234965088.py:81: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
      "  image, mask = torch.tensor(image).float(), torch.tensor(mask).float()\n",
      "/tmp/ipykernel_5028/234965088.py:81: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
      "  image, mask = torch.tensor(image).float(), torch.tensor(mask).float()\n",
      "smth:0.4334: 100%|██████████| 18/18 [19:12<00:00, 64.01s/it]  \n",
      "smth:0.4372: 100%|██████████| 5/5 [22:45<00:00, 273.07s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "best th: 0.2 best dc: 0.15637483311637895\n",
      "Sat May 18 17:05:00 2024 Fold 2, Epoch 6, lr: 0.0001500, train loss: 0.43338, valid loss: 0.43719, metric: 0.156375.\n",
      "Sat May 18 17:05:01 2024 Epoch: 7\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/18 [00:00<?, ?it/s]/tmp/ipykernel_5028/234965088.py:81: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
      "  image, mask = torch.tensor(image).float(), torch.tensor(mask).float()\n",
      "/tmp/ipykernel_5028/234965088.py:81: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
      "  image, mask = torch.tensor(image).float(), torch.tensor(mask).float()\n",
      "/tmp/ipykernel_5028/234965088.py:81: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
      "  image, mask = torch.tensor(image).float(), torch.tensor(mask).float()\n",
      "/tmp/ipykernel_5028/234965088.py:81: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
      "  image, mask = torch.tensor(image).float(), torch.tensor(mask).float()\n",
      "smth:0.4103: 100%|██████████| 18/18 [19:39<00:00, 65.50s/it]  \n",
      "smth:0.4015: 100%|██████████| 5/5 [23:59<00:00, 287.95s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "best th: 0.2 best dc: 0.24487682755403042\n",
      "Sat May 18 17:48:39 2024 Fold 2, Epoch 7, lr: 0.0001036, train loss: 0.41033, valid loss: 0.40149, metric: 0.244877.\n",
      "metric_best (0.167078 --> 0.244877). Saving model ...\n",
      "Sat May 18 17:48:40 2024 Epoch: 8\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/18 [00:00<?, ?it/s]/tmp/ipykernel_5028/234965088.py:81: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
      "  image, mask = torch.tensor(image).float(), torch.tensor(mask).float()\n",
      "/tmp/ipykernel_5028/234965088.py:81: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
      "  image, mask = torch.tensor(image).float(), torch.tensor(mask).float()\n",
      "/tmp/ipykernel_5028/234965088.py:81: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
      "  image, mask = torch.tensor(image).float(), torch.tensor(mask).float()\n",
      "/tmp/ipykernel_5028/234965088.py:81: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
      "  image, mask = torch.tensor(image).float(), torch.tensor(mask).float()\n",
      "smth:0.3877: 100%|██████████| 18/18 [21:28<00:00, 71.57s/it]  \n",
      "smth:0.3852: 100%|██████████| 5/5 [23:13<00:00, 278.63s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "best th: 0.2 best dc: 0.2642449025832354\n",
      "Sat May 18 18:33:21 2024 Fold 2, Epoch 8, lr: 0.0000618, train loss: 0.38768, valid loss: 0.38517, metric: 0.264245.\n",
      "metric_best (0.244877 --> 0.264245). Saving model ...\n",
      "Sat May 18 18:33:22 2024 Epoch: 9\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/18 [00:00<?, ?it/s]/tmp/ipykernel_5028/234965088.py:81: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
      "  image, mask = torch.tensor(image).float(), torch.tensor(mask).float()\n",
      "/tmp/ipykernel_5028/234965088.py:81: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
      "  image, mask = torch.tensor(image).float(), torch.tensor(mask).float()\n",
      "/tmp/ipykernel_5028/234965088.py:81: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
      "  image, mask = torch.tensor(image).float(), torch.tensor(mask).float()\n",
      "/tmp/ipykernel_5028/234965088.py:81: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
      "  image, mask = torch.tensor(image).float(), torch.tensor(mask).float()\n",
      "smth:0.3846: 100%|██████████| 18/18 [18:41<00:00, 62.32s/it]\n",
      "smth:0.3805: 100%|██████████| 5/5 [23:43<00:00, 284.63s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "best th: 0.2 best dc: 0.2702205217126899\n",
      "Sat May 18 19:15:47 2024 Fold 2, Epoch 9, lr: 0.0000286, train loss: 0.38463, valid loss: 0.38051, metric: 0.270221.\n",
      "metric_best (0.264245 --> 0.270221). Saving model ...\n",
      "Sat May 18 19:15:48 2024 Epoch: 10\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/18 [00:00<?, ?it/s]/tmp/ipykernel_5028/234965088.py:81: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
      "  image, mask = torch.tensor(image).float(), torch.tensor(mask).float()\n",
      "/tmp/ipykernel_5028/234965088.py:81: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
      "  image, mask = torch.tensor(image).float(), torch.tensor(mask).float()\n",
      "/tmp/ipykernel_5028/234965088.py:81: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
      "  image, mask = torch.tensor(image).float(), torch.tensor(mask).float()\n",
      "/tmp/ipykernel_5028/234965088.py:81: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
      "  image, mask = torch.tensor(image).float(), torch.tensor(mask).float()\n",
      "smth:0.3883: 100%|██████████| 18/18 [18:42<00:00, 62.34s/it]  \n",
      "smth:0.3773: 100%|██████████| 5/5 [24:34<00:00, 294.98s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "best th: 0.2 best dc: 0.27290530066935176\n",
      "Sat May 18 19:59:05 2024 Fold 2, Epoch 10, lr: 0.0000073, train loss: 0.38830, valid loss: 0.37728, metric: 0.272905.\n",
      "metric_best (0.270221 --> 0.272905). Saving model ...\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "run(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "e8b63339-9f78-45e1-9a6c-accf47e90928",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "70 17\n",
      "Sun May 12 17:43:02 2024 Epoch: 1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/18 [00:00<?, ?it/s]/tmp/ipykernel_113/234965088.py:81: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
      "  image, mask = torch.tensor(image).float(), torch.tensor(mask).float()\n",
      "/tmp/ipykernel_113/234965088.py:81: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
      "  image, mask = torch.tensor(image).float(), torch.tensor(mask).float()\n",
      "/tmp/ipykernel_113/234965088.py:81: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
      "  image, mask = torch.tensor(image).float(), torch.tensor(mask).float()\n",
      "/tmp/ipykernel_113/234965088.py:81: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
      "  image, mask = torch.tensor(image).float(), torch.tensor(mask).float()\n",
      "smth:0.6768: 100%|██████████| 18/18 [20:29<00:00, 68.32s/it]  \n",
      "smth:0.5820: 100%|██████████| 5/5 [08:25<00:00, 101.16s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "best th: 0.2 best dc: 0.00027754991984714294\n",
      "Sun May 12 18:11:58 2024 Fold 3, Epoch 1, lr: 0.0003000, train loss: 0.67675, valid loss: 0.58200, metric: 0.000278.\n",
      "metric_best (0.000000 --> 0.000278). Saving model ...\n",
      "Sun May 12 18:11:58 2024 Epoch: 2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/18 [00:00<?, ?it/s]/tmp/ipykernel_113/234965088.py:81: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
      "  image, mask = torch.tensor(image).float(), torch.tensor(mask).float()\n",
      "/tmp/ipykernel_113/234965088.py:81: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
      "  image, mask = torch.tensor(image).float(), torch.tensor(mask).float()\n",
      "/tmp/ipykernel_113/234965088.py:81: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
      "  image, mask = torch.tensor(image).float(), torch.tensor(mask).float()\n",
      "/tmp/ipykernel_113/234965088.py:81: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
      "  image, mask = torch.tensor(image).float(), torch.tensor(mask).float()\n",
      "smth:0.5407: 100%|██████████| 18/18 [17:18<00:00, 57.70s/it]  \n",
      "smth:0.5392: 100%|██████████| 5/5 [07:49<00:00, 93.98s/it] \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "best th: 0.2 best dc: 0.00037511827861450296\n",
      "Sun May 12 18:37:07 2024 Fold 3, Epoch 2, lr: 0.0002927, train loss: 0.54069, valid loss: 0.53919, metric: 0.000375.\n",
      "metric_best (0.000278 --> 0.000375). Saving model ...\n",
      "Sun May 12 18:37:07 2024 Epoch: 3\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/18 [00:00<?, ?it/s]/tmp/ipykernel_113/234965088.py:81: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
      "  image, mask = torch.tensor(image).float(), torch.tensor(mask).float()\n",
      "/tmp/ipykernel_113/234965088.py:81: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
      "  image, mask = torch.tensor(image).float(), torch.tensor(mask).float()\n",
      "/tmp/ipykernel_113/234965088.py:81: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
      "  image, mask = torch.tensor(image).float(), torch.tensor(mask).float()\n",
      "/tmp/ipykernel_113/234965088.py:81: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
      "  image, mask = torch.tensor(image).float(), torch.tensor(mask).float()\n",
      "smth:0.5038: 100%|██████████| 18/18 [21:08<00:00, 70.48s/it]  \n",
      "smth:0.4991: 100%|██████████| 5/5 [05:00<00:00, 60.05s/it] \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "best th: 0.2 best dc: 0.05272905467791157\n",
      "Sun May 12 19:03:16 2024 Fold 3, Epoch 3, lr: 0.0002714, train loss: 0.50381, valid loss: 0.49905, metric: 0.052729.\n",
      "metric_best (0.000375 --> 0.052729). Saving model ...\n",
      "Sun May 12 19:03:17 2024 Epoch: 4\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/18 [00:00<?, ?it/s]/tmp/ipykernel_113/234965088.py:81: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
      "  image, mask = torch.tensor(image).float(), torch.tensor(mask).float()\n",
      "/tmp/ipykernel_113/234965088.py:81: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
      "  image, mask = torch.tensor(image).float(), torch.tensor(mask).float()\n",
      "/tmp/ipykernel_113/234965088.py:81: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
      "  image, mask = torch.tensor(image).float(), torch.tensor(mask).float()\n",
      "/tmp/ipykernel_113/234965088.py:81: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
      "  image, mask = torch.tensor(image).float(), torch.tensor(mask).float()\n",
      "smth:0.4743: 100%|██████████| 18/18 [17:43<00:00, 59.09s/it]  \n",
      "smth:0.4721: 100%|██████████| 5/5 [06:00<00:00, 72.19s/it] \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "best th: 0.2 best dc: 0.11436232413979562\n",
      "Sun May 12 19:27:02 2024 Fold 3, Epoch 4, lr: 0.0002382, train loss: 0.47429, valid loss: 0.47209, metric: 0.114362.\n",
      "metric_best (0.052729 --> 0.114362). Saving model ...\n",
      "Sun May 12 19:27:02 2024 Epoch: 5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/18 [00:00<?, ?it/s]/tmp/ipykernel_113/234965088.py:81: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
      "  image, mask = torch.tensor(image).float(), torch.tensor(mask).float()\n",
      "/tmp/ipykernel_113/234965088.py:81: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
      "  image, mask = torch.tensor(image).float(), torch.tensor(mask).float()\n",
      "/tmp/ipykernel_113/234965088.py:81: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
      "  image, mask = torch.tensor(image).float(), torch.tensor(mask).float()\n",
      "/tmp/ipykernel_113/234965088.py:81: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
      "  image, mask = torch.tensor(image).float(), torch.tensor(mask).float()\n",
      "smth:0.4474: 100%|██████████| 18/18 [17:59<00:00, 59.96s/it]  \n",
      "smth:0.4426: 100%|██████████| 5/5 [06:05<00:00, 73.19s/it] \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "best th: 0.2 best dc: 0.16821121103105904\n",
      "Sun May 12 19:51:08 2024 Fold 3, Epoch 5, lr: 0.0001964, train loss: 0.44736, valid loss: 0.44264, metric: 0.168211.\n",
      "metric_best (0.114362 --> 0.168211). Saving model ...\n",
      "Sun May 12 19:51:08 2024 Epoch: 6\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/18 [00:00<?, ?it/s]/tmp/ipykernel_113/234965088.py:81: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
      "  image, mask = torch.tensor(image).float(), torch.tensor(mask).float()\n",
      "/tmp/ipykernel_113/234965088.py:81: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
      "  image, mask = torch.tensor(image).float(), torch.tensor(mask).float()\n",
      "/tmp/ipykernel_113/234965088.py:81: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
      "  image, mask = torch.tensor(image).float(), torch.tensor(mask).float()\n",
      "/tmp/ipykernel_113/234965088.py:81: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
      "  image, mask = torch.tensor(image).float(), torch.tensor(mask).float()\n",
      "smth:0.4196: 100%|██████████| 18/18 [19:27<00:00, 64.86s/it] \n",
      "smth:0.4255: 100%|██████████| 5/5 [06:28<00:00, 77.67s/it] \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "best th: 0.2 best dc: 0.15517211253976143\n",
      "Sun May 12 20:17:04 2024 Fold 3, Epoch 6, lr: 0.0001500, train loss: 0.41962, valid loss: 0.42545, metric: 0.155172.\n",
      "Sun May 12 20:17:04 2024 Epoch: 7\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/18 [00:00<?, ?it/s]/tmp/ipykernel_113/234965088.py:81: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
      "  image, mask = torch.tensor(image).float(), torch.tensor(mask).float()\n",
      "/tmp/ipykernel_113/234965088.py:81: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
      "  image, mask = torch.tensor(image).float(), torch.tensor(mask).float()\n",
      "/tmp/ipykernel_113/234965088.py:81: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
      "  image, mask = torch.tensor(image).float(), torch.tensor(mask).float()\n",
      "/tmp/ipykernel_113/234965088.py:81: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
      "  image, mask = torch.tensor(image).float(), torch.tensor(mask).float()\n",
      "smth:0.3895: 100%|██████████| 18/18 [19:44<00:00, 65.83s/it]\n",
      "smth:0.4089: 100%|██████████| 5/5 [06:06<00:00, 73.38s/it] \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "best th: 0.2 best dc: 0.18806653830422593\n",
      "Sun May 12 20:42:56 2024 Fold 3, Epoch 7, lr: 0.0001036, train loss: 0.38946, valid loss: 0.40891, metric: 0.188067.\n",
      "metric_best (0.168211 --> 0.188067). Saving model ...\n",
      "Sun May 12 20:42:57 2024 Epoch: 8\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/18 [00:00<?, ?it/s]/tmp/ipykernel_113/234965088.py:81: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
      "  image, mask = torch.tensor(image).float(), torch.tensor(mask).float()\n",
      "/tmp/ipykernel_113/234965088.py:81: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
      "  image, mask = torch.tensor(image).float(), torch.tensor(mask).float()\n",
      "/tmp/ipykernel_113/234965088.py:81: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
      "  image, mask = torch.tensor(image).float(), torch.tensor(mask).float()\n",
      "/tmp/ipykernel_113/234965088.py:81: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
      "  image, mask = torch.tensor(image).float(), torch.tensor(mask).float()\n",
      "smth:0.3635: 100%|██████████| 18/18 [21:50<00:00, 72.78s/it]  \n",
      "smth:0.3409: 100%|██████████| 5/5 [06:08<00:00, 73.80s/it] \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "best th: 0.2 best dc: 0.3460445902566429\n",
      "Sun May 12 21:10:56 2024 Fold 3, Epoch 8, lr: 0.0000618, train loss: 0.36352, valid loss: 0.34086, metric: 0.346045.\n",
      "metric_best (0.188067 --> 0.346045). Saving model ...\n",
      "Sun May 12 21:10:57 2024 Epoch: 9\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/18 [00:00<?, ?it/s]/tmp/ipykernel_113/234965088.py:81: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
      "  image, mask = torch.tensor(image).float(), torch.tensor(mask).float()\n",
      "/tmp/ipykernel_113/234965088.py:81: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
      "  image, mask = torch.tensor(image).float(), torch.tensor(mask).float()\n",
      "/tmp/ipykernel_113/234965088.py:81: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
      "  image, mask = torch.tensor(image).float(), torch.tensor(mask).float()\n",
      "/tmp/ipykernel_113/234965088.py:81: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
      "  image, mask = torch.tensor(image).float(), torch.tensor(mask).float()\n",
      "smth:0.3445: 100%|██████████| 18/18 [17:05<00:00, 56.96s/it]  \n",
      "smth:0.3298: 100%|██████████| 5/5 [06:19<00:00, 75.97s/it] \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "best th: 0.2 best dc: 0.3595240918955148\n",
      "Sun May 12 21:34:22 2024 Fold 3, Epoch 9, lr: 0.0000286, train loss: 0.34452, valid loss: 0.32979, metric: 0.359524.\n",
      "metric_best (0.346045 --> 0.359524). Saving model ...\n",
      "Sun May 12 21:34:23 2024 Epoch: 10\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/18 [00:00<?, ?it/s]/tmp/ipykernel_113/234965088.py:81: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
      "  image, mask = torch.tensor(image).float(), torch.tensor(mask).float()\n",
      "/tmp/ipykernel_113/234965088.py:81: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
      "  image, mask = torch.tensor(image).float(), torch.tensor(mask).float()\n",
      "/tmp/ipykernel_113/234965088.py:81: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
      "  image, mask = torch.tensor(image).float(), torch.tensor(mask).float()\n",
      "/tmp/ipykernel_113/234965088.py:81: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
      "  image, mask = torch.tensor(image).float(), torch.tensor(mask).float()\n",
      "smth:0.3407: 100%|██████████| 18/18 [18:12<00:00, 60.68s/it]\n",
      "smth:0.3247: 100%|██████████| 5/5 [04:47<00:00, 57.46s/it] \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "best th: 0.2 best dc: 0.377662267961722\n",
      "Sun May 12 21:57:22 2024 Fold 3, Epoch 10, lr: 0.0000073, train loss: 0.34068, valid loss: 0.32469, metric: 0.377662.\n",
      "metric_best (0.359524 --> 0.377662). Saving model ...\n"
     ]
    }
   ],
   "source": [
    "# run(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "14ccef4f-77cd-4f55-acf1-b4c74c02cb2f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "70 17\n",
      "Sun May 12 21:57:32 2024 Epoch: 1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/18 [00:00<?, ?it/s]/tmp/ipykernel_113/234965088.py:81: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
      "  image, mask = torch.tensor(image).float(), torch.tensor(mask).float()\n",
      "/tmp/ipykernel_113/234965088.py:81: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
      "  image, mask = torch.tensor(image).float(), torch.tensor(mask).float()\n",
      "/tmp/ipykernel_113/234965088.py:81: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
      "  image, mask = torch.tensor(image).float(), torch.tensor(mask).float()\n",
      "/tmp/ipykernel_113/234965088.py:81: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
      "  image, mask = torch.tensor(image).float(), torch.tensor(mask).float()\n",
      "smth:0.6823: 100%|██████████| 18/18 [18:29<00:00, 61.64s/it]\n",
      "smth:0.5619: 100%|██████████| 5/5 [05:29<00:00, 65.94s/it] \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "best th: 0.2 best dc: 0.00029409005849818785\n",
      "Sun May 12 22:21:31 2024 Fold 4, Epoch 1, lr: 0.0003000, train loss: 0.68231, valid loss: 0.56193, metric: 0.000294.\n",
      "metric_best (0.000000 --> 0.000294). Saving model ...\n",
      "Sun May 12 22:21:32 2024 Epoch: 2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/18 [00:00<?, ?it/s]/tmp/ipykernel_113/234965088.py:81: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
      "  image, mask = torch.tensor(image).float(), torch.tensor(mask).float()\n",
      "/tmp/ipykernel_113/234965088.py:81: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
      "  image, mask = torch.tensor(image).float(), torch.tensor(mask).float()\n",
      "/tmp/ipykernel_113/234965088.py:81: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
      "  image, mask = torch.tensor(image).float(), torch.tensor(mask).float()\n",
      "/tmp/ipykernel_113/234965088.py:81: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
      "  image, mask = torch.tensor(image).float(), torch.tensor(mask).float()\n",
      "smth:0.5417: 100%|██████████| 18/18 [17:51<00:00, 59.50s/it]\n",
      "smth:0.5227: 100%|██████████| 5/5 [05:16<00:00, 63.25s/it] \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "best th: 0.2 best dc: 0.014293260369594479\n",
      "Sun May 12 22:44:39 2024 Fold 4, Epoch 2, lr: 0.0002927, train loss: 0.54171, valid loss: 0.52273, metric: 0.014293.\n",
      "metric_best (0.000294 --> 0.014293). Saving model ...\n",
      "Sun May 12 22:44:40 2024 Epoch: 3\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/18 [00:00<?, ?it/s]/tmp/ipykernel_113/234965088.py:81: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
      "  image, mask = torch.tensor(image).float(), torch.tensor(mask).float()\n",
      "/tmp/ipykernel_113/234965088.py:81: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
      "  image, mask = torch.tensor(image).float(), torch.tensor(mask).float()\n",
      "/tmp/ipykernel_113/234965088.py:81: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
      "  image, mask = torch.tensor(image).float(), torch.tensor(mask).float()\n",
      "/tmp/ipykernel_113/234965088.py:81: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
      "  image, mask = torch.tensor(image).float(), torch.tensor(mask).float()\n",
      "smth:0.5092:  44%|████▍     | 8/18 [08:48<05:23, 32.33s/it]   "
     ]
    }
   ],
   "source": [
    "# run(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "421f0832-637f-409d-a45c-a50362ef84ca",
   "metadata": {},
   "outputs": [],
   "source": [
    "def run_last_epoch(fold):\n",
    "\n",
    "    log_file = os.path.join(log_dir, f'{kernel_type}.txt')\n",
    "    model_file = os.path.join(model_dir, f'{kernel_type}_fold{fold}_best.pth')\n",
    "\n",
    "    train_ = df_seg[df_seg['fold'] != fold].reset_index(drop=True)\n",
    "    valid_ = df_seg[df_seg['fold'] == fold].reset_index(drop=True)\n",
    "    dataset_train = SEGDataset(train_, 'train', transform=transforms_train)\n",
    "    dataset_valid = SEGDataset(valid_, 'valid', transform=transforms_valid)\n",
    "    loader_train = torch.utils.data.DataLoader(dataset_train, batch_size=batch_size, shuffle=True, num_workers=num_workers)\n",
    "    loader_valid = torch.utils.data.DataLoader(dataset_valid, batch_size=batch_size, shuffle=False, num_workers=num_workers)\n",
    "\n",
    "    model = TimmSegModel(backbone, pretrained=True)\n",
    "    model = convert_3d(model)\n",
    "    model = model.to(device)\n",
    "\n",
    "    optimizer = optim.AdamW(model.parameters(), lr=init_lr)\n",
    "    scaler = torch.cuda.amp.GradScaler()\n",
    "    from_epoch = 0\n",
    "    metric_best = 0.\n",
    "    loss_min = np.inf\n",
    "\n",
    "    # Load the saved model checkpoint\n",
    "    checkpoint = torch.load(model_file.replace('_best', '_last'))\n",
    "    model.load_state_dict(checkpoint['model_state_dict'])\n",
    "    optimizer.load_state_dict(checkpoint['optimizer_state_dict'])\n",
    "    if scaler:\n",
    "        scaler.load_state_dict(checkpoint['scaler_state_dict'])\n",
    "\n",
    "    scheduler_cosine = torch.optim.lr_scheduler.CosineAnnealingWarmRestarts(optimizer, n_epochs)\n",
    "\n",
    "    print(len(dataset_train), len(dataset_valid))\n",
    "\n",
    "    epoch = checkpoint['epoch']\n",
    "    print(time.ctime(), 'Last Epoch:', epoch)\n",
    "\n",
    "    # Run validation for the loaded model\n",
    "    valid_loss, metric = valid_func(model, loader_valid)\n",
    "\n",
    "    content = time.ctime() + ' ' + f'Fold {fold}, Epoch {epoch}, lr: {optimizer.param_groups[0][\"lr\"]:.7f}, valid loss: {valid_loss:.5f}, metric: {(metric):.6f}.'\n",
    "    print(content)\n",
    "    with open(log_file, 'a') as appender:\n",
    "        appender.write(content + '\\n')\n",
    "\n",
    "    # Save last model checkpoint if needed\n",
    "    if not DEBUG:\n",
    "        torch.save(\n",
    "            {\n",
    "                'epoch': epoch,\n",
    "                'model_state_dict': model.state_dict(),\n",
    "                'optimizer_state_dict': optimizer.state_dict(),\n",
    "                'scaler_state_dict': scaler.state_dict() if scaler else None,\n",
    "                'score_best': metric_best,\n",
    "            },\n",
    "            model_file.replace('_best', '_last')\n",
    "        )\n",
    "\n",
    "    del model\n",
    "    torch.cuda.empty_cache()\n",
    "    gc.collect()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "41eddda6-3eee-4f14-9133-d8e3d6f3545e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:timm.models._builder:Loading pretrained weights from Hugging Face hub (timm/resnet18d.ra2_in1k)\n",
      "INFO:timm.models._hub:[timm/resnet18d.ra2_in1k] Safe alternative available for 'pytorch_model.bin' (as 'model.safetensors'). Loading weights using safetensors.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "70 17\n",
      "Wed May 15 06:25:03 2024 Last Epoch: 9\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "smth:0.4466: 100%|██████████| 5/5 [26:43<00:00, 320.75s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "best th: 0.2 best dc: 0.17501567635846285\n",
      "Wed May 15 06:51:47 2024 Fold 2, Epoch 9, lr: 0.0003000, valid loss: 0.44663, metric: 0.175016.\n"
     ]
    }
   ],
   "source": [
    "run_last_epoch(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "97d3d461-52e1-4851-b871-46556f622d9c",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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