Segmentation_PyTorch_Unet++__ResNet34).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "background_save": true
    },
    "id": "jzOHPX0-aTdf"
   },
   "outputs": [],
   "source": [
    "import os, cv2\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import random, tqdm\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "from torch.utils.data import DataLoader\n",
    "from torchvision import transforms\n",
    "from torchsummary import summary\n",
    "\n",
    "import albumentations as album\n",
    "\n",
    "#!pip install -q -U segmentation-models-pytorch albumentations > /dev/null\n",
    "import segmentation_models_pytorch as smp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "2_c9vChPav3w",
    "outputId": "b8613e3f-30da-45f0-aff6-da1b153c84c7"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "len_train:  228\n",
      "len_valid:  66\n",
      "len:  66\n"
     ]
    }
   ],
   "source": [
    "#Defining train / val / test directories\n",
    "DATA_DIR = 'Three_Phases_Segmentation/datasets/'\n",
    "\n",
    "x_train_dir = os.path.join(DATA_DIR, 'train')\n",
    "y_train_dir = os.path.join(DATA_DIR, 'train_labels')\n",
    "\n",
    "list = os.listdir(y_train_dir) # dir is your directory path\n",
    "number_files = len(list)\n",
    "print('len_train: ',number_files)\n",
    "\n",
    "x_valid_dir = os.path.join(DATA_DIR, 'validation')\n",
    "y_valid_dir = os.path.join(DATA_DIR, 'validation_labels')\n",
    "\n",
    "list = os.listdir(y_valid_dir) # dir is your directory path\n",
    "number_files = len(list)\n",
    "print('len_valid: ',number_files)\n",
    "\n",
    "x_test_dir = os.path.join(DATA_DIR, 'test')\n",
    "y_test_dir = os.path.join(DATA_DIR, 'test_labels')\n",
    "\n",
    "list = os.listdir(y_test_dir) # dir is your directory path\n",
    "number_files = len(list)\n",
    "print('len: ',number_files)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "R8oECAlZa5SQ",
    "outputId": "7be60ef7-0a6c-4529-a857-cfe360e48aaf"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "All dataset classes and their corresponding RGB values in labels:\n",
      "Class Names:  ['pore', 'grain', 'water', 'air-grain', 'air-water', 'grain-water']\n",
      "Class RGB values:  [[255, 255, 255], [0, 0, 0], [255, 0, 0], [0, 0, 255], [0, 255, 0], [255, 255, 0]]\n"
     ]
    }
   ],
   "source": [
    "#All dataset classes and their corresponding RGB values in labels\n",
    "class_dict = pd.read_csv(\"Three_Phases_Segmentation/label_class_dict.csv\")\n",
    "# Get class names\n",
    "select_classes = class_dict['name'].tolist()\n",
    "# Get class RGB values\n",
    "select_class_rgb_values = class_dict[['r','g','b']].values.tolist()\n",
    "\n",
    "print('All dataset classes and their corresponding RGB values in labels:')\n",
    "print('Class Names: ', select_classes)\n",
    "print('Class RGB values: ', select_class_rgb_values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "TKjSrfScbEKD"
   },
   "outputs": [],
   "source": [
    "#Helper functions for one-hot encoding/decoding\n",
    "# Perform one hot encoding on label\n",
    "def one_hot_encode(label, label_values):\n",
    "    \"\"\"\n",
    "    Convert a segmentation image label array to one-hot format by replacing each pixel value with a vector of length num_classes\n",
    "    # Arguments\n",
    "        label: The 2D array segmentation image label\n",
    "        label_values\n",
    "        \n",
    "    # Returns\n",
    "        A 2D array with the same width and hieght as the input, but with a depth size of num_classes\n",
    "    \"\"\"\n",
    "    semantic_map = []\n",
    "    for colour in label_values:\n",
    "        equality = np.equal(label, colour)\n",
    "        class_map = np.all(equality, axis = -1)\n",
    "        semantic_map.append(class_map)\n",
    "    semantic_map = np.stack(semantic_map, axis=-1)\n",
    "\n",
    "    return semantic_map\n",
    "    \n",
    "# Perform reverse one-hot-encoding on labels / preds\n",
    "def reverse_one_hot(image):\n",
    "    \"\"\"\n",
    "    Transform a 2D array in one-hot format (depth is num_classes),\n",
    "    to a 2D array with only 1 channel, where each pixel value is\n",
    "    the classified class key.\n",
    "    # Arguments\n",
    "        image: The one-hot format image \n",
    "        \n",
    "    # Returns\n",
    "        A 2D array with the same width and hieght as the input, but\n",
    "        with a depth size of 1, where each pixel value is the classified \n",
    "        class key.\n",
    "    \"\"\"\n",
    "    x = np.argmax(image, axis = -1)\n",
    "    return x\n",
    "\n",
    "# Perform colour coding on the reverse-one-hot outputs\n",
    "def colour_code_segmentation(image, label_values):\n",
    "    \"\"\"\n",
    "    Given a 1-channel array of class keys, colour code the segmentation results.\n",
    "    # Arguments\n",
    "        image: single channel array where each value represents the class key.\n",
    "        label_values\n",
    "\n",
    "    # Returns\n",
    "        Colour coded image for segmentation visualization\n",
    "    \"\"\"\n",
    "    colour_codes = np.array(label_values)\n",
    "    x = colour_codes[image.astype(int)]\n",
    "\n",
    "    return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "xrQnidaabS2E"
   },
   "outputs": [],
   "source": [
    "class BuildingsDataset(torch.utils.data.Dataset):\n",
    "\n",
    "    \"\"\"Read images, apply augmentation and preprocessing transformations.\n",
    "    \n",
    "    Args:\n",
    "        images_dir (str): path to images folder\n",
    "        masks_dir (str): path to segmentation masks folder\n",
    "        class_rgb_values (list): RGB values of select classes to extract from segmentation mask\n",
    "        augmentation (albumentations.Compose): data transfromation pipeline \n",
    "            (e.g. flip, scale, etc.)\n",
    "        preprocessing (albumentations.Compose): data preprocessing \n",
    "            (e.g. noralization, shape manipulation, etc.)\n",
    "    \n",
    "    \"\"\"\n",
    "    \n",
    "    def __init__(\n",
    "            self, \n",
    "            images_dir, \n",
    "            masks_dir, \n",
    "            class_rgb_values=None, \n",
    "            augmentation=None, \n",
    "    ):\n",
    "        \n",
    "        self.image_paths = [os.path.join(images_dir, image_id) for image_id in sorted(os.listdir(images_dir))]\n",
    "        self.mask_paths = [os.path.join(masks_dir, image_id) for image_id in sorted(os.listdir(masks_dir))]\n",
    "\n",
    "        self.class_rgb_values = class_rgb_values\n",
    "        \n",
    "        self.lr_transform = transforms.Compose([transforms.ToTensor()])\n",
    "                    \n",
    "        self.augmentation = augmentation\n",
    "    \n",
    "    def __getitem__(self, i):\n",
    "        \n",
    "        # read images and masks\n",
    "        image = cv2.cvtColor(cv2.imread(self.image_paths[i]), cv2.COLOR_BGR2RGB)\n",
    "        mask = cv2.cvtColor(cv2.imread(self.mask_paths[i]), cv2.COLOR_BGR2RGB)\n",
    "        \n",
    "        # one-hot-encode the mask\n",
    "        mask = one_hot_encode(mask, self.class_rgb_values).astype('float')\n",
    "        \n",
    "        # apply augmentations\n",
    "        if self.augmentation:\n",
    "            sample = self.augmentation(image=image, mask=mask)\n",
    "            image, mask = sample['image'], sample['mask']\n",
    "        \n",
    "        # apply preprocessing\n",
    "        return self.lr_transform(image), self.lr_transform(mask)\n",
    "        \n",
    "    def __len__(self):\n",
    "        # return length of \n",
    "        return len(self.image_paths)\n",
    "    \n",
    "# Defining Augmentations\n",
    "def get_training_augmentation():\n",
    "    train_transform = [    \n",
    "        album.RandomCrop(height=128, width=128, always_apply=True),\n",
    "        album.OneOf([album.HorizontalFlip(p=1),\n",
    "                     album.VerticalFlip(p=1),\n",
    "                     album.RandomRotate90(p=1)], p=0.5)]\n",
    "    return album.Compose(train_transform)\n",
    "\n",
    "\n",
    "def get_validation_augmentation():   \n",
    "    test_transform = [\n",
    "        album.RandomCrop(height=128, width=128, always_apply=True)]\n",
    "    return album.Compose(test_transform)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "len_train_dataset:  228\n",
      "len_valid_dataset:  66\n"
     ]
    }
   ],
   "source": [
    "# Get train and val dataset instances\n",
    "train_dataset = BuildingsDataset(\n",
    "    x_train_dir, y_train_dir, \n",
    "    augmentation= get_training_augmentation(),\n",
    "    class_rgb_values=select_class_rgb_values) \n",
    "\n",
    "print('len_train_dataset: ',len(train_dataset))\n",
    "\n",
    "valid_dataset = BuildingsDataset(\n",
    "    x_valid_dir, y_valid_dir, \n",
    "    augmentation= get_validation_augmentation(), \n",
    "    class_rgb_values=select_class_rgb_values)\n",
    "\n",
    "print('len_valid_dataset: ',len(valid_dataset))\n",
    "\n",
    "# Get train and val data loaders\n",
    "train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True, drop_last=True)\n",
    "valid_loader = DataLoader(valid_dataset, batch_size=16, shuffle=False, drop_last=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "lPJCYpTuboNI"
   },
   "outputs": [],
   "source": [
    "#Model Definition\n",
    "ENCODER = 'resnet34'\n",
    "ENCODER_WEIGHTS = 'imagenet'\n",
    "ENCODER_DEPTH=5\n",
    "CLASSES = select_classes\n",
    "ACTIVATION = 'softmax2d' #'sigmoid' # could be None for logits or 'softmax2d' for multiclass segmentation\n",
    "\n",
    "# create segmentation model with pretrained encoder\n",
    "model = smp.UnetPlusPlus(\n",
    "    encoder_name=ENCODER, \n",
    "    encoder_depth=ENCODER_DEPTH,\n",
    "    encoder_weights=ENCODER_WEIGHTS, \n",
    "    decoder_use_batchnorm=True,\n",
    "    decoder_channels=(256, 128, 64, 32, 16),#same legnth as encoder_depth\n",
    "    in_channels=3,#rgb\n",
    "    classes=len(CLASSES), \n",
    "    activation=ACTIVATION,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "XqAQQRHzby-J"
   },
   "outputs": [],
   "source": [
    "# Set Hyperparams\n",
    "# Set flag to train the model or not. If set to 'False', only prediction is performed (using an older model checkpoint)\n",
    "TRAINING = True\n",
    "\n",
    "# Set num of epochs\n",
    "EPOCHS = 500\n",
    "\n",
    "# Set device: `cuda` or `cpu`\n",
    "DEVICE = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "\n",
    "# define loss function\n",
    "#loss = smp.utils.losses.constants.MULTICLASS_MODE()\n",
    "loss = smp.utils.losses.DiceLoss()\n",
    "\n",
    "# define metrics\n",
    "metrics = [\n",
    "    smp.utils.metrics.IoU(threshold=0.5)]\n",
    "\n",
    "# define optimizer\n",
    "optimizer = torch.optim.Adam([ \n",
    "    dict(params=model.parameters(), lr=1e-5, weight_decay=0.0001)])#\n",
    "\n",
    "# define learning rate scheduler \n",
    "\"\"\"\n",
    "lmbda = lambda epoch: 0.947 #0.65** epoch\n",
    "lr_scheduler = torch.optim.lr_scheduler.MultiplicativeLR(optimizer, lr_lambda=lmbda)\n",
    "\"\"\"\n",
    "\n",
    "# load best saved model checkpoint from previous commit (if present)\n",
    "if os.path.exists('best_model_previous_run/best_model.pth'):\n",
    "    model = torch.load('best_model_previous_run/best_model.pth', map_location=DEVICE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "Fi_JZgBPb2mm"
   },
   "outputs": [],
   "source": [
    "train_epoch = smp.utils.train.TrainEpoch(\n",
    "    model, \n",
    "    loss=loss, \n",
    "    metrics=metrics, \n",
    "    optimizer=optimizer,\n",
    "    device=DEVICE,\n",
    "    verbose=True,\n",
    ")\n",
    "\n",
    "valid_epoch = smp.utils.train.ValidEpoch(\n",
    "    model, \n",
    "    loss=loss, \n",
    "    metrics=metrics, \n",
    "    device=DEVICE,\n",
    "    verbose=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "background_save": true,
     "base_uri": "https://localhost:8080/"
    },
    "id": "0iwRPItgb7hW",
    "outputId": "35141b90-679f-47ff-c7cc-86fe929e49c3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch: 0\n",
      "train: 100%|████████████████████████████████████| 7/7 [00:05<00:00,  1.39it/s, dice_loss - 0.8736, iou_score - 0.01011]\n",
      "valid: 100%|███████████████████████████████████| 4/4 [00:00<00:00, 10.32it/s, dice_loss - 0.852, iou_score - 1.923e-06]\n",
      "Model saved!\n",
      "\n",
      "Epoch: 1\n",
      "train: 100%|████████████████████████████████████| 7/7 [00:02<00:00,  2.74it/s, dice_loss - 0.8622, iou_score - 0.01505]\n",
      "valid: 100%|███████████████████████████████████| 4/4 [00:00<00:00, 10.45it/s, dice_loss - 0.8584, iou_score - 0.001989]\n",
      "Model saved!\n",
      "\n",
      "Epoch: 2\n",
      "train: 100%|████████████████████████████████████| 7/7 [00:02<00:00,  2.75it/s, dice_loss - 0.8496, iou_score - 0.02374]\n",
      "valid: 100%|████████████████████████████████████| 4/4 [00:00<00:00, 10.36it/s, dice_loss - 0.8601, iou_score - 0.01057]\n",
      "Model saved!\n",
      "\n",
      "Epoch: 3\n",
      "train: 100%|████████████████████████████████████| 7/7 [00:02<00:00,  2.71it/s, dice_loss - 0.8391, iou_score - 0.03508]\n",
      "valid: 100%|████████████████████████████████████| 4/4 [00:00<00:00, 10.31it/s, dice_loss - 0.8589, iou_score - 0.01721]\n",
      "Model saved!\n",
      "\n",
      "Epoch: 4\n",
      "train: 100%|██████████████████████████████████████| 7/7 [00:02<00:00,  2.71it/s, dice_loss - 0.8297, iou_score - 0.049]\n",
      "valid: 100%|████████████████████████████████████| 4/4 [00:00<00:00,  9.70it/s, dice_loss - 0.8528, iou_score - 0.02689]\n",
      "Model saved!\n",
      "\n",
      "Epoch: 5\n",
      "train: 100%|████████████████████████████████████| 7/7 [00:02<00:00,  2.70it/s, dice_loss - 0.8218, iou_score - 0.06318]\n",
      "valid: 100%|████████████████████████████████████| 4/4 [00:00<00:00,  9.72it/s, dice_loss - 0.8491, iou_score - 0.03369]\n",
      "Model saved!\n",
      "\n",
      "Epoch: 6\n",
      "train: 100%|████████████████████████████████████| 7/7 [00:02<00:00,  2.70it/s, dice_loss - 0.8127, iou_score - 0.08043]\n",
      "valid: 100%|████████████████████████████████████| 4/4 [00:00<00:00,  9.86it/s, dice_loss - 0.8394, iou_score - 0.04817]\n",
      "Model saved!\n",
      "\n",
      "Epoch: 7\n",
      "train: 100%|████████████████████████████████████| 7/7 [00:02<00:00,  2.64it/s, dice_loss - 0.8071, iou_score - 0.09419]\n",
      "valid: 100%|████████████████████████████████████| 4/4 [00:00<00:00, 10.09it/s, dice_loss - 0.8311, iou_score - 0.06037]\n",
      "Model saved!\n",
      "\n",
      "Epoch: 8\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.68it/s, dice_loss - 0.8004, iou_score - 0.1069]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  9.72it/s, dice_loss - 0.8226, iou_score - 0.0735]\n",
      "Model saved!\n",
      "\n",
      "Epoch: 9\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.66it/s, dice_loss - 0.7943, iou_score - 0.1179]\n",
      "valid: 100%|████████████████████████████████████| 4/4 [00:00<00:00,  9.69it/s, dice_loss - 0.8158, iou_score - 0.08141]\n",
      "Model saved!\n",
      "\n",
      "Epoch: 10\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.65it/s, dice_loss - 0.7884, iou_score - 0.1269]\n",
      "valid: 100%|████████████████████████████████████| 4/4 [00:00<00:00,  9.99it/s, dice_loss - 0.8065, iou_score - 0.09421]\n",
      "Model saved!\n",
      "\n",
      "Epoch: 11\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.62it/s, dice_loss - 0.7814, iou_score - 0.1352]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  9.56it/s, dice_loss - 0.8015, iou_score - 0.1009]\n",
      "Model saved!\n",
      "\n",
      "Epoch: 12\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.65it/s, dice_loss - 0.7755, iou_score - 0.1412]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  9.67it/s, dice_loss - 0.7937, iou_score - 0.1085]\n",
      "Model saved!\n",
      "\n",
      "Epoch: 13\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.67it/s, dice_loss - 0.7696, iou_score - 0.1463]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  9.26it/s, dice_loss - 0.7894, iou_score - 0.1108]\n",
      "Model saved!\n",
      "\n",
      "Epoch: 14\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.60it/s, dice_loss - 0.7653, iou_score - 0.1493]\n",
      "valid: 100%|██████████████████████████████████████| 4/4 [00:00<00:00,  9.69it/s, dice_loss - 0.786, iou_score - 0.1128]\n",
      "Model saved!\n",
      "\n",
      "Epoch: 15\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.59it/s, dice_loss - 0.7545, iou_score - 0.1594]\n",
      "valid: 100%|██████████████████████████████████████| 4/4 [00:00<00:00,  9.47it/s, dice_loss - 0.776, iou_score - 0.1214]\n",
      "Model saved!\n",
      "\n",
      "Epoch: 16\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.64it/s, dice_loss - 0.7501, iou_score - 0.1607]\n",
      "valid: 100%|███████████████████████████████████████| 4/4 [00:00<00:00,  9.69it/s, dice_loss - 0.7739, iou_score - 0.12]\n",
      "\n",
      "Epoch: 17\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.62it/s, dice_loss - 0.7456, iou_score - 0.1626]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  9.67it/s, dice_loss - 0.7629, iou_score - 0.1304]\n",
      "Model saved!\n",
      "\n",
      "Epoch: 18\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.59it/s, dice_loss - 0.7369, iou_score - 0.1692]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  9.74it/s, dice_loss - 0.7593, iou_score - 0.1307]\n",
      "Model saved!\n",
      "\n",
      "Epoch: 19\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.64it/s, dice_loss - 0.7323, iou_score - 0.1706]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.84it/s, dice_loss - 0.7511, iou_score - 0.1372]\n",
      "Model saved!\n",
      "\n",
      "Epoch: 20\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.62it/s, dice_loss - 0.7252, iou_score - 0.1753]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  9.25it/s, dice_loss - 0.7449, iou_score - 0.1414]\n",
      "Model saved!\n",
      "\n",
      "Epoch: 21\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.62it/s, dice_loss - 0.7184, iou_score - 0.1799]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  9.44it/s, dice_loss - 0.7337, iou_score - 0.1492]\n",
      "Model saved!\n",
      "\n",
      "Epoch: 22\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.59it/s, dice_loss - 0.7116, iou_score - 0.1842]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  9.41it/s, dice_loss - 0.7285, iou_score - 0.1502]\n",
      "Model saved!\n",
      "\n",
      "Epoch: 23\n",
      "train: 100%|██████████████████████████████████████| 7/7 [00:02<00:00,  2.63it/s, dice_loss - 0.708, iou_score - 0.1846]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  9.75it/s, dice_loss - 0.7182, iou_score - 0.1613]\n",
      "Model saved!\n",
      "\n",
      "Epoch: 24\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.59it/s, dice_loss - 0.7003, iou_score - 0.1903]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  9.49it/s, dice_loss - 0.7121, iou_score - 0.1638]\n",
      "Model saved!\n",
      "\n",
      "Epoch: 25\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.59it/s, dice_loss - 0.6975, iou_score - 0.1897]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  9.52it/s, dice_loss - 0.7029, iou_score - 0.1714]\n",
      "Model saved!\n",
      "\n",
      "Epoch: 26\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.60it/s, dice_loss - 0.6911, iou_score - 0.1937]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  9.32it/s, dice_loss - 0.7028, iou_score - 0.1686]\n",
      "\n",
      "Epoch: 27\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.62it/s, dice_loss - 0.6854, iou_score - 0.1976]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  9.61it/s, dice_loss - 0.6939, iou_score - 0.1754]\n",
      "Model saved!\n",
      "\n",
      "Epoch: 28\n",
      "train: 100%|████████████████████████████████████████| 7/7 [00:02<00:00,  2.64it/s, dice_loss - 0.6809, iou_score - 0.2]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  9.01it/s, dice_loss - 0.6932, iou_score - 0.1722]\n",
      "\n",
      "Epoch: 29\n",
      "train: 100%|██████████████████████████████████████| 7/7 [00:02<00:00,  2.64it/s, dice_loss - 0.679, iou_score - 0.1983]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  9.58it/s, dice_loss - 0.6848, iou_score - 0.1796]\n",
      "Model saved!\n",
      "\n",
      "Epoch: 30\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.66it/s, dice_loss - 0.6742, iou_score - 0.2021]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  9.51it/s, dice_loss - 0.6798, iou_score - 0.1821]\n",
      "Model saved!\n",
      "\n",
      "Epoch: 31\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.61it/s, dice_loss - 0.6691, iou_score - 0.2049]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  9.57it/s, dice_loss - 0.6758, iou_score - 0.1858]\n",
      "Model saved!\n",
      "\n",
      "Epoch: 32\n",
      "train: 100%|██████████████████████████████████████| 7/7 [00:02<00:00,  2.45it/s, dice_loss - 0.665, iou_score - 0.2062]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.68it/s, dice_loss - 0.6774, iou_score - 0.1815]\n",
      "\n",
      "Epoch: 33\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.54it/s, dice_loss - 0.6634, iou_score - 0.2058]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  9.63it/s, dice_loss - 0.6671, iou_score - 0.1885]\n",
      "Model saved!\n",
      "\n",
      "Epoch: 34\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.58it/s, dice_loss - 0.6607, iou_score - 0.2059]\n",
      "valid: 100%|██████████████████████████████████████| 4/4 [00:00<00:00,  9.27it/s, dice_loss - 0.6682, iou_score - 0.186]\n",
      "\n",
      "Epoch: 35\n",
      "train: 100%|██████████████████████████████████████| 7/7 [00:02<00:00,  2.51it/s, dice_loss - 0.655, iou_score - 0.2102]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.24it/s, dice_loss - 0.6631, iou_score - 0.1887]\n",
      "Model saved!\n",
      "\n",
      "Epoch: 36\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.55it/s, dice_loss - 0.6525, iou_score - 0.2109]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  9.81it/s, dice_loss - 0.6625, iou_score - 0.1894]\n",
      "Model saved!\n",
      "\n",
      "Epoch: 37\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.59it/s, dice_loss - 0.6504, iou_score - 0.2107]\n",
      "valid: 100%|██████████████████████████████████████| 4/4 [00:00<00:00,  9.32it/s, dice_loss - 0.6541, iou_score - 0.196]\n",
      "Model saved!\n",
      "\n",
      "Epoch: 38\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.57it/s, dice_loss - 0.6472, iou_score - 0.2124]\n",
      "valid: 100%|██████████████████████████████████████| 4/4 [00:00<00:00,  9.31it/s, dice_loss - 0.6517, iou_score - 0.197]\n",
      "Model saved!\n",
      "\n",
      "Epoch: 39\n",
      "train: 100%|██████████████████████████████████████| 7/7 [00:02<00:00,  2.58it/s, dice_loss - 0.6458, iou_score - 0.212]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  9.79it/s, dice_loss - 0.6488, iou_score - 0.1971]\n",
      "Model saved!\n",
      "\n",
      "Epoch: 40\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.62it/s, dice_loss - 0.6403, iou_score - 0.2154]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  9.61it/s, dice_loss - 0.6501, iou_score - 0.1942]\n",
      "\n",
      "Epoch: 41\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.53it/s, dice_loss - 0.6388, iou_score - 0.2155]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  9.55it/s, dice_loss - 0.6497, iou_score - 0.1937]\n",
      "\n",
      "Epoch: 42\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.61it/s, dice_loss - 0.6359, iou_score - 0.2171]\n",
      "valid: 100%|██████████████████████████████████████| 4/4 [00:00<00:00,  9.21it/s, dice_loss - 0.643, iou_score - 0.1978]\n",
      "Model saved!\n",
      "\n",
      "Epoch: 43\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.63it/s, dice_loss - 0.6327, iou_score - 0.2185]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  9.16it/s, dice_loss - 0.6404, iou_score - 0.1986]\n",
      "Model saved!\n",
      "\n",
      "Epoch: 44\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.57it/s, dice_loss - 0.6304, iou_score - 0.2193]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  9.55it/s, dice_loss - 0.6399, iou_score - 0.1984]\n",
      "\n",
      "Epoch: 45\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.60it/s, dice_loss - 0.6307, iou_score - 0.2171]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  9.56it/s, dice_loss - 0.6342, iou_score - 0.2006]\n",
      "Model saved!\n",
      "\n",
      "Epoch: 46\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.63it/s, dice_loss - 0.6244, iou_score - 0.2217]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  9.16it/s, dice_loss - 0.6402, iou_score - 0.1932]\n",
      "\n",
      "Epoch: 47\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.58it/s, dice_loss - 0.6211, iou_score - 0.2251]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.81it/s, dice_loss - 0.6313, iou_score - 0.2022]\n",
      "Model saved!\n",
      "\n",
      "Epoch: 48\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.62it/s, dice_loss - 0.6214, iou_score - 0.2222]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  9.44it/s, dice_loss - 0.6331, iou_score - 0.1974]\n",
      "\n",
      "Epoch: 49\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.62it/s, dice_loss - 0.6188, iou_score - 0.2231]\n",
      "valid: 100%|██████████████████████████████████████| 4/4 [00:00<00:00,  9.51it/s, dice_loss - 0.6287, iou_score - 0.201]\n",
      "\n",
      "Epoch: 50\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.67it/s, dice_loss - 0.6179, iou_score - 0.2223]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  9.40it/s, dice_loss - 0.6219, iou_score - 0.2085]\n",
      "Model saved!\n",
      "\n",
      "Epoch: 51\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.57it/s, dice_loss - 0.6158, iou_score - 0.2229]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  9.02it/s, dice_loss - 0.6249, iou_score - 0.2026]\n",
      "\n",
      "Epoch: 52\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.65it/s, dice_loss - 0.6111, iou_score - 0.2276]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  9.98it/s, dice_loss - 0.6176, iou_score - 0.2102]\n",
      "Model saved!\n",
      "\n",
      "Epoch: 53\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.58it/s, dice_loss - 0.6105, iou_score - 0.2278]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  9.90it/s, dice_loss - 0.6208, iou_score - 0.2074]\n",
      "\n",
      "Epoch: 54\n",
      "train: 100%|██████████████████████████████████████| 7/7 [00:02<00:00,  2.62it/s, dice_loss - 0.6088, iou_score - 0.227]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.76it/s, dice_loss - 0.6174, iou_score - 0.2075]\n",
      "\n",
      "Epoch: 55\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.61it/s, dice_loss - 0.6056, iou_score - 0.2287]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  9.36it/s, dice_loss - 0.6183, iou_score - 0.2042]\n",
      "\n",
      "Epoch: 56\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.56it/s, dice_loss - 0.6037, iou_score - 0.2299]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  9.32it/s, dice_loss - 0.6161, iou_score - 0.2073]\n",
      "\n",
      "Epoch: 57\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.61it/s, dice_loss - 0.6001, iou_score - 0.2336]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.77it/s, dice_loss - 0.6126, iou_score - 0.2097]\n",
      "\n",
      "Epoch: 58\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.65it/s, dice_loss - 0.5994, iou_score - 0.2324]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  9.83it/s, dice_loss - 0.6101, iou_score - 0.2092]\n",
      "\n",
      "Epoch: 59\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.42it/s, dice_loss - 0.5969, iou_score - 0.2347]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.51it/s, dice_loss - 0.6107, iou_score - 0.2088]\n",
      "\n",
      "Epoch: 60\n",
      "train: 100%|██████████████████████████████████████| 7/7 [00:02<00:00,  2.41it/s, dice_loss - 0.594, iou_score - 0.2378]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.45it/s, dice_loss - 0.6066, iou_score - 0.2074]\n",
      "\n",
      "Epoch: 61\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.48it/s, dice_loss - 0.5936, iou_score - 0.2363]\n",
      "valid: 100%|██████████████████████████████████████| 4/4 [00:00<00:00,  8.74it/s, dice_loss - 0.604, iou_score - 0.2143]\n",
      "Model saved!\n",
      "\n",
      "Epoch: 62\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.51it/s, dice_loss - 0.5914, iou_score - 0.2393]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.79it/s, dice_loss - 0.6037, iou_score - 0.2158]\n",
      "Model saved!\n",
      "\n",
      "Epoch: 63\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.51it/s, dice_loss - 0.5894, iou_score - 0.2405]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.62it/s, dice_loss - 0.6036, iou_score - 0.2141]\n",
      "\n",
      "Epoch: 64\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.52it/s, dice_loss - 0.5848, iou_score - 0.2465]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.78it/s, dice_loss - 0.5988, iou_score - 0.2183]\n",
      "Model saved!\n",
      "\n",
      "Epoch: 65\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.52it/s, dice_loss - 0.5866, iou_score - 0.2445]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.53it/s, dice_loss - 0.5954, iou_score - 0.2224]\n",
      "Model saved!\n",
      "\n",
      "Epoch: 66\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.50it/s, dice_loss - 0.5829, iou_score - 0.2479]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  9.05it/s, dice_loss - 0.5951, iou_score - 0.2234]\n",
      "Model saved!\n",
      "\n",
      "Epoch: 67\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.48it/s, dice_loss - 0.5837, iou_score - 0.2476]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.61it/s, dice_loss - 0.5936, iou_score - 0.2263]\n",
      "Model saved!\n",
      "\n",
      "Epoch: 68\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.51it/s, dice_loss - 0.5797, iou_score - 0.2527]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.59it/s, dice_loss - 0.5962, iou_score - 0.2256]\n",
      "\n",
      "Epoch: 69\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.51it/s, dice_loss - 0.5768, iou_score - 0.2595]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.38it/s, dice_loss - 0.5945, iou_score - 0.2256]\n",
      "\n",
      "Epoch: 70\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.51it/s, dice_loss - 0.5752, iou_score - 0.2597]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.79it/s, dice_loss - 0.5927, iou_score - 0.2313]\n",
      "Model saved!\n",
      "\n",
      "Epoch: 71\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.48it/s, dice_loss - 0.5741, iou_score - 0.2608]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.85it/s, dice_loss - 0.5898, iou_score - 0.2339]\n",
      "Model saved!\n",
      "\n",
      "Epoch: 72\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.50it/s, dice_loss - 0.5724, iou_score - 0.2674]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.33it/s, dice_loss - 0.5944, iou_score - 0.2312]\n",
      "\n",
      "Epoch: 73\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.52it/s, dice_loss - 0.5681, iou_score - 0.2723]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.35it/s, dice_loss - 0.5853, iou_score - 0.2426]\n",
      "Model saved!\n",
      "\n",
      "Epoch: 74\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.49it/s, dice_loss - 0.5692, iou_score - 0.2746]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.30it/s, dice_loss - 0.5835, iou_score - 0.2504]\n",
      "Model saved!\n",
      "\n",
      "Epoch: 75\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.49it/s, dice_loss - 0.5645, iou_score - 0.2806]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.70it/s, dice_loss - 0.5808, iou_score - 0.2531]\n",
      "Model saved!\n",
      "\n",
      "Epoch: 76\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.50it/s, dice_loss - 0.5645, iou_score - 0.2814]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.37it/s, dice_loss - 0.5874, iou_score - 0.2469]\n",
      "\n",
      "Epoch: 77\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.49it/s, dice_loss - 0.5626, iou_score - 0.2858]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.79it/s, dice_loss - 0.5805, iou_score - 0.2551]\n",
      "Model saved!\n",
      "\n",
      "Epoch: 78\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.48it/s, dice_loss - 0.5614, iou_score - 0.2883]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.72it/s, dice_loss - 0.5765, iou_score - 0.2629]\n",
      "Model saved!\n",
      "\n",
      "Epoch: 79\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.52it/s, dice_loss - 0.5585, iou_score - 0.2959]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.53it/s, dice_loss - 0.5735, iou_score - 0.2676]\n",
      "Model saved!\n",
      "\n",
      "Epoch: 80\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.48it/s, dice_loss - 0.5586, iou_score - 0.2985]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.79it/s, dice_loss - 0.5764, iou_score - 0.2686]\n",
      "Model saved!\n",
      "\n",
      "Epoch: 81\n",
      "train: 100%|██████████████████████████████████████| 7/7 [00:02<00:00,  2.49it/s, dice_loss - 0.5564, iou_score - 0.302]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.63it/s, dice_loss - 0.5715, iou_score - 0.2754]\n",
      "Model saved!\n",
      "\n",
      "Epoch: 82\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.52it/s, dice_loss - 0.5549, iou_score - 0.3052]\n",
      "valid: 100%|██████████████████████████████████████| 4/4 [00:00<00:00,  8.85it/s, dice_loss - 0.574, iou_score - 0.2753]\n",
      "\n",
      "Epoch: 83\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.43it/s, dice_loss - 0.5493, iou_score - 0.3173]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.83it/s, dice_loss - 0.5713, iou_score - 0.2824]\n",
      "Model saved!\n",
      "\n",
      "Epoch: 84\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.44it/s, dice_loss - 0.5521, iou_score - 0.3142]\n",
      "valid: 100%|██████████████████████████████████████| 4/4 [00:00<00:00,  8.57it/s, dice_loss - 0.5679, iou_score - 0.289]\n",
      "Model saved!\n",
      "\n",
      "Epoch: 85\n",
      "train: 100%|███████████████████████████████████████| 7/7 [00:02<00:00,  2.40it/s, dice_loss - 0.55, iou_score - 0.3215]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.45it/s, dice_loss - 0.5699, iou_score - 0.2902]\n",
      "Model saved!\n",
      "\n",
      "Epoch: 86\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.49it/s, dice_loss - 0.5458, iou_score - 0.3316]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.64it/s, dice_loss - 0.5666, iou_score - 0.2987]\n",
      "Model saved!\n",
      "\n",
      "Epoch: 87\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.51it/s, dice_loss - 0.5441, iou_score - 0.3355]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.42it/s, dice_loss - 0.5683, iou_score - 0.3018]\n",
      "Model saved!\n",
      "\n",
      "Epoch: 88\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.49it/s, dice_loss - 0.5425, iou_score - 0.3402]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.56it/s, dice_loss - 0.5622, iou_score - 0.3116]\n",
      "Model saved!\n",
      "\n",
      "Epoch: 89\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.47it/s, dice_loss - 0.5415, iou_score - 0.3419]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.59it/s, dice_loss - 0.5643, iou_score - 0.3144]\n",
      "Model saved!\n",
      "\n",
      "Epoch: 90\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.46it/s, dice_loss - 0.5403, iou_score - 0.3483]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.82it/s, dice_loss - 0.5628, iou_score - 0.3144]\n",
      "Model saved!\n",
      "\n",
      "Epoch: 91\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.49it/s, dice_loss - 0.5398, iou_score - 0.3514]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.79it/s, dice_loss - 0.5607, iou_score - 0.3233]\n",
      "Model saved!\n",
      "\n",
      "Epoch: 92\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.48it/s, dice_loss - 0.5358, iou_score - 0.3623]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.62it/s, dice_loss - 0.5628, iou_score - 0.3261]\n",
      "Model saved!\n",
      "\n",
      "Epoch: 93\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.53it/s, dice_loss - 0.5336, iou_score - 0.3674]\n",
      "valid: 100%|███████████████████████████████████████| 4/4 [00:00<00:00,  8.29it/s, dice_loss - 0.5544, iou_score - 0.34]\n",
      "Model saved!\n",
      "\n",
      "Epoch: 94\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.48it/s, dice_loss - 0.5352, iou_score - 0.3709]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.50it/s, dice_loss - 0.5552, iou_score - 0.3479]\n",
      "Model saved!\n",
      "\n",
      "Epoch: 95\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.49it/s, dice_loss - 0.5321, iou_score - 0.3823]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.61it/s, dice_loss - 0.5464, iou_score - 0.3625]\n",
      "Model saved!\n",
      "\n",
      "Epoch: 96\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.52it/s, dice_loss - 0.5309, iou_score - 0.3877]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.43it/s, dice_loss - 0.5488, iou_score - 0.3651]\n",
      "Model saved!\n",
      "\n",
      "Epoch: 97\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.45it/s, dice_loss - 0.5264, iou_score - 0.3971]\n",
      "valid: 100%|██████████████████████████████████████| 4/4 [00:00<00:00,  8.67it/s, dice_loss - 0.5494, iou_score - 0.366]\n",
      "Model saved!\n",
      "\n",
      "Epoch: 98\n",
      "train: 100%|██████████████████████████████████████| 7/7 [00:02<00:00,  2.49it/s, dice_loss - 0.527, iou_score - 0.3957]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.06it/s, dice_loss - 0.5524, iou_score - 0.3699]\n",
      "Model saved!\n",
      "\n",
      "Epoch: 99\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.50it/s, dice_loss - 0.5232, iou_score - 0.4091]\n",
      "valid: 100%|██████████████████████████████████████| 4/4 [00:00<00:00,  8.54it/s, dice_loss - 0.554, iou_score - 0.3767]\n",
      "Model saved!\n",
      "\n",
      "Epoch: 100\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.49it/s, dice_loss - 0.5225, iou_score - 0.4187]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  7.81it/s, dice_loss - 0.5513, iou_score - 0.3874]\n",
      "Model saved!\n",
      "\n",
      "Epoch: 101\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.49it/s, dice_loss - 0.5212, iou_score - 0.4286]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.78it/s, dice_loss - 0.5487, iou_score - 0.3969]\n",
      "Model saved!\n",
      "\n",
      "Epoch: 102\n",
      "train: 100%|██████████████████████████████████████| 7/7 [00:02<00:00,  2.47it/s, dice_loss - 0.519, iou_score - 0.4354]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.06it/s, dice_loss - 0.5475, iou_score - 0.4052]\n",
      "Model saved!\n",
      "\n",
      "Epoch: 103\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.50it/s, dice_loss - 0.5135, iou_score - 0.4504]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.92it/s, dice_loss - 0.5473, iou_score - 0.4168]\n",
      "Model saved!\n",
      "\n",
      "Epoch: 104\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.50it/s, dice_loss - 0.5132, iou_score - 0.4545]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.68it/s, dice_loss - 0.5446, iou_score - 0.4248]\n",
      "Model saved!\n",
      "\n",
      "Epoch: 105\n",
      "train: 100%|██████████████████████████████████████| 7/7 [00:02<00:00,  2.50it/s, dice_loss - 0.516, iou_score - 0.4571]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.34it/s, dice_loss - 0.5406, iou_score - 0.4305]\n",
      "Model saved!\n",
      "\n",
      "Epoch: 106\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.48it/s, dice_loss - 0.5104, iou_score - 0.4739]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.42it/s, dice_loss - 0.5388, iou_score - 0.4465]\n",
      "Model saved!\n",
      "\n",
      "Epoch: 107\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.39it/s, dice_loss - 0.5085, iou_score - 0.4735]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.55it/s, dice_loss - 0.5388, iou_score - 0.4491]\n",
      "Model saved!\n",
      "\n",
      "Epoch: 108\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.45it/s, dice_loss - 0.5077, iou_score - 0.4822]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.01it/s, dice_loss - 0.5392, iou_score - 0.4518]\n",
      "Model saved!\n",
      "\n",
      "Epoch: 109\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.49it/s, dice_loss - 0.5039, iou_score - 0.4891]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.48it/s, dice_loss - 0.5406, iou_score - 0.4526]\n",
      "Model saved!\n",
      "\n",
      "Epoch: 110\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.49it/s, dice_loss - 0.5035, iou_score - 0.4899]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.48it/s, dice_loss - 0.5359, iou_score - 0.4587]\n",
      "Model saved!\n",
      "\n",
      "Epoch: 111\n",
      "train: 100%|██████████████████████████████████████| 7/7 [00:02<00:00,  2.46it/s, dice_loss - 0.5021, iou_score - 0.498]\n",
      "valid: 100%|██████████████████████████████████████| 4/4 [00:00<00:00,  9.01it/s, dice_loss - 0.538, iou_score - 0.4659]\n",
      "Model saved!\n",
      "\n",
      "Epoch: 112\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.40it/s, dice_loss - 0.5002, iou_score - 0.5018]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.62it/s, dice_loss - 0.5381, iou_score - 0.4663]\n",
      "Model saved!\n",
      "\n",
      "Epoch: 113\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.44it/s, dice_loss - 0.4994, iou_score - 0.5059]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.50it/s, dice_loss - 0.5354, iou_score - 0.4746]\n",
      "Model saved!\n",
      "\n",
      "Epoch: 114\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.47it/s, dice_loss - 0.4962, iou_score - 0.5128]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.04it/s, dice_loss - 0.5288, iou_score - 0.4876]\n",
      "Model saved!\n",
      "\n",
      "Epoch: 115\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.38it/s, dice_loss - 0.4928, iou_score - 0.5159]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.65it/s, dice_loss - 0.5351, iou_score - 0.4803]\n",
      "\n",
      "Epoch: 116\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.42it/s, dice_loss - 0.4879, iou_score - 0.5288]\n",
      "valid: 100%|██████████████████████████████████████| 4/4 [00:00<00:00,  7.90it/s, dice_loss - 0.5304, iou_score - 0.489]\n",
      "Model saved!\n",
      "\n",
      "Epoch: 117\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.41it/s, dice_loss - 0.4898, iou_score - 0.5239]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.41it/s, dice_loss - 0.5307, iou_score - 0.4905]\n",
      "Model saved!\n",
      "\n",
      "Epoch: 118\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.46it/s, dice_loss - 0.4844, iou_score - 0.5352]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.72it/s, dice_loss - 0.5295, iou_score - 0.4905]\n",
      "\n",
      "Epoch: 119\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.46it/s, dice_loss - 0.4832, iou_score - 0.5338]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.49it/s, dice_loss - 0.5211, iou_score - 0.5035]\n",
      "Model saved!\n",
      "\n",
      "Epoch: 120\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.43it/s, dice_loss - 0.4834, iou_score - 0.5335]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.75it/s, dice_loss - 0.5204, iou_score - 0.5053]\n",
      "Model saved!\n",
      "\n",
      "Epoch: 121\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.45it/s, dice_loss - 0.4836, iou_score - 0.5312]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.73it/s, dice_loss - 0.5206, iou_score - 0.5018]\n",
      "\n",
      "Epoch: 122\n",
      "train: 100%|██████████████████████████████████████| 7/7 [00:02<00:00,  2.44it/s, dice_loss - 0.4811, iou_score - 0.537]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  7.66it/s, dice_loss - 0.5181, iou_score - 0.5061]\n",
      "Model saved!\n",
      "\n",
      "Epoch: 123\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.48it/s, dice_loss - 0.4777, iou_score - 0.5389]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.90it/s, dice_loss - 0.5203, iou_score - 0.5036]\n",
      "\n",
      "Epoch: 124\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.46it/s, dice_loss - 0.4779, iou_score - 0.5369]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.34it/s, dice_loss - 0.5165, iou_score - 0.5059]\n",
      "\n",
      "Epoch: 125\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.46it/s, dice_loss - 0.4756, iou_score - 0.5414]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.69it/s, dice_loss - 0.5169, iou_score - 0.5024]\n",
      "\n",
      "Epoch: 126\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.43it/s, dice_loss - 0.4741, iou_score - 0.5427]\n",
      "valid: 100%|██████████████████████████████████████| 4/4 [00:00<00:00,  8.06it/s, dice_loss - 0.5097, iou_score - 0.513]\n",
      "Model saved!\n",
      "\n",
      "Epoch: 127\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.44it/s, dice_loss - 0.4711, iou_score - 0.5455]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.76it/s, dice_loss - 0.5069, iou_score - 0.5173]\n",
      "Model saved!\n",
      "\n",
      "Epoch: 128\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.45it/s, dice_loss - 0.4633, iou_score - 0.5583]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.34it/s, dice_loss - 0.5051, iou_score - 0.5214]\n",
      "Model saved!\n",
      "\n",
      "Epoch: 129\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.45it/s, dice_loss - 0.4668, iou_score - 0.5507]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  7.73it/s, dice_loss - 0.5109, iou_score - 0.5114]\n",
      "\n",
      "Epoch: 130\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.42it/s, dice_loss - 0.4645, iou_score - 0.5532]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.85it/s, dice_loss - 0.5111, iou_score - 0.5109]\n",
      "\n",
      "Epoch: 131\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.44it/s, dice_loss - 0.4633, iou_score - 0.5541]\n",
      "valid: 100%|████████████████████████████████████████| 4/4 [00:00<00:00,  8.73it/s, dice_loss - 0.5, iou_score - 0.5234]\n",
      "Model saved!\n",
      "\n",
      "Epoch: 132\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.52it/s, dice_loss - 0.4601, iou_score - 0.5572]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.59it/s, dice_loss - 0.5096, iou_score - 0.5118]\n",
      "\n",
      "Epoch: 133\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.49it/s, dice_loss - 0.4545, iou_score - 0.5672]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.30it/s, dice_loss - 0.5127, iou_score - 0.5062]\n",
      "\n",
      "Epoch: 134\n",
      "train: 100%|██████████████████████████████████████| 7/7 [00:02<00:00,  2.50it/s, dice_loss - 0.4533, iou_score - 0.565]\n",
      "valid: 100%|███████████████████████████████████████| 4/4 [00:00<00:00,  8.60it/s, dice_loss - 0.502, iou_score - 0.519]\n",
      "\n",
      "Epoch: 135\n",
      "train: 100%|██████████████████████████████████████| 7/7 [00:02<00:00,  2.51it/s, dice_loss - 0.4533, iou_score - 0.564]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  7.98it/s, dice_loss - 0.4963, iou_score - 0.5232]\n",
      "\n",
      "Epoch: 136\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.46it/s, dice_loss - 0.4514, iou_score - 0.5632]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.15it/s, dice_loss - 0.4871, iou_score - 0.5345]\n",
      "Model saved!\n",
      "\n",
      "Epoch: 137\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.48it/s, dice_loss - 0.4514, iou_score - 0.5643]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.49it/s, dice_loss - 0.4912, iou_score - 0.5282]\n",
      "\n",
      "Epoch: 138\n",
      "train: 100%|██████████████████████████████████████| 7/7 [00:02<00:00,  2.45it/s, dice_loss - 0.4489, iou_score - 0.566]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.62it/s, dice_loss - 0.4891, iou_score - 0.5313]\n",
      "\n",
      "Epoch: 139\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.43it/s, dice_loss - 0.4493, iou_score - 0.5645]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  7.99it/s, dice_loss - 0.4953, iou_score - 0.5239]\n",
      "\n",
      "Epoch: 140\n",
      "train: 100%|██████████████████████████████████████| 7/7 [00:03<00:00,  2.20it/s, dice_loss - 0.443, iou_score - 0.5713]\n",
      "valid: 100%|██████████████████████████████████████| 4/4 [00:00<00:00,  8.28it/s, dice_loss - 0.4845, iou_score - 0.537]\n",
      "Model saved!\n",
      "\n",
      "Epoch: 141\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.50it/s, dice_loss - 0.4419, iou_score - 0.5716]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.23it/s, dice_loss - 0.4839, iou_score - 0.5325]\n",
      "\n",
      "Epoch: 142\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.48it/s, dice_loss - 0.4394, iou_score - 0.5745]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  9.07it/s, dice_loss - 0.4828, iou_score - 0.5374]\n",
      "Model saved!\n",
      "\n",
      "Epoch: 143\n",
      "train: 100%|██████████████████████████████████████| 7/7 [00:02<00:00,  2.47it/s, dice_loss - 0.4372, iou_score - 0.578]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.39it/s, dice_loss - 0.4865, iou_score - 0.5283]\n",
      "\n",
      "Epoch: 144\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.48it/s, dice_loss - 0.4364, iou_score - 0.5776]\n",
      "valid: 100%|██████████████████████████████████████| 4/4 [00:00<00:00,  8.41it/s, dice_loss - 0.486, iou_score - 0.5297]\n",
      "\n",
      "Epoch: 145\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.39it/s, dice_loss - 0.4331, iou_score - 0.5812]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.49it/s, dice_loss - 0.4807, iou_score - 0.5344]\n",
      "\n",
      "Epoch: 146\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.41it/s, dice_loss - 0.4298, iou_score - 0.5851]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.07it/s, dice_loss - 0.4767, iou_score - 0.5416]\n",
      "Model saved!\n",
      "\n",
      "Epoch: 147\n",
      "train: 100%|██████████████████████████████████████| 7/7 [00:02<00:00,  2.45it/s, dice_loss - 0.4272, iou_score - 0.588]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.64it/s, dice_loss - 0.4718, iou_score - 0.5469]\n",
      "Model saved!\n",
      "\n",
      "Epoch: 148\n",
      "train: 100%|███████████████████████████████████████| 7/7 [00:02<00:00,  2.46it/s, dice_loss - 0.432, iou_score - 0.576]\n",
      "valid: 100%|██████████████████████████████████████| 4/4 [00:00<00:00,  8.38it/s, dice_loss - 0.4741, iou_score - 0.543]\n",
      "\n",
      "Epoch: 149\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:03<00:00,  2.27it/s, dice_loss - 0.4242, iou_score - 0.5891]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  7.64it/s, dice_loss - 0.4737, iou_score - 0.5414]\n",
      "\n",
      "Epoch: 150\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:03<00:00,  2.32it/s, dice_loss - 0.4246, iou_score - 0.5877]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  7.51it/s, dice_loss - 0.4675, iou_score - 0.5505]\n",
      "Model saved!\n",
      "\n",
      "Epoch: 151\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.34it/s, dice_loss - 0.4213, iou_score - 0.5931]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.09it/s, dice_loss - 0.4735, iou_score - 0.5391]\n",
      "\n",
      "Epoch: 152\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.40it/s, dice_loss - 0.4216, iou_score - 0.5896]\n",
      "valid: 100%|██████████████████████████████████████| 4/4 [00:00<00:00,  7.01it/s, dice_loss - 0.462, iou_score - 0.5553]\n",
      "Model saved!\n",
      "\n",
      "Epoch: 153\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:03<00:00,  2.29it/s, dice_loss - 0.4222, iou_score - 0.5884]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.12it/s, dice_loss - 0.4639, iou_score - 0.5514]\n",
      "\n",
      "Epoch: 154\n",
      "train: 100%|██████████████████████████████████████| 7/7 [00:02<00:00,  2.43it/s, dice_loss - 0.4161, iou_score - 0.596]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.46it/s, dice_loss - 0.4585, iou_score - 0.5576]\n",
      "Model saved!\n",
      "\n",
      "Epoch: 155\n",
      "train: 100%|██████████████████████████████████████| 7/7 [00:02<00:00,  2.47it/s, dice_loss - 0.416, iou_score - 0.5947]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.05it/s, dice_loss - 0.4603, iou_score - 0.5554]\n",
      "\n",
      "Epoch: 156\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.43it/s, dice_loss - 0.4143, iou_score - 0.5986]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.49it/s, dice_loss - 0.4624, iou_score - 0.5567]\n",
      "\n",
      "Epoch: 157\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.48it/s, dice_loss - 0.4129, iou_score - 0.5993]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  9.00it/s, dice_loss - 0.4541, iou_score - 0.5649]\n",
      "Model saved!\n",
      "\n",
      "Epoch: 158\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.46it/s, dice_loss - 0.4131, iou_score - 0.6001]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.77it/s, dice_loss - 0.4555, iou_score - 0.5617]\n",
      "\n",
      "Epoch: 159\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.49it/s, dice_loss - 0.4083, iou_score - 0.6047]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.32it/s, dice_loss - 0.4568, iou_score - 0.5609]\n",
      "\n",
      "Epoch: 160\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.49it/s, dice_loss - 0.4083, iou_score - 0.6067]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.45it/s, dice_loss - 0.4572, iou_score - 0.5625]\n",
      "\n",
      "Epoch: 161\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.45it/s, dice_loss - 0.4099, iou_score - 0.6035]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.59it/s, dice_loss - 0.4598, iou_score - 0.5554]\n",
      "\n",
      "Epoch: 162\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.44it/s, dice_loss - 0.4079, iou_score - 0.6027]\n",
      "valid: 100%|██████████████████████████████████████| 4/4 [00:00<00:00,  7.92it/s, dice_loss - 0.456, iou_score - 0.5627]\n",
      "\n",
      "Epoch: 163\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.46it/s, dice_loss - 0.4019, iou_score - 0.6133]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.83it/s, dice_loss - 0.4449, iou_score - 0.5747]\n",
      "Model saved!\n",
      "\n",
      "Epoch: 164\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.44it/s, dice_loss - 0.4027, iou_score - 0.6094]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.13it/s, dice_loss - 0.4475, iou_score - 0.5735]\n",
      "\n",
      "Epoch: 165\n",
      "train: 100%|██████████████████████████████████████| 7/7 [00:02<00:00,  2.39it/s, dice_loss - 0.401, iou_score - 0.6121]\n",
      "valid: 100%|██████████████████████████████████████| 4/4 [00:00<00:00,  8.48it/s, dice_loss - 0.448, iou_score - 0.5704]\n",
      "\n",
      "Epoch: 166\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.46it/s, dice_loss - 0.3985, iou_score - 0.6151]\n",
      "valid: 100%|██████████████████████████████████████| 4/4 [00:00<00:00,  8.83it/s, dice_loss - 0.4471, iou_score - 0.573]\n",
      "\n",
      "Epoch: 167\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.48it/s, dice_loss - 0.3989, iou_score - 0.6145]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  9.03it/s, dice_loss - 0.4472, iou_score - 0.5739]\n",
      "\n",
      "Epoch: 168\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.50it/s, dice_loss - 0.3955, iou_score - 0.6194]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.24it/s, dice_loss - 0.4394, iou_score - 0.5819]\n",
      "Model saved!\n",
      "\n",
      "Epoch: 169\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.48it/s, dice_loss - 0.3985, iou_score - 0.6159]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.76it/s, dice_loss - 0.4423, iou_score - 0.5781]\n",
      "\n",
      "Epoch: 170\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.45it/s, dice_loss - 0.3949, iou_score - 0.6191]\n",
      "valid: 100%|██████████████████████████████████████| 4/4 [00:00<00:00,  8.33it/s, dice_loss - 0.4517, iou_score - 0.562]\n",
      "\n",
      "Epoch: 171\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.49it/s, dice_loss - 0.3913, iou_score - 0.6235]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.02it/s, dice_loss - 0.4402, iou_score - 0.5803]\n",
      "\n",
      "Epoch: 172\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.38it/s, dice_loss - 0.3915, iou_score - 0.6234]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.34it/s, dice_loss - 0.4373, iou_score - 0.5874]\n",
      "Model saved!\n",
      "\n",
      "Epoch: 173\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.49it/s, dice_loss - 0.3889, iou_score - 0.6266]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.67it/s, dice_loss - 0.4392, iou_score - 0.5812]\n",
      "\n",
      "Epoch: 174\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.46it/s, dice_loss - 0.3919, iou_score - 0.6225]\n",
      "valid: 100%|██████████████████████████████████████| 4/4 [00:00<00:00,  8.71it/s, dice_loss - 0.439, iou_score - 0.5802]\n",
      "\n",
      "Epoch: 175\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.46it/s, dice_loss - 0.3871, iou_score - 0.6283]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.86it/s, dice_loss - 0.4418, iou_score - 0.5744]\n",
      "\n",
      "Epoch: 176\n",
      "train: 100%|██████████████████████████████████████| 7/7 [00:02<00:00,  2.50it/s, dice_loss - 0.387, iou_score - 0.6281]\n",
      "valid: 100%|██████████████████████████████████████| 4/4 [00:00<00:00,  8.88it/s, dice_loss - 0.441, iou_score - 0.5755]\n",
      "\n",
      "Epoch: 177\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.50it/s, dice_loss - 0.3867, iou_score - 0.6287]\n",
      "valid: 100%|██████████████████████████████████████| 4/4 [00:00<00:00,  8.65it/s, dice_loss - 0.444, iou_score - 0.5749]\n",
      "\n",
      "Epoch: 178\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.44it/s, dice_loss - 0.3867, iou_score - 0.6282]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.69it/s, dice_loss - 0.4371, iou_score - 0.5821]\n",
      "\n",
      "Epoch: 179\n",
      "train: 100%|██████████████████████████████████████| 7/7 [00:02<00:00,  2.42it/s, dice_loss - 0.3822, iou_score - 0.633]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  7.65it/s, dice_loss - 0.4356, iou_score - 0.5837]\n",
      "\n",
      "Epoch: 180\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.41it/s, dice_loss - 0.3804, iou_score - 0.6356]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.81it/s, dice_loss - 0.4359, iou_score - 0.5831]\n",
      "\n",
      "Epoch: 181\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.50it/s, dice_loss - 0.3816, iou_score - 0.6313]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.80it/s, dice_loss - 0.4313, iou_score - 0.5887]\n",
      "Model saved!\n",
      "\n",
      "Epoch: 182\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.49it/s, dice_loss - 0.3771, iou_score - 0.6389]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.82it/s, dice_loss - 0.4268, iou_score - 0.5941]\n",
      "Model saved!\n",
      "\n",
      "Epoch: 183\n",
      "train: 100%|██████████████████████████████████████| 7/7 [00:02<00:00,  2.47it/s, dice_loss - 0.3773, iou_score - 0.638]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.77it/s, dice_loss - 0.4281, iou_score - 0.5924]\n",
      "\n",
      "Epoch: 184\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.45it/s, dice_loss - 0.3776, iou_score - 0.6377]\n",
      "valid: 100%|██████████████████████████████████████| 4/4 [00:00<00:00,  8.75it/s, dice_loss - 0.433, iou_score - 0.5879]\n",
      "\n",
      "Epoch: 185\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.49it/s, dice_loss - 0.3738, iou_score - 0.6419]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.44it/s, dice_loss - 0.4244, iou_score - 0.5982]\n",
      "Model saved!\n",
      "\n",
      "Epoch: 186\n",
      "train: 100%|██████████████████████████████████████| 7/7 [00:02<00:00,  2.50it/s, dice_loss - 0.3769, iou_score - 0.635]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.43it/s, dice_loss - 0.4381, iou_score - 0.5774]\n",
      "\n",
      "Epoch: 187\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.49it/s, dice_loss - 0.3724, iou_score - 0.6419]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.26it/s, dice_loss - 0.4288, iou_score - 0.5876]\n",
      "\n",
      "Epoch: 188\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.50it/s, dice_loss - 0.3748, iou_score - 0.6376]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.59it/s, dice_loss - 0.4217, iou_score - 0.5973]\n",
      "\n",
      "Epoch: 189\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.50it/s, dice_loss - 0.3709, iou_score - 0.6421]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.59it/s, dice_loss - 0.4257, iou_score - 0.5922]\n",
      "\n",
      "Epoch: 190\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.45it/s, dice_loss - 0.3714, iou_score - 0.6384]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.39it/s, dice_loss - 0.4159, iou_score - 0.6026]\n",
      "Model saved!\n",
      "\n",
      "Epoch: 191\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.39it/s, dice_loss - 0.3701, iou_score - 0.6428]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  7.58it/s, dice_loss - 0.4222, iou_score - 0.5967]\n",
      "\n",
      "Epoch: 192\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.41it/s, dice_loss - 0.3693, iou_score - 0.6445]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.32it/s, dice_loss - 0.4171, iou_score - 0.6017]\n",
      "\n",
      "Epoch: 193\n",
      "train: 100%|██████████████████████████████████████| 7/7 [00:02<00:00,  2.47it/s, dice_loss - 0.367, iou_score - 0.6471]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.77it/s, dice_loss - 0.4173, iou_score - 0.6018]\n",
      "\n",
      "Epoch: 194\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.45it/s, dice_loss - 0.3673, iou_score - 0.6442]\n",
      "valid: 100%|██████████████████████████████████████| 4/4 [00:00<00:00,  8.74it/s, dice_loss - 0.422, iou_score - 0.5937]\n",
      "\n",
      "Epoch: 195\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.48it/s, dice_loss - 0.3674, iou_score - 0.6445]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.70it/s, dice_loss - 0.4196, iou_score - 0.5966]\n",
      "\n",
      "Epoch: 196\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.48it/s, dice_loss - 0.3665, iou_score - 0.6427]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.50it/s, dice_loss - 0.4178, iou_score - 0.5999]\n",
      "\n",
      "Epoch: 197\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.46it/s, dice_loss - 0.3659, iou_score - 0.6447]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  7.85it/s, dice_loss - 0.4146, iou_score - 0.6043]\n",
      "Model saved!\n",
      "\n",
      "Epoch: 198\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.51it/s, dice_loss - 0.3615, iou_score - 0.6503]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.44it/s, dice_loss - 0.4184, iou_score - 0.5983]\n",
      "\n",
      "Epoch: 199\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.41it/s, dice_loss - 0.3604, iou_score - 0.6514]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.47it/s, dice_loss - 0.4109, iou_score - 0.6079]\n",
      "Model saved!\n",
      "\n",
      "Epoch: 200\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.48it/s, dice_loss - 0.3599, iou_score - 0.6524]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.65it/s, dice_loss - 0.4137, iou_score - 0.6046]\n",
      "\n",
      "Epoch: 201\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.42it/s, dice_loss - 0.3587, iou_score - 0.6528]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.34it/s, dice_loss - 0.4081, iou_score - 0.6105]\n",
      "Model saved!\n",
      "\n",
      "Epoch: 202\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.43it/s, dice_loss - 0.3586, iou_score - 0.6529]\n",
      "valid: 100%|██████████████████████████████████████| 4/4 [00:00<00:00,  8.15it/s, dice_loss - 0.4116, iou_score - 0.606]\n",
      "\n",
      "Epoch: 203\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.47it/s, dice_loss - 0.3551, iou_score - 0.6572]\n",
      "valid: 100%|██████████████████████████████████████| 4/4 [00:00<00:00,  8.24it/s, dice_loss - 0.4141, iou_score - 0.605]\n",
      "\n",
      "Epoch: 204\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.39it/s, dice_loss - 0.3553, iou_score - 0.6554]\n",
      "valid: 100%|██████████████████████████████████████| 4/4 [00:00<00:00,  8.82it/s, dice_loss - 0.407, iou_score - 0.6117]\n",
      "Model saved!\n",
      "\n",
      "Epoch: 205\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.46it/s, dice_loss - 0.3538, iou_score - 0.6579]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.80it/s, dice_loss - 0.4097, iou_score - 0.6064]\n",
      "\n",
      "Epoch: 206\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.44it/s, dice_loss - 0.3527, iou_score - 0.6585]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.75it/s, dice_loss - 0.4058, iou_score - 0.6077]\n",
      "\n",
      "Epoch: 207\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.46it/s, dice_loss - 0.3499, iou_score - 0.6608]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.57it/s, dice_loss - 0.4085, iou_score - 0.6081]\n",
      "\n",
      "Epoch: 208\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.42it/s, dice_loss - 0.3507, iou_score - 0.6607]\n",
      "valid: 100%|██████████████████████████████████████| 4/4 [00:00<00:00,  8.75it/s, dice_loss - 0.412, iou_score - 0.6047]\n",
      "\n",
      "Epoch: 209\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.48it/s, dice_loss - 0.3483, iou_score - 0.6634]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  6.81it/s, dice_loss - 0.4065, iou_score - 0.6103]\n",
      "\n",
      "Epoch: 210\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:03<00:00,  2.07it/s, dice_loss - 0.3501, iou_score - 0.6616]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  6.60it/s, dice_loss - 0.4099, iou_score - 0.6054]\n",
      "\n",
      "Epoch: 211\n",
      "train: 100%|██████████████████████████████████████| 7/7 [00:02<00:00,  2.36it/s, dice_loss - 0.3488, iou_score - 0.665]\n",
      "valid: 100%|██████████████████████████████████████| 4/4 [00:00<00:00,  8.17it/s, dice_loss - 0.4173, iou_score - 0.596]\n",
      "\n",
      "Epoch: 212\n",
      "train: 100%|██████████████████████████████████████| 7/7 [00:02<00:00,  2.40it/s, dice_loss - 0.3488, iou_score - 0.661]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.62it/s, dice_loss - 0.4066, iou_score - 0.6094]\n",
      "\n",
      "Epoch: 213\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.49it/s, dice_loss - 0.3448, iou_score - 0.6655]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  7.31it/s, dice_loss - 0.4092, iou_score - 0.6045]\n",
      "\n",
      "Epoch: 214\n",
      "train: 100%|██████████████████████████████████████| 7/7 [00:02<00:00,  2.40it/s, dice_loss - 0.345, iou_score - 0.6642]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.88it/s, dice_loss - 0.4037, iou_score - 0.6105]\n",
      "\n",
      "Epoch: 215\n",
      "train: 100%|██████████████████████████████████████| 7/7 [00:02<00:00,  2.37it/s, dice_loss - 0.345, iou_score - 0.6645]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.12it/s, dice_loss - 0.4056, iou_score - 0.6067]\n",
      "\n",
      "Epoch: 216\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.41it/s, dice_loss - 0.3464, iou_score - 0.6626]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.78it/s, dice_loss - 0.3971, iou_score - 0.6194]\n",
      "Model saved!\n",
      "\n",
      "Epoch: 217\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.46it/s, dice_loss - 0.3413, iou_score - 0.6689]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.40it/s, dice_loss - 0.4031, iou_score - 0.6121]\n",
      "\n",
      "Epoch: 218\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:03<00:00,  2.28it/s, dice_loss - 0.3403, iou_score - 0.6697]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.58it/s, dice_loss - 0.4091, iou_score - 0.6012]\n",
      "\n",
      "Epoch: 219\n",
      "train: 100%|███████████████████████████████████████| 7/7 [00:02<00:00,  2.40it/s, dice_loss - 0.34, iou_score - 0.6674]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.17it/s, dice_loss - 0.3956, iou_score - 0.6175]\n",
      "\n",
      "Epoch: 220\n",
      "train: 100%|██████████████████████████████████████| 7/7 [00:02<00:00,  2.46it/s, dice_loss - 0.3415, iou_score - 0.667]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.85it/s, dice_loss - 0.4002, iou_score - 0.6107]\n",
      "\n",
      "Epoch: 221\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.48it/s, dice_loss - 0.3394, iou_score - 0.6712]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.55it/s, dice_loss - 0.3943, iou_score - 0.6211]\n",
      "Model saved!\n",
      "\n",
      "Epoch: 222\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.49it/s, dice_loss - 0.3368, iou_score - 0.6733]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.38it/s, dice_loss - 0.3958, iou_score - 0.6176]\n",
      "\n",
      "Epoch: 223\n",
      "train: 100%|██████████████████████████████████████| 7/7 [00:02<00:00,  2.35it/s, dice_loss - 0.3379, iou_score - 0.671]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.53it/s, dice_loss - 0.3932, iou_score - 0.6198]\n",
      "\n",
      "Epoch: 224\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.37it/s, dice_loss - 0.3366, iou_score - 0.6721]\n",
      "valid: 100%|██████████████████████████████████████| 4/4 [00:00<00:00,  8.06it/s, dice_loss - 0.3905, iou_score - 0.624]\n",
      "Model saved!\n",
      "\n",
      "Epoch: 225\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.44it/s, dice_loss - 0.3367, iou_score - 0.6728]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.64it/s, dice_loss - 0.3917, iou_score - 0.6231]\n",
      "\n",
      "Epoch: 226\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.38it/s, dice_loss - 0.3354, iou_score - 0.6721]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.53it/s, dice_loss - 0.3959, iou_score - 0.6145]\n",
      "\n",
      "Epoch: 227\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.38it/s, dice_loss - 0.3313, iou_score - 0.6781]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.09it/s, dice_loss - 0.3943, iou_score - 0.6191]\n",
      "\n",
      "Epoch: 228\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.37it/s, dice_loss - 0.3336, iou_score - 0.6742]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.27it/s, dice_loss - 0.4001, iou_score - 0.6093]\n",
      "\n",
      "Epoch: 229\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.46it/s, dice_loss - 0.3294, iou_score - 0.6781]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.17it/s, dice_loss - 0.3941, iou_score - 0.6179]\n",
      "\n",
      "Epoch: 230\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.45it/s, dice_loss - 0.3295, iou_score - 0.6795]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.64it/s, dice_loss - 0.3853, iou_score - 0.6289]\n",
      "Model saved!\n",
      "\n",
      "Epoch: 231\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.42it/s, dice_loss - 0.3304, iou_score - 0.6789]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.49it/s, dice_loss - 0.3929, iou_score - 0.6176]\n",
      "\n",
      "Epoch: 232\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.49it/s, dice_loss - 0.3302, iou_score - 0.6785]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.23it/s, dice_loss - 0.3859, iou_score - 0.6256]\n",
      "\n",
      "Epoch: 233\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.42it/s, dice_loss - 0.3294, iou_score - 0.6776]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.19it/s, dice_loss - 0.3846, iou_score - 0.6287]\n",
      "\n",
      "Epoch: 234\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.41it/s, dice_loss - 0.3286, iou_score - 0.6779]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.41it/s, dice_loss - 0.3822, iou_score - 0.6298]\n",
      "Model saved!\n",
      "\n",
      "Epoch: 235\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.35it/s, dice_loss - 0.3254, iou_score - 0.6822]\n",
      "valid: 100%|██████████████████████████████████████| 4/4 [00:00<00:00,  8.67it/s, dice_loss - 0.3942, iou_score - 0.614]\n",
      "\n",
      "Epoch: 236\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.47it/s, dice_loss - 0.3276, iou_score - 0.6811]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  7.90it/s, dice_loss - 0.3837, iou_score - 0.6267]\n",
      "\n",
      "Epoch: 237\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:03<00:00,  2.33it/s, dice_loss - 0.3238, iou_score - 0.6848]\n",
      "valid: 100%|██████████████████████████████████████| 4/4 [00:00<00:00,  8.12it/s, dice_loss - 0.383, iou_score - 0.6302]\n",
      "Model saved!\n",
      "\n",
      "Epoch: 238\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.50it/s, dice_loss - 0.3239, iou_score - 0.6844]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  7.98it/s, dice_loss - 0.3808, iou_score - 0.6315]\n",
      "Model saved!\n",
      "\n",
      "Epoch: 239\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.44it/s, dice_loss - 0.3211, iou_score - 0.6875]\n",
      "valid: 100%|██████████████████████████████████████| 4/4 [00:00<00:00,  8.31it/s, dice_loss - 0.384, iou_score - 0.6272]\n",
      "\n",
      "Epoch: 240\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.50it/s, dice_loss - 0.3231, iou_score - 0.6845]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.74it/s, dice_loss - 0.3824, iou_score - 0.6263]\n",
      "\n",
      "Epoch: 241\n",
      "train: 100%|██████████████████████████████████████| 7/7 [00:02<00:00,  2.45it/s, dice_loss - 0.326, iou_score - 0.6816]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.69it/s, dice_loss - 0.3831, iou_score - 0.6276]\n",
      "\n",
      "Epoch: 242\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.43it/s, dice_loss - 0.3204, iou_score - 0.6898]\n",
      "valid: 100%|██████████████████████████████████████| 4/4 [00:00<00:00,  8.50it/s, dice_loss - 0.3855, iou_score - 0.627]\n",
      "\n",
      "Epoch: 243\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.41it/s, dice_loss - 0.3167, iou_score - 0.6906]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  7.98it/s, dice_loss - 0.3807, iou_score - 0.6297]\n",
      "\n",
      "Epoch: 244\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.47it/s, dice_loss - 0.3175, iou_score - 0.6865]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.53it/s, dice_loss - 0.3781, iou_score - 0.6313]\n",
      "\n",
      "Epoch: 245\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.49it/s, dice_loss - 0.3135, iou_score - 0.6936]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.56it/s, dice_loss - 0.3845, iou_score - 0.6243]\n",
      "\n",
      "Epoch: 246\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.47it/s, dice_loss - 0.3158, iou_score - 0.6911]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.69it/s, dice_loss - 0.3717, iou_score - 0.6408]\n",
      "Model saved!\n",
      "\n",
      "Epoch: 247\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.50it/s, dice_loss - 0.3125, iou_score - 0.6964]\n",
      "valid: 100%|██████████████████████████████████████| 4/4 [00:00<00:00,  8.75it/s, dice_loss - 0.376, iou_score - 0.6357]\n",
      "\n",
      "Epoch: 248\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.49it/s, dice_loss - 0.3128, iou_score - 0.6958]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  7.93it/s, dice_loss - 0.3863, iou_score - 0.6203]\n",
      "\n",
      "Epoch: 249\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.47it/s, dice_loss - 0.3122, iou_score - 0.6936]\n",
      "valid: 100%|███████████████████████████████████████| 4/4 [00:00<00:00,  8.54it/s, dice_loss - 0.3804, iou_score - 0.63]\n",
      "\n",
      "Epoch: 250\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.43it/s, dice_loss - 0.3137, iou_score - 0.6923]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.76it/s, dice_loss - 0.3763, iou_score - 0.6331]\n",
      "\n",
      "Epoch: 251\n",
      "train: 100%|██████████████████████████████████████| 7/7 [00:02<00:00,  2.43it/s, dice_loss - 0.313, iou_score - 0.6947]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.52it/s, dice_loss - 0.3717, iou_score - 0.6403]\n",
      "\n",
      "Epoch: 252\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.48it/s, dice_loss - 0.3125, iou_score - 0.6924]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.49it/s, dice_loss - 0.3741, iou_score - 0.6356]\n",
      "\n",
      "Epoch: 253\n",
      "train: 100%|██████████████████████████████████████| 7/7 [00:02<00:00,  2.43it/s, dice_loss - 0.309, iou_score - 0.6978]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.02it/s, dice_loss - 0.3781, iou_score - 0.6291]\n",
      "\n",
      "Epoch: 254\n",
      "train: 100%|██████████████████████████████████████| 7/7 [00:02<00:00,  2.49it/s, dice_loss - 0.306, iou_score - 0.7035]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.05it/s, dice_loss - 0.3756, iou_score - 0.6334]\n",
      "\n",
      "Epoch: 255\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.45it/s, dice_loss - 0.3099, iou_score - 0.6972]\n",
      "valid: 100%|██████████████████████████████████████| 4/4 [00:00<00:00,  8.54it/s, dice_loss - 0.3731, iou_score - 0.636]\n",
      "\n",
      "Epoch: 256\n",
      "train: 100%|██████████████████████████████████████| 7/7 [00:02<00:00,  2.42it/s, dice_loss - 0.3066, iou_score - 0.702]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.11it/s, dice_loss - 0.3741, iou_score - 0.6346]\n",
      "\n",
      "Epoch: 257\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.42it/s, dice_loss - 0.3084, iou_score - 0.6964]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.01it/s, dice_loss - 0.3784, iou_score - 0.6297]\n",
      "\n",
      "Epoch: 258\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.47it/s, dice_loss - 0.3078, iou_score - 0.6997]\n",
      "valid: 100%|██████████████████████████████████████| 4/4 [00:00<00:00,  8.39it/s, dice_loss - 0.372, iou_score - 0.6368]\n",
      "\n",
      "Epoch: 259\n",
      "train: 100%|██████████████████████████████████████| 7/7 [00:02<00:00,  2.39it/s, dice_loss - 0.307, iou_score - 0.6966]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.56it/s, dice_loss - 0.3659, iou_score - 0.6448]\n",
      "Model saved!\n",
      "\n",
      "Epoch: 260\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.47it/s, dice_loss - 0.3019, iou_score - 0.7073]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.36it/s, dice_loss - 0.3605, iou_score - 0.6516]\n",
      "Model saved!\n",
      "\n",
      "Epoch: 261\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.43it/s, dice_loss - 0.3024, iou_score - 0.7054]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.18it/s, dice_loss - 0.3678, iou_score - 0.6372]\n",
      "\n",
      "Epoch: 262\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.44it/s, dice_loss - 0.3024, iou_score - 0.7025]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.10it/s, dice_loss - 0.3766, iou_score - 0.6276]\n",
      "\n",
      "Epoch: 263\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.47it/s, dice_loss - 0.3037, iou_score - 0.6989]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.71it/s, dice_loss - 0.3721, iou_score - 0.6326]\n",
      "\n",
      "Epoch: 264\n",
      "train: 100%|████████████████████████████████████████| 7/7 [00:02<00:00,  2.44it/s, dice_loss - 0.3049, iou_score - 0.7]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.40it/s, dice_loss - 0.3611, iou_score - 0.6479]\n",
      "\n",
      "Epoch: 265\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.42it/s, dice_loss - 0.2992, iou_score - 0.7081]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.61it/s, dice_loss - 0.3719, iou_score - 0.6344]\n",
      "\n",
      "Epoch: 266\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.44it/s, dice_loss - 0.2972, iou_score - 0.7109]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.16it/s, dice_loss - 0.3723, iou_score - 0.6334]\n",
      "\n",
      "Epoch: 267\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.42it/s, dice_loss - 0.2993, iou_score - 0.7069]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.44it/s, dice_loss - 0.3687, iou_score - 0.6362]\n",
      "\n",
      "Epoch: 268\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.44it/s, dice_loss - 0.3008, iou_score - 0.7061]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.27it/s, dice_loss - 0.3655, iou_score - 0.6409]\n",
      "\n",
      "Epoch: 269\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.43it/s, dice_loss - 0.2948, iou_score - 0.7118]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.09it/s, dice_loss - 0.3693, iou_score - 0.6343]\n",
      "\n",
      "Epoch: 270\n",
      "train: 100%|██████████████████████████████████████| 7/7 [00:02<00:00,  2.41it/s, dice_loss - 0.298, iou_score - 0.7068]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.37it/s, dice_loss - 0.3634, iou_score - 0.6424]\n",
      "\n",
      "Epoch: 271\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.40it/s, dice_loss - 0.2955, iou_score - 0.7111]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.32it/s, dice_loss - 0.3708, iou_score - 0.6326]\n",
      "\n",
      "Epoch: 272\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.44it/s, dice_loss - 0.2986, iou_score - 0.7022]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.69it/s, dice_loss - 0.3626, iou_score - 0.6408]\n",
      "\n",
      "Epoch: 273\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.50it/s, dice_loss - 0.2937, iou_score - 0.7125]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.71it/s, dice_loss - 0.3644, iou_score - 0.6396]\n",
      "\n",
      "Epoch: 274\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.44it/s, dice_loss - 0.2956, iou_score - 0.7105]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.67it/s, dice_loss - 0.3663, iou_score - 0.6373]\n",
      "\n",
      "Epoch: 275\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.44it/s, dice_loss - 0.2945, iou_score - 0.7093]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.46it/s, dice_loss - 0.3598, iou_score - 0.6455]\n",
      "\n",
      "Epoch: 276\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.49it/s, dice_loss - 0.2918, iou_score - 0.7137]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.10it/s, dice_loss - 0.3571, iou_score - 0.6476]\n",
      "\n",
      "Epoch: 277\n",
      "train: 100%|██████████████████████████████████████| 7/7 [00:02<00:00,  2.46it/s, dice_loss - 0.293, iou_score - 0.7132]\n",
      "valid: 100%|██████████████████████████████████████| 4/4 [00:00<00:00,  8.60it/s, dice_loss - 0.3498, iou_score - 0.654]\n",
      "Model saved!\n",
      "\n",
      "Epoch: 278\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.44it/s, dice_loss - 0.2905, iou_score - 0.7142]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.02it/s, dice_loss - 0.3665, iou_score - 0.6337]\n",
      "\n",
      "Epoch: 279\n",
      "train: 100%|███████████████████████████████████████| 7/7 [00:02<00:00,  2.45it/s, dice_loss - 0.2858, iou_score - 0.72]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.70it/s, dice_loss - 0.3571, iou_score - 0.6468]\n",
      "\n",
      "Epoch: 280\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.44it/s, dice_loss - 0.2867, iou_score - 0.7202]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.75it/s, dice_loss - 0.3561, iou_score - 0.6471]\n",
      "\n",
      "Epoch: 281\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.40it/s, dice_loss - 0.2855, iou_score - 0.7209]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.42it/s, dice_loss - 0.3603, iou_score - 0.6415]\n",
      "\n",
      "Epoch: 282\n",
      "train: 100%|██████████████████████████████████████| 7/7 [00:02<00:00,  2.47it/s, dice_loss - 0.2884, iou_score - 0.716]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.25it/s, dice_loss - 0.3517, iou_score - 0.6493]\n",
      "\n",
      "Epoch: 283\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.35it/s, dice_loss - 0.2888, iou_score - 0.7134]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  7.91it/s, dice_loss - 0.3564, iou_score - 0.6439]\n",
      "\n",
      "Epoch: 284\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.41it/s, dice_loss - 0.2838, iou_score - 0.7232]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.20it/s, dice_loss - 0.3534, iou_score - 0.6504]\n",
      "\n",
      "Epoch: 285\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.47it/s, dice_loss - 0.2848, iou_score - 0.7211]\n",
      "valid: 100%|██████████████████████████████████████| 4/4 [00:00<00:00,  8.38it/s, dice_loss - 0.3545, iou_score - 0.647]\n",
      "\n",
      "Epoch: 286\n",
      "train: 100%|██████████████████████████████████████| 7/7 [00:02<00:00,  2.41it/s, dice_loss - 0.2864, iou_score - 0.718]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.39it/s, dice_loss - 0.3596, iou_score - 0.6383]\n",
      "\n",
      "Epoch: 287\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.43it/s, dice_loss - 0.2856, iou_score - 0.7175]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.39it/s, dice_loss - 0.3605, iou_score - 0.6392]\n",
      "\n",
      "Epoch: 288\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.48it/s, dice_loss - 0.2832, iou_score - 0.7213]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  7.90it/s, dice_loss - 0.3536, iou_score - 0.6452]\n",
      "\n",
      "Epoch: 289\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.44it/s, dice_loss - 0.2818, iou_score - 0.7209]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.32it/s, dice_loss - 0.3466, iou_score - 0.6558]\n",
      "Model saved!\n",
      "\n",
      "Epoch: 290\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.46it/s, dice_loss - 0.2808, iou_score - 0.7247]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.43it/s, dice_loss - 0.3586, iou_score - 0.6395]\n",
      "\n",
      "Epoch: 291\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.49it/s, dice_loss - 0.2798, iou_score - 0.7275]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  7.43it/s, dice_loss - 0.3551, iou_score - 0.6457]\n",
      "\n",
      "Epoch: 292\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.43it/s, dice_loss - 0.2828, iou_score - 0.7186]\n",
      "valid: 100%|██████████████████████████████████████| 4/4 [00:00<00:00,  8.35it/s, dice_loss - 0.349, iou_score - 0.6496]\n",
      "\n",
      "Epoch: 293\n",
      "train: 100%|██████████████████████████████████████| 7/7 [00:02<00:00,  2.44it/s, dice_loss - 0.282, iou_score - 0.7188]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.41it/s, dice_loss - 0.3556, iou_score - 0.6413]\n",
      "\n",
      "Epoch: 294\n",
      "train: 100%|██████████████████████████████████████| 7/7 [00:02<00:00,  2.48it/s, dice_loss - 0.278, iou_score - 0.7254]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.12it/s, dice_loss - 0.3522, iou_score - 0.6462]\n",
      "\n",
      "Epoch: 295\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.49it/s, dice_loss - 0.2733, iou_score - 0.7329]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.60it/s, dice_loss - 0.3458, iou_score - 0.6548]\n",
      "\n",
      "Epoch: 296\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.48it/s, dice_loss - 0.2761, iou_score - 0.7286]\n",
      "valid: 100%|██████████████████████████████████████| 4/4 [00:00<00:00,  8.63it/s, dice_loss - 0.3492, iou_score - 0.652]\n",
      "\n",
      "Epoch: 297\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.45it/s, dice_loss - 0.2757, iou_score - 0.7287]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.25it/s, dice_loss - 0.3496, iou_score - 0.6504]\n",
      "\n",
      "Epoch: 298\n",
      "train: 100%|████████████████████████████████████████| 7/7 [00:02<00:00,  2.43it/s, dice_loss - 0.281, iou_score - 0.72]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.61it/s, dice_loss - 0.3479, iou_score - 0.6491]\n",
      "\n",
      "Epoch: 299\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.42it/s, dice_loss - 0.2742, iou_score - 0.7301]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.41it/s, dice_loss - 0.3479, iou_score - 0.6489]\n",
      "\n",
      "Epoch: 300\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.45it/s, dice_loss - 0.2735, iou_score - 0.7325]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.36it/s, dice_loss - 0.3452, iou_score - 0.6545]\n",
      "\n",
      "Epoch: 301\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.45it/s, dice_loss - 0.2721, iou_score - 0.7308]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.29it/s, dice_loss - 0.3418, iou_score - 0.6581]\n",
      "Model saved!\n",
      "\n",
      "Epoch: 302\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.43it/s, dice_loss - 0.2708, iou_score - 0.7345]\n",
      "valid: 100%|██████████████████████████████████████| 4/4 [00:00<00:00,  8.02it/s, dice_loss - 0.3449, iou_score - 0.651]\n",
      "\n",
      "Epoch: 303\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.49it/s, dice_loss - 0.2765, iou_score - 0.7236]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  7.88it/s, dice_loss - 0.3432, iou_score - 0.6546]\n",
      "\n",
      "Epoch: 304\n",
      "train: 100%|██████████████████████████████████████| 7/7 [00:02<00:00,  2.46it/s, dice_loss - 0.269, iou_score - 0.7354]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  7.79it/s, dice_loss - 0.3485, iou_score - 0.6485]\n",
      "\n",
      "Epoch: 305\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.44it/s, dice_loss - 0.2692, iou_score - 0.7334]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.31it/s, dice_loss - 0.3431, iou_score - 0.6523]\n",
      "\n",
      "Epoch: 306\n",
      "train: 100%|██████████████████████████████████████| 7/7 [00:02<00:00,  2.40it/s, dice_loss - 0.2695, iou_score - 0.733]\n",
      "valid: 100%|██████████████████████████████████████| 4/4 [00:00<00:00,  8.71it/s, dice_loss - 0.3397, iou_score - 0.658]\n",
      "\n",
      "Epoch: 307\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.44it/s, dice_loss - 0.2692, iou_score - 0.7351]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.34it/s, dice_loss - 0.3377, iou_score - 0.6594]\n",
      "Model saved!\n",
      "\n",
      "Epoch: 308\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.41it/s, dice_loss - 0.2678, iou_score - 0.7345]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.04it/s, dice_loss - 0.3355, iou_score - 0.6611]\n",
      "Model saved!\n",
      "\n",
      "Epoch: 309\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.43it/s, dice_loss - 0.2658, iou_score - 0.7366]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  7.85it/s, dice_loss - 0.3477, iou_score - 0.6464]\n",
      "\n",
      "Epoch: 310\n",
      "train: 100%|██████████████████████████████████████| 7/7 [00:02<00:00,  2.47it/s, dice_loss - 0.2644, iou_score - 0.741]\n",
      "valid: 100%|██████████████████████████████████████| 4/4 [00:00<00:00,  8.44it/s, dice_loss - 0.338, iou_score - 0.6582]\n",
      "\n",
      "Epoch: 311\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.42it/s, dice_loss - 0.2686, iou_score - 0.7328]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  7.35it/s, dice_loss - 0.3422, iou_score - 0.6521]\n",
      "\n",
      "Epoch: 312\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.43it/s, dice_loss - 0.2635, iou_score - 0.7418]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.35it/s, dice_loss - 0.3445, iou_score - 0.6504]\n",
      "\n",
      "Epoch: 313\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.44it/s, dice_loss - 0.2652, iou_score - 0.7377]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.20it/s, dice_loss - 0.3395, iou_score - 0.6562]\n",
      "\n",
      "Epoch: 314\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.46it/s, dice_loss - 0.2636, iou_score - 0.7381]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.58it/s, dice_loss - 0.3369, iou_score - 0.6597]\n",
      "\n",
      "Epoch: 315\n",
      "train: 100%|██████████████████████████████████████| 7/7 [00:02<00:00,  2.45it/s, dice_loss - 0.2617, iou_score - 0.742]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.57it/s, dice_loss - 0.3428, iou_score - 0.6521]\n",
      "\n",
      "Epoch: 316\n",
      "train: 100%|██████████████████████████████████████| 7/7 [00:02<00:00,  2.48it/s, dice_loss - 0.2622, iou_score - 0.743]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.39it/s, dice_loss - 0.3524, iou_score - 0.6397]\n",
      "\n",
      "Epoch: 317\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.45it/s, dice_loss - 0.2622, iou_score - 0.7398]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  6.90it/s, dice_loss - 0.3426, iou_score - 0.6483]\n",
      "\n",
      "Epoch: 318\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.36it/s, dice_loss - 0.2656, iou_score - 0.7337]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.14it/s, dice_loss - 0.3461, iou_score - 0.6458]\n",
      "\n",
      "Epoch: 319\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.39it/s, dice_loss - 0.2556, iou_score - 0.7473]\n",
      "valid: 100%|██████████████████████████████████████| 4/4 [00:00<00:00,  8.39it/s, dice_loss - 0.347, iou_score - 0.6424]\n",
      "\n",
      "Epoch: 320\n",
      "train: 100%|██████████████████████████████████████| 7/7 [00:02<00:00,  2.39it/s, dice_loss - 0.262, iou_score - 0.7389]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  7.84it/s, dice_loss - 0.3428, iou_score - 0.6483]\n",
      "\n",
      "Epoch: 321\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.43it/s, dice_loss - 0.2582, iou_score - 0.7424]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.40it/s, dice_loss - 0.3384, iou_score - 0.6545]\n",
      "\n",
      "Epoch: 322\n",
      "train: 100%|██████████████████████████████████████| 7/7 [00:02<00:00,  2.44it/s, dice_loss - 0.2568, iou_score - 0.746]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  7.74it/s, dice_loss - 0.3413, iou_score - 0.6529]\n",
      "\n",
      "Epoch: 323\n",
      "train: 100%|██████████████████████████████████████| 7/7 [00:02<00:00,  2.46it/s, dice_loss - 0.2586, iou_score - 0.743]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.32it/s, dice_loss - 0.3441, iou_score - 0.6481]\n",
      "\n",
      "Epoch: 324\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.48it/s, dice_loss - 0.2577, iou_score - 0.7435]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.86it/s, dice_loss - 0.3337, iou_score - 0.6571]\n",
      "\n",
      "Epoch: 325\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.42it/s, dice_loss - 0.2553, iou_score - 0.7479]\n",
      "valid: 100%|██████████████████████████████████████| 4/4 [00:00<00:00,  8.85it/s, dice_loss - 0.3358, iou_score - 0.653]\n",
      "\n",
      "Epoch: 326\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.47it/s, dice_loss - 0.2547, iou_score - 0.7495]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.98it/s, dice_loss - 0.3327, iou_score - 0.6582]\n",
      "\n",
      "Epoch: 327\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.47it/s, dice_loss - 0.2516, iou_score - 0.7532]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.57it/s, dice_loss - 0.3355, iou_score - 0.6564]\n",
      "\n",
      "Epoch: 328\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.39it/s, dice_loss - 0.2549, iou_score - 0.7443]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  7.35it/s, dice_loss - 0.3305, iou_score - 0.6595]\n",
      "\n",
      "Epoch: 329\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.40it/s, dice_loss - 0.2528, iou_score - 0.7485]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  7.89it/s, dice_loss - 0.3319, iou_score - 0.6574]\n",
      "\n",
      "Epoch: 330\n",
      "train: 100%|██████████████████████████████████████| 7/7 [00:02<00:00,  2.41it/s, dice_loss - 0.249, iou_score - 0.7538]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.67it/s, dice_loss - 0.3317, iou_score - 0.6575]\n",
      "\n",
      "Epoch: 331\n",
      "train: 100%|██████████████████████████████████████| 7/7 [00:02<00:00,  2.43it/s, dice_loss - 0.252, iou_score - 0.7475]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  7.65it/s, dice_loss - 0.3352, iou_score - 0.6529]\n",
      "\n",
      "Epoch: 332\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.47it/s, dice_loss - 0.2523, iou_score - 0.7481]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.44it/s, dice_loss - 0.3395, iou_score - 0.6489]\n",
      "\n",
      "Epoch: 333\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.41it/s, dice_loss - 0.2505, iou_score - 0.7518]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.40it/s, dice_loss - 0.3415, iou_score - 0.6442]\n",
      "\n",
      "Epoch: 334\n",
      "train: 100%|███████████████████████████████████████| 7/7 [00:02<00:00,  2.44it/s, dice_loss - 0.25, iou_score - 0.7489]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.46it/s, dice_loss - 0.3301, iou_score - 0.6593]\n",
      "\n",
      "Epoch: 335\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.40it/s, dice_loss - 0.2453, iou_score - 0.7563]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.16it/s, dice_loss - 0.3331, iou_score - 0.6553]\n",
      "\n",
      "Epoch: 336\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.49it/s, dice_loss - 0.2489, iou_score - 0.7524]\n",
      "valid: 100%|██████████████████████████████████████| 4/4 [00:00<00:00,  8.20it/s, dice_loss - 0.3346, iou_score - 0.653]\n",
      "\n",
      "Epoch: 337\n",
      "train: 100%|██████████████████████████████████████| 7/7 [00:02<00:00,  2.42it/s, dice_loss - 0.245, iou_score - 0.7586]\n",
      "valid: 100%|██████████████████████████████████████| 4/4 [00:00<00:00,  7.83it/s, dice_loss - 0.3332, iou_score - 0.653]\n",
      "\n",
      "Epoch: 338\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.44it/s, dice_loss - 0.2455, iou_score - 0.7561]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.49it/s, dice_loss - 0.3345, iou_score - 0.6507]\n",
      "\n",
      "Epoch: 339\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.43it/s, dice_loss - 0.2461, iou_score - 0.7541]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  6.96it/s, dice_loss - 0.3319, iou_score - 0.6528]\n",
      "\n",
      "Epoch: 340\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.37it/s, dice_loss - 0.2491, iou_score - 0.7527]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.55it/s, dice_loss - 0.3343, iou_score - 0.6533]\n",
      "\n",
      "Epoch: 341\n",
      "train: 100%|██████████████████████████████████████| 7/7 [00:02<00:00,  2.43it/s, dice_loss - 0.2502, iou_score - 0.749]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.55it/s, dice_loss - 0.3311, iou_score - 0.6556]\n",
      "\n",
      "Epoch: 342\n",
      "train: 100%|██████████████████████████████████████| 7/7 [00:02<00:00,  2.42it/s, dice_loss - 0.242, iou_score - 0.7575]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.19it/s, dice_loss - 0.3324, iou_score - 0.6518]\n",
      "\n",
      "Epoch: 343\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.39it/s, dice_loss - 0.2437, iou_score - 0.7566]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.43it/s, dice_loss - 0.3325, iou_score - 0.6537]\n",
      "\n",
      "Epoch: 344\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.49it/s, dice_loss - 0.2386, iou_score - 0.7662]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.41it/s, dice_loss - 0.3303, iou_score - 0.6564]\n",
      "\n",
      "Epoch: 345\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.48it/s, dice_loss - 0.2401, iou_score - 0.7618]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.38it/s, dice_loss - 0.3346, iou_score - 0.6512]\n",
      "\n",
      "Epoch: 346\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.50it/s, dice_loss - 0.2395, iou_score - 0.7639]\n",
      "valid: 100%|██████████████████████████████████████| 4/4 [00:00<00:00,  8.86it/s, dice_loss - 0.3317, iou_score - 0.654]\n",
      "\n",
      "Epoch: 347\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.46it/s, dice_loss - 0.2374, iou_score - 0.7657]\n",
      "valid: 100%|██████████████████████████████████████| 4/4 [00:00<00:00,  8.43it/s, dice_loss - 0.329, iou_score - 0.6554]\n",
      "\n",
      "Epoch: 348\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.47it/s, dice_loss - 0.2399, iou_score - 0.7612]\n",
      "valid: 100%|██████████████████████████████████████| 4/4 [00:00<00:00,  8.05it/s, dice_loss - 0.329, iou_score - 0.6531]\n",
      "\n",
      "Epoch: 349\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.47it/s, dice_loss - 0.2412, iou_score - 0.7592]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.60it/s, dice_loss - 0.3319, iou_score - 0.6502]\n",
      "\n",
      "Epoch: 350\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.52it/s, dice_loss - 0.2357, iou_score - 0.7667]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.26it/s, dice_loss - 0.3222, iou_score - 0.6623]\n",
      "Model saved!\n",
      "\n",
      "Epoch: 351\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.37it/s, dice_loss - 0.2383, iou_score - 0.7606]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.21it/s, dice_loss - 0.3227, iou_score - 0.6615]\n",
      "\n",
      "Epoch: 352\n",
      "train: 100%|██████████████████████████████████████| 7/7 [00:02<00:00,  2.39it/s, dice_loss - 0.2368, iou_score - 0.763]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  7.78it/s, dice_loss - 0.3263, iou_score - 0.6567]\n",
      "\n",
      "Epoch: 353\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.35it/s, dice_loss - 0.2335, iou_score - 0.7673]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.29it/s, dice_loss - 0.3294, iou_score - 0.6521]\n",
      "\n",
      "Epoch: 354\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.43it/s, dice_loss - 0.2342, iou_score - 0.7691]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.05it/s, dice_loss - 0.3228, iou_score - 0.6619]\n",
      "\n",
      "Epoch: 355\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.35it/s, dice_loss - 0.2355, iou_score - 0.7652]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  7.64it/s, dice_loss - 0.3284, iou_score - 0.6557]\n",
      "\n",
      "Epoch: 356\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.42it/s, dice_loss - 0.2336, iou_score - 0.7663]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.58it/s, dice_loss - 0.3261, iou_score - 0.6553]\n",
      "\n",
      "Epoch: 357\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.42it/s, dice_loss - 0.2335, iou_score - 0.7673]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  7.83it/s, dice_loss - 0.3247, iou_score - 0.6562]\n",
      "\n",
      "Epoch: 358\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.44it/s, dice_loss - 0.2353, iou_score - 0.7636]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.20it/s, dice_loss - 0.3296, iou_score - 0.6496]\n",
      "\n",
      "Epoch: 359\n",
      "train: 100%|███████████████████████████████████████| 7/7 [00:02<00:00,  2.42it/s, dice_loss - 0.23, iou_score - 0.7706]\n",
      "valid: 100%|██████████████████████████████████████| 4/4 [00:00<00:00,  8.54it/s, dice_loss - 0.326, iou_score - 0.6539]\n",
      "\n",
      "Epoch: 360\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.48it/s, dice_loss - 0.2289, iou_score - 0.7723]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  7.79it/s, dice_loss - 0.3172, iou_score - 0.6656]\n",
      "Model saved!\n",
      "\n",
      "Epoch: 361\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.44it/s, dice_loss - 0.2315, iou_score - 0.7674]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.23it/s, dice_loss - 0.3169, iou_score - 0.6671]\n",
      "Model saved!\n",
      "\n",
      "Epoch: 362\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.40it/s, dice_loss - 0.2312, iou_score - 0.7672]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  7.94it/s, dice_loss - 0.3192, iou_score - 0.6623]\n",
      "\n",
      "Epoch: 363\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.47it/s, dice_loss - 0.2306, iou_score - 0.7673]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.65it/s, dice_loss - 0.3187, iou_score - 0.6629]\n",
      "\n",
      "Epoch: 364\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.62it/s, dice_loss - 0.2274, iou_score - 0.7724]\n",
      "valid: 100%|██████████████████████████████████████| 4/4 [00:00<00:00,  9.43it/s, dice_loss - 0.3211, iou_score - 0.659]\n",
      "\n",
      "Epoch: 365\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.53it/s, dice_loss - 0.2279, iou_score - 0.7686]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.18it/s, dice_loss - 0.3114, iou_score - 0.6693]\n",
      "Model saved!\n",
      "\n",
      "Epoch: 366\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.51it/s, dice_loss - 0.2277, iou_score - 0.7703]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  9.06it/s, dice_loss - 0.3148, iou_score - 0.6643]\n",
      "\n",
      "Epoch: 367\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.55it/s, dice_loss - 0.2272, iou_score - 0.7723]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.65it/s, dice_loss - 0.3149, iou_score - 0.6654]\n",
      "\n",
      "Epoch: 368\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.49it/s, dice_loss - 0.2268, iou_score - 0.7738]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.52it/s, dice_loss - 0.3156, iou_score - 0.6649]\n",
      "\n",
      "Epoch: 369\n",
      "train: 100%|██████████████████████████████████████| 7/7 [00:02<00:00,  2.54it/s, dice_loss - 0.2217, iou_score - 0.778]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.98it/s, dice_loss - 0.3234, iou_score - 0.6537]\n",
      "\n",
      "Epoch: 370\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.56it/s, dice_loss - 0.2243, iou_score - 0.7732]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.11it/s, dice_loss - 0.3153, iou_score - 0.6641]\n",
      "\n",
      "Epoch: 371\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.53it/s, dice_loss - 0.2256, iou_score - 0.7714]\n",
      "valid: 100%|██████████████████████████████████████| 4/4 [00:00<00:00,  8.87it/s, dice_loss - 0.3104, iou_score - 0.668]\n",
      "\n",
      "Epoch: 372\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.46it/s, dice_loss - 0.2269, iou_score - 0.7669]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  7.99it/s, dice_loss - 0.3152, iou_score - 0.6615]\n",
      "\n",
      "Epoch: 373\n",
      "train: 100%|██████████████████████████████████████| 7/7 [00:02<00:00,  2.54it/s, dice_loss - 0.2234, iou_score - 0.771]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.69it/s, dice_loss - 0.3153, iou_score - 0.6611]\n",
      "\n",
      "Epoch: 374\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.52it/s, dice_loss - 0.2248, iou_score - 0.7691]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  9.29it/s, dice_loss - 0.3154, iou_score - 0.6592]\n",
      "\n",
      "Epoch: 375\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.54it/s, dice_loss - 0.2219, iou_score - 0.7743]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.40it/s, dice_loss - 0.3109, iou_score - 0.6665]\n",
      "\n",
      "Epoch: 376\n",
      "train: 100%|███████████████████████████████████████| 7/7 [00:02<00:00,  2.56it/s, dice_loss - 0.22, iou_score - 0.7779]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.78it/s, dice_loss - 0.3153, iou_score - 0.6618]\n",
      "\n",
      "Epoch: 377\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.53it/s, dice_loss - 0.2171, iou_score - 0.7803]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  9.19it/s, dice_loss - 0.3145, iou_score - 0.6617]\n",
      "\n",
      "Epoch: 378\n",
      "train: 100%|██████████████████████████████████████| 7/7 [00:02<00:00,  2.53it/s, dice_loss - 0.2195, iou_score - 0.775]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.81it/s, dice_loss - 0.3175, iou_score - 0.6553]\n",
      "\n",
      "Epoch: 379\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.52it/s, dice_loss - 0.2231, iou_score - 0.7695]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.99it/s, dice_loss - 0.3091, iou_score - 0.6645]\n",
      "\n",
      "Epoch: 380\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.52it/s, dice_loss - 0.2203, iou_score - 0.7743]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  9.06it/s, dice_loss - 0.3133, iou_score - 0.6606]\n",
      "\n",
      "Epoch: 381\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.51it/s, dice_loss - 0.2189, iou_score - 0.7786]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  9.00it/s, dice_loss - 0.3146, iou_score - 0.6597]\n",
      "\n",
      "Epoch: 382\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.57it/s, dice_loss - 0.2161, iou_score - 0.7802]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.65it/s, dice_loss - 0.3032, iou_score - 0.6724]\n",
      "Model saved!\n",
      "\n",
      "Epoch: 383\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.54it/s, dice_loss - 0.2178, iou_score - 0.7761]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.27it/s, dice_loss - 0.3131, iou_score - 0.6594]\n",
      "\n",
      "Epoch: 384\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.52it/s, dice_loss - 0.2122, iou_score - 0.7854]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  9.18it/s, dice_loss - 0.3146, iou_score - 0.6581]\n",
      "\n",
      "Epoch: 385\n",
      "train: 100%|██████████████████████████████████████| 7/7 [00:02<00:00,  2.58it/s, dice_loss - 0.214, iou_score - 0.7833]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.39it/s, dice_loss - 0.3082, iou_score - 0.6672]\n",
      "\n",
      "Epoch: 386\n",
      "train: 100%|██████████████████████████████████████| 7/7 [00:02<00:00,  2.60it/s, dice_loss - 0.218, iou_score - 0.7754]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  9.21it/s, dice_loss - 0.3067, iou_score - 0.6682]\n",
      "\n",
      "Epoch: 387\n",
      "train: 100%|██████████████████████████████████████| 7/7 [00:02<00:00,  2.49it/s, dice_loss - 0.2154, iou_score - 0.779]\n",
      "valid: 100%|██████████████████████████████████████| 4/4 [00:00<00:00,  8.01it/s, dice_loss - 0.312, iou_score - 0.6622]\n",
      "\n",
      "Epoch: 388\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.51it/s, dice_loss - 0.2155, iou_score - 0.7779]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  7.65it/s, dice_loss - 0.3133, iou_score - 0.6593]\n",
      "\n",
      "Epoch: 389\n",
      "train: 100%|██████████████████████████████████████| 7/7 [00:02<00:00,  2.51it/s, dice_loss - 0.2123, iou_score - 0.782]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.49it/s, dice_loss - 0.3113, iou_score - 0.6627]\n",
      "\n",
      "Epoch: 390\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.50it/s, dice_loss - 0.2154, iou_score - 0.7768]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  7.39it/s, dice_loss - 0.3102, iou_score - 0.6622]\n",
      "\n",
      "Epoch: 391\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.47it/s, dice_loss - 0.2154, iou_score - 0.7773]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.44it/s, dice_loss - 0.3133, iou_score - 0.6592]\n",
      "\n",
      "Epoch: 392\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.51it/s, dice_loss - 0.2109, iou_score - 0.7831]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.03it/s, dice_loss - 0.3093, iou_score - 0.6598]\n",
      "\n",
      "Epoch: 393\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.53it/s, dice_loss - 0.2106, iou_score - 0.7831]\n",
      "valid: 100%|███████████████████████████████████████| 4/4 [00:00<00:00,  8.98it/s, dice_loss - 0.307, iou_score - 0.666]\n",
      "\n",
      "Epoch: 394\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.57it/s, dice_loss - 0.2068, iou_score - 0.7882]\n",
      "valid: 100%|███████████████████████████████████████| 4/4 [00:00<00:00,  9.13it/s, dice_loss - 0.31, iou_score - 0.6604]\n",
      "\n",
      "Epoch: 395\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.51it/s, dice_loss - 0.2105, iou_score - 0.7833]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  9.28it/s, dice_loss - 0.3109, iou_score - 0.6587]\n",
      "\n",
      "Epoch: 396\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.57it/s, dice_loss - 0.2115, iou_score - 0.7793]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  9.11it/s, dice_loss - 0.3052, iou_score - 0.6661]\n",
      "\n",
      "Epoch: 397\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.54it/s, dice_loss - 0.2146, iou_score - 0.7759]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.87it/s, dice_loss - 0.3071, iou_score - 0.6615]\n",
      "\n",
      "Epoch: 398\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.58it/s, dice_loss - 0.2109, iou_score - 0.7811]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  9.32it/s, dice_loss - 0.3124, iou_score - 0.6545]\n",
      "\n",
      "Epoch: 399\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.49it/s, dice_loss - 0.2096, iou_score - 0.7812]\n",
      "valid: 100%|██████████████████████████████████████| 4/4 [00:00<00:00,  8.74it/s, dice_loss - 0.3068, iou_score - 0.661]\n",
      "\n",
      "Epoch: 400\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.56it/s, dice_loss - 0.2092, iou_score - 0.7813]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  9.02it/s, dice_loss - 0.3015, iou_score - 0.6691]\n",
      "\n",
      "Epoch: 401\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.55it/s, dice_loss - 0.2082, iou_score - 0.7835]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.25it/s, dice_loss - 0.3072, iou_score - 0.6629]\n",
      "\n",
      "Epoch: 402\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.59it/s, dice_loss - 0.2091, iou_score - 0.7818]\n",
      "valid: 100%|██████████████████████████████████████| 4/4 [00:00<00:00,  9.43it/s, dice_loss - 0.3076, iou_score - 0.662]\n",
      "\n",
      "Epoch: 403\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.54it/s, dice_loss - 0.2048, iou_score - 0.7877]\n",
      "valid: 100%|██████████████████████████████████████| 4/4 [00:00<00:00,  9.44it/s, dice_loss - 0.3015, iou_score - 0.669]\n",
      "\n",
      "Epoch: 404\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.50it/s, dice_loss - 0.2094, iou_score - 0.7811]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  7.55it/s, dice_loss - 0.3066, iou_score - 0.6601]\n",
      "\n",
      "Epoch: 405\n",
      "train: 100%|██████████████████████████████████████| 7/7 [00:02<00:00,  2.50it/s, dice_loss - 0.206, iou_score - 0.7854]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  7.91it/s, dice_loss - 0.3086, iou_score - 0.6587]\n",
      "\n",
      "Epoch: 406\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.53it/s, dice_loss - 0.2066, iou_score - 0.7835]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.34it/s, dice_loss - 0.3026, iou_score - 0.6657]\n",
      "\n",
      "Epoch: 407\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:03<00:00,  2.32it/s, dice_loss - 0.2044, iou_score - 0.7876]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.63it/s, dice_loss - 0.2957, iou_score - 0.6726]\n",
      "Model saved!\n",
      "\n",
      "Epoch: 408\n",
      "train: 100%|██████████████████████████████████████| 7/7 [00:02<00:00,  2.49it/s, dice_loss - 0.206, iou_score - 0.7841]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.89it/s, dice_loss - 0.3027, iou_score - 0.6653]\n",
      "\n",
      "Epoch: 409\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.56it/s, dice_loss - 0.2042, iou_score - 0.7871]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  9.34it/s, dice_loss - 0.3038, iou_score - 0.6638]\n",
      "\n",
      "Epoch: 410\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.55it/s, dice_loss - 0.2031, iou_score - 0.7883]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.27it/s, dice_loss - 0.3044, iou_score - 0.6623]\n",
      "\n",
      "Epoch: 411\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.52it/s, dice_loss - 0.2058, iou_score - 0.7834]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.37it/s, dice_loss - 0.3018, iou_score - 0.6663]\n",
      "\n",
      "Epoch: 412\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.52it/s, dice_loss - 0.2023, iou_score - 0.7894]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  9.09it/s, dice_loss - 0.3072, iou_score - 0.6582]\n",
      "\n",
      "Epoch: 413\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.57it/s, dice_loss - 0.2017, iou_score - 0.7877]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  9.20it/s, dice_loss - 0.2988, iou_score - 0.6676]\n",
      "\n",
      "Epoch: 414\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.47it/s, dice_loss - 0.2015, iou_score - 0.7877]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  9.23it/s, dice_loss - 0.3084, iou_score - 0.6567]\n",
      "\n",
      "Epoch: 415\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.49it/s, dice_loss - 0.2016, iou_score - 0.7865]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  9.17it/s, dice_loss - 0.3037, iou_score - 0.6602]\n",
      "\n",
      "Epoch: 416\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.53it/s, dice_loss - 0.2011, iou_score - 0.7874]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  9.12it/s, dice_loss - 0.2928, iou_score - 0.6747]\n",
      "Model saved!\n",
      "\n",
      "Epoch: 417\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.46it/s, dice_loss - 0.1996, iou_score - 0.7891]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.86it/s, dice_loss - 0.2996, iou_score - 0.6658]\n",
      "\n",
      "Epoch: 418\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.43it/s, dice_loss - 0.1966, iou_score - 0.7933]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.17it/s, dice_loss - 0.3049, iou_score - 0.6561]\n",
      "\n",
      "Epoch: 419\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.55it/s, dice_loss - 0.2018, iou_score - 0.7866]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.87it/s, dice_loss - 0.2996, iou_score - 0.6641]\n",
      "\n",
      "Epoch: 420\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.54it/s, dice_loss - 0.1995, iou_score - 0.7884]\n",
      "valid: 100%|██████████████████████████████████████| 4/4 [00:00<00:00,  8.26it/s, dice_loss - 0.303, iou_score - 0.6617]\n",
      "\n",
      "Epoch: 421\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.54it/s, dice_loss - 0.2019, iou_score - 0.7824]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.87it/s, dice_loss - 0.2995, iou_score - 0.6642]\n",
      "\n",
      "Epoch: 422\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.50it/s, dice_loss - 0.1985, iou_score - 0.7893]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.82it/s, dice_loss - 0.3006, iou_score - 0.6619]\n",
      "\n",
      "Epoch: 423\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.44it/s, dice_loss - 0.1981, iou_score - 0.7892]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.70it/s, dice_loss - 0.3011, iou_score - 0.6625]\n",
      "\n",
      "Epoch: 424\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.55it/s, dice_loss - 0.1945, iou_score - 0.7954]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.90it/s, dice_loss - 0.3001, iou_score - 0.6618]\n",
      "\n",
      "Epoch: 425\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.57it/s, dice_loss - 0.1981, iou_score - 0.7885]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.61it/s, dice_loss - 0.2964, iou_score - 0.6689]\n",
      "\n",
      "Epoch: 426\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.45it/s, dice_loss - 0.1967, iou_score - 0.7904]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.81it/s, dice_loss - 0.2981, iou_score - 0.6636]\n",
      "\n",
      "Epoch: 427\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.37it/s, dice_loss - 0.1951, iou_score - 0.7914]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.23it/s, dice_loss - 0.3044, iou_score - 0.6579]\n",
      "\n",
      "Epoch: 428\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.46it/s, dice_loss - 0.1962, iou_score - 0.7881]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.32it/s, dice_loss - 0.2888, iou_score - 0.6751]\n",
      "Model saved!\n",
      "\n",
      "Epoch: 429\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.43it/s, dice_loss - 0.1968, iou_score - 0.7873]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.53it/s, dice_loss - 0.2992, iou_score - 0.6602]\n",
      "\n",
      "Epoch: 430\n",
      "train: 100%|██████████████████████████████████████| 7/7 [00:02<00:00,  2.46it/s, dice_loss - 0.194, iou_score - 0.7921]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  9.21it/s, dice_loss - 0.2891, iou_score - 0.6739]\n",
      "\n",
      "Epoch: 431\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.53it/s, dice_loss - 0.1947, iou_score - 0.7932]\n",
      "valid: 100%|██████████████████████████████████████| 4/4 [00:00<00:00,  9.16it/s, dice_loss - 0.292, iou_score - 0.6725]\n",
      "\n",
      "Epoch: 432\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.53it/s, dice_loss - 0.1928, iou_score - 0.7954]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.90it/s, dice_loss - 0.2946, iou_score - 0.6664]\n",
      "\n",
      "Epoch: 433\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.34it/s, dice_loss - 0.1939, iou_score - 0.7925]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.67it/s, dice_loss - 0.2884, iou_score - 0.6756]\n",
      "Model saved!\n",
      "\n",
      "Epoch: 434\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.44it/s, dice_loss - 0.1956, iou_score - 0.7885]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  9.15it/s, dice_loss - 0.2937, iou_score - 0.6678]\n",
      "\n",
      "Epoch: 435\n",
      "train: 100%|██████████████████████████████████████| 7/7 [00:02<00:00,  2.55it/s, dice_loss - 0.1932, iou_score - 0.793]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  9.08it/s, dice_loss - 0.2963, iou_score - 0.6623]\n",
      "\n",
      "Epoch: 436\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.53it/s, dice_loss - 0.1936, iou_score - 0.7898]\n",
      "valid: 100%|██████████████████████████████████████| 4/4 [00:00<00:00,  9.63it/s, dice_loss - 0.288, iou_score - 0.6746]\n",
      "\n",
      "Epoch: 437\n",
      "train: 100%|██████████████████████████████████████| 7/7 [00:02<00:00,  2.51it/s, dice_loss - 0.1942, iou_score - 0.789]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  9.25it/s, dice_loss - 0.2931, iou_score - 0.6682]\n",
      "\n",
      "Epoch: 438\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.49it/s, dice_loss - 0.1951, iou_score - 0.7882]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.23it/s, dice_loss - 0.2999, iou_score - 0.6583]\n",
      "\n",
      "Epoch: 439\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.49it/s, dice_loss - 0.1885, iou_score - 0.7976]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.63it/s, dice_loss - 0.2909, iou_score - 0.6706]\n",
      "\n",
      "Epoch: 440\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.50it/s, dice_loss - 0.1893, iou_score - 0.7965]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.29it/s, dice_loss - 0.2869, iou_score - 0.6756]\n",
      "Model saved!\n",
      "\n",
      "Epoch: 441\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.53it/s, dice_loss - 0.1881, iou_score - 0.7981]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  9.04it/s, dice_loss - 0.2937, iou_score - 0.6655]\n",
      "\n",
      "Epoch: 442\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.57it/s, dice_loss - 0.1919, iou_score - 0.7914]\n",
      "valid: 100%|██████████████████████████████████████| 4/4 [00:00<00:00,  8.67it/s, dice_loss - 0.2968, iou_score - 0.663]\n",
      "\n",
      "Epoch: 443\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.52it/s, dice_loss - 0.1901, iou_score - 0.7941]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  9.17it/s, dice_loss - 0.2895, iou_score - 0.6695]\n",
      "\n",
      "Epoch: 444\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.56it/s, dice_loss - 0.1879, iou_score - 0.7965]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  9.51it/s, dice_loss - 0.2912, iou_score - 0.6686]\n",
      "\n",
      "Epoch: 445\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.55it/s, dice_loss - 0.1898, iou_score - 0.7932]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  9.30it/s, dice_loss - 0.2915, iou_score - 0.6676]\n",
      "\n",
      "Epoch: 446\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.57it/s, dice_loss - 0.1863, iou_score - 0.7983]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  9.17it/s, dice_loss - 0.2888, iou_score - 0.6695]\n",
      "\n",
      "Epoch: 447\n",
      "train: 100%|██████████████████████████████████████| 7/7 [00:02<00:00,  2.61it/s, dice_loss - 0.1868, iou_score - 0.798]\n",
      "valid: 100%|██████████████████████████████████████| 4/4 [00:00<00:00,  8.77it/s, dice_loss - 0.282, iou_score - 0.6783]\n",
      "Model saved!\n",
      "\n",
      "Epoch: 448\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.58it/s, dice_loss - 0.1889, iou_score - 0.7943]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  9.68it/s, dice_loss - 0.2921, iou_score - 0.6671]\n",
      "\n",
      "Epoch: 449\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.51it/s, dice_loss - 0.1849, iou_score - 0.7992]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  9.01it/s, dice_loss - 0.2909, iou_score - 0.6671]\n",
      "\n",
      "Epoch: 450\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.58it/s, dice_loss - 0.1872, iou_score - 0.7945]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  9.01it/s, dice_loss - 0.2891, iou_score - 0.6666]\n",
      "\n",
      "Epoch: 451\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.54it/s, dice_loss - 0.1856, iou_score - 0.7986]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  9.16it/s, dice_loss - 0.2857, iou_score - 0.6738]\n",
      "\n",
      "Epoch: 452\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.55it/s, dice_loss - 0.1836, iou_score - 0.8014]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.86it/s, dice_loss - 0.2894, iou_score - 0.6658]\n",
      "\n",
      "Epoch: 453\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.56it/s, dice_loss - 0.1845, iou_score - 0.7999]\n",
      "valid: 100%|██████████████████████████████████████| 4/4 [00:00<00:00,  9.30it/s, dice_loss - 0.285, iou_score - 0.6736]\n",
      "\n",
      "Epoch: 454\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.62it/s, dice_loss - 0.1846, iou_score - 0.7984]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.82it/s, dice_loss - 0.2906, iou_score - 0.6656]\n",
      "\n",
      "Epoch: 455\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.52it/s, dice_loss - 0.1859, iou_score - 0.7964]\n",
      "valid: 100%|██████████████████████████████████████| 4/4 [00:00<00:00,  8.50it/s, dice_loss - 0.289, iou_score - 0.6682]\n",
      "\n",
      "Epoch: 456\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.42it/s, dice_loss - 0.1813, iou_score - 0.8024]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  9.28it/s, dice_loss - 0.2922, iou_score - 0.6623]\n",
      "\n",
      "Epoch: 457\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.54it/s, dice_loss - 0.1854, iou_score - 0.7951]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  9.34it/s, dice_loss - 0.2921, iou_score - 0.6623]\n",
      "\n",
      "Epoch: 458\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.59it/s, dice_loss - 0.1873, iou_score - 0.7928]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.92it/s, dice_loss - 0.2835, iou_score - 0.6715]\n",
      "\n",
      "Epoch: 459\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.55it/s, dice_loss - 0.1837, iou_score - 0.7986]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  9.47it/s, dice_loss - 0.2835, iou_score - 0.6739]\n",
      "\n",
      "Epoch: 460\n",
      "train: 100%|██████████████████████████████████████| 7/7 [00:02<00:00,  2.60it/s, dice_loss - 0.185, iou_score - 0.7943]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.83it/s, dice_loss - 0.2922, iou_score - 0.6597]\n",
      "\n",
      "Epoch: 461\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.55it/s, dice_loss - 0.1778, iou_score - 0.8072]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  9.14it/s, dice_loss - 0.2794, iou_score - 0.6768]\n",
      "\n",
      "Epoch: 462\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.45it/s, dice_loss - 0.1839, iou_score - 0.7962]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  9.22it/s, dice_loss - 0.2861, iou_score - 0.6687]\n",
      "\n",
      "Epoch: 463\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.56it/s, dice_loss - 0.1807, iou_score - 0.8014]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  9.09it/s, dice_loss - 0.2874, iou_score - 0.6665]\n",
      "\n",
      "Epoch: 464\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.55it/s, dice_loss - 0.1799, iou_score - 0.8024]\n",
      "valid: 100%|██████████████████████████████████████| 4/4 [00:00<00:00,  8.53it/s, dice_loss - 0.285, iou_score - 0.6695]\n",
      "\n",
      "Epoch: 465\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.59it/s, dice_loss - 0.1798, iou_score - 0.8009]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  9.27it/s, dice_loss - 0.2854, iou_score - 0.6682]\n",
      "\n",
      "Epoch: 466\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.56it/s, dice_loss - 0.1805, iou_score - 0.7998]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  9.07it/s, dice_loss - 0.2821, iou_score - 0.6724]\n",
      "\n",
      "Epoch: 467\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.55it/s, dice_loss - 0.1772, iou_score - 0.8063]\n",
      "valid: 100%|██████████████████████████████████████| 4/4 [00:00<00:00,  8.60it/s, dice_loss - 0.2871, iou_score - 0.665]\n",
      "\n",
      "Epoch: 468\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.45it/s, dice_loss - 0.1751, iou_score - 0.8086]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.73it/s, dice_loss - 0.2885, iou_score - 0.6646]\n",
      "\n",
      "Epoch: 469\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.59it/s, dice_loss - 0.1776, iou_score - 0.8038]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.53it/s, dice_loss - 0.2841, iou_score - 0.6687]\n",
      "\n",
      "Epoch: 470\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.53it/s, dice_loss - 0.1774, iou_score - 0.8033]\n",
      "valid: 100%|██████████████████████████████████████| 4/4 [00:00<00:00,  9.14it/s, dice_loss - 0.289, iou_score - 0.6628]\n",
      "\n",
      "Epoch: 471\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.50it/s, dice_loss - 0.1777, iou_score - 0.8008]\n",
      "valid: 100%|██████████████████████████████████████| 4/4 [00:00<00:00,  8.65it/s, dice_loss - 0.2834, iou_score - 0.668]\n",
      "\n",
      "Epoch: 472\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.55it/s, dice_loss - 0.1787, iou_score - 0.7987]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  9.47it/s, dice_loss - 0.2864, iou_score - 0.6633]\n",
      "\n",
      "Epoch: 473\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.61it/s, dice_loss - 0.1767, iou_score - 0.8035]\n",
      "valid: 100%|██████████████████████████████████████| 4/4 [00:00<00:00,  9.36it/s, dice_loss - 0.2858, iou_score - 0.665]\n",
      "\n",
      "Epoch: 474\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.59it/s, dice_loss - 0.1768, iou_score - 0.8019]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  9.62it/s, dice_loss - 0.2862, iou_score - 0.6653]\n",
      "\n",
      "Epoch: 475\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.54it/s, dice_loss - 0.1756, iou_score - 0.8034]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.27it/s, dice_loss - 0.2841, iou_score - 0.6663]\n",
      "\n",
      "Epoch: 476\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.56it/s, dice_loss - 0.1757, iou_score - 0.8035]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.59it/s, dice_loss - 0.2793, iou_score - 0.6711]\n",
      "\n",
      "Epoch: 477\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.55it/s, dice_loss - 0.1779, iou_score - 0.8005]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  9.08it/s, dice_loss - 0.2898, iou_score - 0.6599]\n",
      "\n",
      "Epoch: 478\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.52it/s, dice_loss - 0.1791, iou_score - 0.7995]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.66it/s, dice_loss - 0.2838, iou_score - 0.6666]\n",
      "\n",
      "Epoch: 479\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.55it/s, dice_loss - 0.1759, iou_score - 0.8043]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.30it/s, dice_loss - 0.2817, iou_score - 0.6702]\n",
      "\n",
      "Epoch: 480\n",
      "train: 100%|██████████████████████████████████████| 7/7 [00:02<00:00,  2.40it/s, dice_loss - 0.172, iou_score - 0.8087]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.91it/s, dice_loss - 0.2842, iou_score - 0.6674]\n",
      "\n",
      "Epoch: 481\n",
      "train: 100%|██████████████████████████████████████| 7/7 [00:03<00:00,  2.33it/s, dice_loss - 0.1751, iou_score - 0.804]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.08it/s, dice_loss - 0.2868, iou_score - 0.6635]\n",
      "\n",
      "Epoch: 482\n",
      "train: 100%|██████████████████████████████████████| 7/7 [00:02<00:00,  2.51it/s, dice_loss - 0.175, iou_score - 0.8029]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.92it/s, dice_loss - 0.2783, iou_score - 0.6728]\n",
      "\n",
      "Epoch: 483\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.56it/s, dice_loss - 0.1752, iou_score - 0.8012]\n",
      "valid: 100%|███████████████████████████████████████| 4/4 [00:00<00:00,  8.77it/s, dice_loss - 0.28, iou_score - 0.6696]\n",
      "\n",
      "Epoch: 484\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.48it/s, dice_loss - 0.1738, iou_score - 0.8022]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  7.61it/s, dice_loss - 0.2771, iou_score - 0.6723]\n",
      "\n",
      "Epoch: 485\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.45it/s, dice_loss - 0.1762, iou_score - 0.7987]\n",
      "valid: 100%|██████████████████████████████████████| 4/4 [00:00<00:00,  8.95it/s, dice_loss - 0.2783, iou_score - 0.671]\n",
      "\n",
      "Epoch: 486\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.51it/s, dice_loss - 0.1699, iou_score - 0.8088]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.72it/s, dice_loss - 0.2799, iou_score - 0.6684]\n",
      "\n",
      "Epoch: 487\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.50it/s, dice_loss - 0.1751, iou_score - 0.8003]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.91it/s, dice_loss - 0.2768, iou_score - 0.6754]\n",
      "\n",
      "Epoch: 488\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.41it/s, dice_loss - 0.1731, iou_score - 0.8053]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.42it/s, dice_loss - 0.2792, iou_score - 0.6715]\n",
      "\n",
      "Epoch: 489\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.47it/s, dice_loss - 0.1714, iou_score - 0.8071]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.47it/s, dice_loss - 0.2748, iou_score - 0.6772]\n",
      "\n",
      "Epoch: 490\n",
      "train: 100%|██████████████████████████████████████| 7/7 [00:02<00:00,  2.47it/s, dice_loss - 0.1712, iou_score - 0.807]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.63it/s, dice_loss - 0.2818, iou_score - 0.6668]\n",
      "\n",
      "Epoch: 491\n",
      "train: 100%|████████████████████████████████████████| 7/7 [00:02<00:00,  2.49it/s, dice_loss - 0.1755, iou_score - 0.8]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.61it/s, dice_loss - 0.2831, iou_score - 0.6638]\n",
      "\n",
      "Epoch: 492\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.48it/s, dice_loss - 0.1709, iou_score - 0.8053]\n",
      "valid: 100%|██████████████████████████████████████| 4/4 [00:00<00:00,  8.30it/s, dice_loss - 0.275, iou_score - 0.6753]\n",
      "\n",
      "Epoch: 493\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.51it/s, dice_loss - 0.1699, iou_score - 0.8072]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.95it/s, dice_loss - 0.2816, iou_score - 0.6649]\n",
      "\n",
      "Epoch: 494\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.51it/s, dice_loss - 0.1714, iou_score - 0.8054]\n",
      "valid: 100%|██████████████████████████████████████| 4/4 [00:00<00:00,  8.71it/s, dice_loss - 0.273, iou_score - 0.6765]\n",
      "\n",
      "Epoch: 495\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.54it/s, dice_loss - 0.1677, iou_score - 0.8106]\n",
      "valid: 100%|██████████████████████████████████████| 4/4 [00:00<00:00,  9.37it/s, dice_loss - 0.281, iou_score - 0.6674]\n",
      "\n",
      "Epoch: 496\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.55it/s, dice_loss - 0.1699, iou_score - 0.8079]\n",
      "valid: 100%|██████████████████████████████████████| 4/4 [00:00<00:00,  9.14it/s, dice_loss - 0.2796, iou_score - 0.667]\n",
      "\n",
      "Epoch: 497\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.57it/s, dice_loss - 0.1683, iou_score - 0.8077]\n",
      "valid: 100%|██████████████████████████████████████| 4/4 [00:00<00:00,  9.02it/s, dice_loss - 0.2706, iou_score - 0.676]\n",
      "\n",
      "Epoch: 498\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.50it/s, dice_loss - 0.1695, iou_score - 0.8078]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.37it/s, dice_loss - 0.2738, iou_score - 0.6743]\n",
      "\n",
      "Epoch: 499\n",
      "train: 100%|█████████████████████████████████████| 7/7 [00:02<00:00,  2.45it/s, dice_loss - 0.1686, iou_score - 0.8083]\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.85it/s, dice_loss - 0.2791, iou_score - 0.6677]\n"
     ]
    }
   ],
   "source": [
    "# Training \n",
    "if TRAINING:\n",
    "\n",
    "    best_iou_score = 0.0\n",
    "    train_logs_list, valid_logs_list = [], []\n",
    "\n",
    "    for i in range(0, EPOCHS):\n",
    "\n",
    "        # update the learning rate\n",
    "        \"\"\"\n",
    "        lr_scheduler.step(i)\n",
    "        for param_group in optimizer.param_groups:\n",
    "            print(\"LEARNING RATE: \", param_group['lr'])\n",
    "            usedLr = float(param_group['lr'])\n",
    "        \"\"\"\n",
    "\n",
    "        # Perform training & validation\n",
    "        print('\\nEpoch: {}'.format(i))\n",
    "        train_logs = train_epoch.run(train_loader)\n",
    "        valid_logs = valid_epoch.run(valid_loader)\n",
    "        train_logs_list.append(train_logs)\n",
    "        valid_logs_list.append(valid_logs)\n",
    "\n",
    "        # Save model if a better val IoU score is obtained\n",
    "        if best_iou_score < valid_logs['iou_score']:\n",
    "            best_iou_score = valid_logs['iou_score']\n",
    "            torch.save(model, 'best_model_current_run/best_model.pth')\n",
    "            print('Model saved!')\n",
    "\n",
    "        # evaluate on the test dataset\n",
    "        # evaluate(model, data_loader_test, device=device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot Dice Loss & IoU Metric for Train vs. Val\n",
    "train_logs_df = pd.DataFrame(train_logs_list)\n",
    "valid_logs_df = pd.DataFrame(valid_logs_list)\n",
    "\n",
    "plt.figure(figsize=(20,8))\n",
    "plt.plot(train_logs_df.index.tolist(), train_logs_df.iou_score.tolist(), lw=3, label = 'Train')\n",
    "plt.plot(valid_logs_df.index.tolist(), valid_logs_df.iou_score.tolist(), lw=3, label = 'Valid')\n",
    "plt.xlabel('Epochs', fontsize=20)\n",
    "plt.ylabel('IoU Score', fontsize=20)\n",
    "plt.title('IoU Score Plot', fontsize=20)\n",
    "plt.legend(loc='best', fontsize=16)\n",
    "plt.grid()\n",
    "plt.savefig('iou_score_plot.png')\n",
    "plt.show()\n",
    "\n",
    "plt.figure(figsize=(20,8))\n",
    "plt.plot(train_logs_df.index.tolist(), train_logs_df.dice_loss.tolist(), lw=3, label = 'Train')\n",
    "plt.plot(valid_logs_df.index.tolist(), valid_logs_df.dice_loss.tolist(), lw=3, label = 'Valid')\n",
    "plt.xlabel('Epochs', fontsize=20)\n",
    "plt.ylabel('Dice Loss', fontsize=20)\n",
    "plt.title('Dice Loss Plot', fontsize=20)\n",
    "plt.legend(loc='best', fontsize=16)\n",
    "plt.grid()\n",
    "plt.savefig('dice_loss_plot.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "background_save": true
    },
    "id": "orwxB8QBcBTL",
    "outputId": "fe55fef5-f87f-45e0-b891-6d7a79f15a7d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loaded model from this run.\n"
     ]
    }
   ],
   "source": [
    "# Prediction on Test Data\n",
    "# load best saved model checkpoint from the current run\n",
    "if os.path.exists('best_model_current_run/best_model.pth'):\n",
    "    best_model = torch.load('best_model_current_run/best_model.pth', map_location=DEVICE)\n",
    "    print('Loaded model from this run.')\n",
    "\n",
    "# load best saved model checkpoint from previous commit (if present)\n",
    "elif os.path.exists('best_model_previous_run/best_model.pth'):\n",
    "    best_model = torch.load('best_model_previous_run/best_model.pth', map_location=DEVICE)\n",
    "    print('Loaded model from a previous commit.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "background_save": true
    },
    "id": "Us7WY_q8cFf9",
    "outputId": "bf233715-286e-4751-8a25-0d0a8c4f2d01"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "len_test_dataset:  66\n",
      "valid: 100%|█████████████████████████████████████| 4/4 [00:00<00:00,  8.80it/s, dice_loss - 0.2773, iou_score - 0.6742]\n",
      "Evaluation on Test Data: \n",
      "Mean IoU Score: 0.6742\n",
      "Mean Dice Loss: 0.2773\n"
     ]
    }
   ],
   "source": [
    "# create test dataloader (with preprocessing operation: to_tensor(...))\n",
    "test_dataset = BuildingsDataset(\n",
    "    x_test_dir, y_test_dir, \n",
    "    augmentation= get_validation_augmentation(), \n",
    "    class_rgb_values=select_class_rgb_values)\n",
    "\n",
    "print('len_test_dataset: ',len(test_dataset))\n",
    "\n",
    "# Get test loaders\n",
    "test_loader = DataLoader(test_dataset, batch_size=16, shuffle=False, drop_last=True)\n",
    "\n",
    "# Model Evaluation on Test Dataset\n",
    "test_epoch = smp.utils.train.ValidEpoch(\n",
    "    model,\n",
    "    loss=loss, \n",
    "    metrics=metrics, \n",
    "    device=DEVICE,\n",
    "    verbose=True,\n",
    ")\n",
    "\n",
    "valid_logs = test_epoch.run(test_loader)\n",
    "print(\"Evaluation on Test Data: \")\n",
    "print(f\"Mean IoU Score: {valid_logs['iou_score']:.4f}\")\n",
    "print(f\"Mean Dice Loss: {valid_logs['dice_loss']:.4f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "background_save": true
    },
    "id": "-9EVxRppcag4",
    "outputId": "6aa75278-66f7-4049-8378-6b8f11477361"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original Images\n",
      "GT Images\n",
      "Output Images\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Batch of train images\n",
    "dataiter = iter(test_loader)\n",
    "imgs = dataiter.next()\n",
    "images, GT = imgs\n",
    "        \n",
    "best_model.eval()\n",
    "with torch.no_grad():\n",
    "    #Sample outputs\n",
    "    output = best_model(images.to(DEVICE))\n",
    "\n",
    "    output = output.cpu()\n",
    "    output = output.view(16, 6, 128, 128)\n",
    "    output = output.detach().numpy()\n",
    "\n",
    "    #Original Images\n",
    "    print(\"Original Images\")\n",
    "    fig, axes = plt.subplots(nrows=1, ncols=4, sharex=True, sharey=True, figsize=(20,5))\n",
    "    for idx in np.arange(4):\n",
    "        ax = fig.add_subplot(1, 4, idx+1, xticks=[], yticks=[])\n",
    "        ax.imshow(np.transpose(np.squeeze(images.cpu().detach().numpy()[idx]),(1, 2, 0)))\n",
    "        \n",
    "    #GT Images\n",
    "    print(\"GT Images\")\n",
    "    fig, axes = plt.subplots(nrows=1, ncols=4, sharex=True, sharey=True, figsize=(20,5))\n",
    "    for idx in np.arange(4):\n",
    "        ax = fig.add_subplot(1, 4, idx+1, xticks=[], yticks=[])\n",
    "        ax.imshow(colour_code_segmentation(reverse_one_hot(np.transpose(np.squeeze(GT.cpu().detach().numpy()[idx]),(1, 2, 0))),select_class_rgb_values))\n",
    "\n",
    "    #OutputImages\n",
    "    print(\"Output Images\")\n",
    "    fig, axes = plt.subplots(nrows=1, ncols=4, sharex=True, sharey=True, figsize=(20,5))\n",
    "    for idx in np.arange(4):\n",
    "        ax = fig.add_subplot(1, 4, idx+1, xticks=[], yticks=[])\n",
    "        ax.imshow(colour_code_segmentation(reverse_one_hot(np.transpose(np.squeeze(output[idx]),(1, 2, 0))),select_class_rgb_values))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "collapsed_sections": [],
   "name": "Segmentation_PyTorch_Unet++ _ResNet34).ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}