Spineweb_UNet++_smp_resnest200e.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/wangjie/anaconda3/envs/dataEngineering/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    }
   ],
   "source": [
    "import nibabel as nib\n",
    "import matplotlib.pyplot as plt\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "from torch.utils.data import Dataset\n",
    "from torch.utils.data import DataLoader\n",
    "from torchvision.transforms import ToTensor\n",
    "import torchmetrics.functional as evafunc\n",
    "import torchvision.transforms as transforms\n",
    "import numpy as np\n",
    "import torchvision.models as models\n",
    "from segmentation_models_pytorch.losses import DiceLoss, FocalLoss\n",
    "import segmentation_models_pytorch as smp\n",
    "import os\n",
    "os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"3\"\n",
    "from PIL import Image\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 自定义数据集类\n",
    "class SpineWeb15(Dataset):\n",
    "    def __init__(self, data_path,transform1=None,transform2=None):\n",
    "        self.data_path = data_path\n",
    "        self.transform1 = transform1\n",
    "        self.transform2 = transform2\n",
    "        \n",
    "    def __len__(self):\n",
    "        file_list = os.listdir(self.data_path)  # 列出文件夹中的所有文件和文件夹\n",
    "        file_count = len(file_list)  # 获取文件数量\n",
    "        return file_count\n",
    "    \n",
    "    def __getitem__(self, index):\n",
    "        metadata = np.load(self.data_path+str(index)+\".npy\")\n",
    "        image = metadata[0]\n",
    "        label = metadata[1]\n",
    "\n",
    "        # 找到数组中的最小值和最大值\n",
    "        min_value = np.min(image)\n",
    "        max_value = np.max(image)\n",
    "\n",
    "        # # 将数组元素限制在0到255之间（类似于归一化）\n",
    "        image = (image - min_value) * (255 / (max_value - min_value))\n",
    "\n",
    "        image_data = Image.fromarray(image)\n",
    "        label_data = Image.fromarray(label)\n",
    "        # image_data = torch.from_numpy(image).float()\n",
    "        # label_data = torch.from_numpy(label).float()\n",
    "        \n",
    "        if self.transform1:\n",
    "            image_data = self.transform1(image_data)\n",
    "        if self.transform2:\n",
    "            label_data = self.transform2(label_data)\n",
    "        return image_data, label_data\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculate_pixel_accuracy(prediction, target):\n",
    "    # 将预测结果和目标标签进行逐像素比较，并计算正确预测像素的比例\n",
    "    correct_pixels = (prediction == target).sum().item()\n",
    "    total_pixels = target.numel()\n",
    "    return correct_pixels, total_pixels\n",
    "\n",
    "def calculate_iou(prediction, target):\n",
    "    intersection = torch.logical_and(prediction, target).sum()\n",
    "    union = torch.logical_or(prediction, target).sum()\n",
    "    iou = intersection / union\n",
    "    return iou.item()\n",
    "\n",
    "def calculate_dice(prediction, target):\n",
    "    intersection = torch.logical_and(prediction, target).sum()\n",
    "    dice = (2 * intersection) / (prediction.sum() + target.sum())\n",
    "    return dice.item()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [1/200], Avg Loss: 0.01193, Avg IoU: 0.5959, Avg Dice: 0.7160,Test IoU: 0.6361, Test Dice: 0.7584, Train Acc: 0.9787, Test Acc: 0.9842\n",
      "Epoch [2/200], Avg Loss: 0.01093, Avg IoU: 0.7370, Avg Dice: 0.8289,Test IoU: 0.7419, Test Dice: 0.8366, Train Acc: 0.9906, Test Acc: 0.9892\n",
      "Epoch [3/200], Avg Loss: 0.01085, Avg IoU: 0.7988, Avg Dice: 0.8720,Test IoU: 0.8012, Test Dice: 0.8752, Train Acc: 0.9932, Test Acc: 0.9919\n",
      "Epoch [4/200], Avg Loss: 0.01083, Avg IoU: 0.8332, Avg Dice: 0.8943,Test IoU: 0.7856, Test Dice: 0.8665, Train Acc: 0.9946, Test Acc: 0.9911\n",
      "Epoch [5/200], Avg Loss: 0.01082, Avg IoU: 0.8444, Avg Dice: 0.9006,Test IoU: 0.7741, Test Dice: 0.8585, Train Acc: 0.9950, Test Acc: 0.9907\n",
      "Epoch [6/200], Avg Loss: 0.01082, Avg IoU: 0.8501, Avg Dice: 0.9042,Test IoU: 0.7461, Test Dice: 0.8392, Train Acc: 0.9953, Test Acc: 0.9889\n",
      "Epoch [7/200], Avg Loss: 0.01082, Avg IoU: 0.8269, Avg Dice: 0.8885,Test IoU: 0.7900, Test Dice: 0.8679, Train Acc: 0.9943, Test Acc: 0.9915\n",
      "Epoch [8/200], Avg Loss: 0.01081, Avg IoU: 0.8407, Avg Dice: 0.8976,Test IoU: 0.8511, Test Dice: 0.9060, Train Acc: 0.9949, Test Acc: 0.9943\n",
      "Epoch [9/200], Avg Loss: 0.01080, Avg IoU: 0.8650, Avg Dice: 0.9128,Test IoU: 0.8486, Test Dice: 0.9043, Train Acc: 0.9959, Test Acc: 0.9942\n",
      "Epoch [10/200], Avg Loss: 0.01080, Avg IoU: 0.8758, Avg Dice: 0.9201,Test IoU: 0.8494, Test Dice: 0.9046, Train Acc: 0.9963, Test Acc: 0.9942\n",
      "Epoch [11/200], Avg Loss: 0.01080, Avg IoU: 0.8729, Avg Dice: 0.9182,Test IoU: 0.8154, Test Dice: 0.8847, Train Acc: 0.9962, Test Acc: 0.9927\n",
      "Epoch [12/200], Avg Loss: 0.01080, Avg IoU: 0.8811, Avg Dice: 0.9238,Test IoU: 0.8313, Test Dice: 0.8943, Train Acc: 0.9965, Test Acc: 0.9933\n",
      "Epoch [13/200], Avg Loss: 0.01080, Avg IoU: 0.8731, Avg Dice: 0.9185,Test IoU: 0.8487, Test Dice: 0.9047, Train Acc: 0.9962, Test Acc: 0.9941\n",
      "Epoch [14/200], Avg Loss: 0.01080, Avg IoU: 0.8857, Avg Dice: 0.9263,Test IoU: 0.8348, Test Dice: 0.8968, Train Acc: 0.9967, Test Acc: 0.9934\n",
      "Epoch [15/200], Avg Loss: 0.01079, Avg IoU: 0.8828, Avg Dice: 0.9247,Test IoU: 0.8568, Test Dice: 0.9094, Train Acc: 0.9966, Test Acc: 0.9945\n",
      "Epoch [16/200], Avg Loss: 0.01079, Avg IoU: 0.8947, Avg Dice: 0.9314,Test IoU: 0.8719, Test Dice: 0.9179, Train Acc: 0.9970, Test Acc: 0.9954\n",
      "Epoch [17/200], Avg Loss: 0.01079, Avg IoU: 0.8976, Avg Dice: 0.9341,Test IoU: 0.8583, Test Dice: 0.9100, Train Acc: 0.9971, Test Acc: 0.9947\n",
      "Epoch [18/200], Avg Loss: 0.01079, Avg IoU: 0.8925, Avg Dice: 0.9307,Test IoU: 0.8731, Test Dice: 0.9189, Train Acc: 0.9969, Test Acc: 0.9954\n",
      "Epoch [19/200], Avg Loss: 0.01079, Avg IoU: 0.9003, Avg Dice: 0.9354,Test IoU: 0.8632, Test Dice: 0.9134, Train Acc: 0.9972, Test Acc: 0.9948\n",
      "Epoch [20/200], Avg Loss: 0.01079, Avg IoU: 0.9026, Avg Dice: 0.9369,Test IoU: 0.8626, Test Dice: 0.9132, Train Acc: 0.9973, Test Acc: 0.9948\n",
      "Epoch [21/200], Avg Loss: 0.01079, Avg IoU: 0.9086, Avg Dice: 0.9403,Test IoU: 0.8689, Test Dice: 0.9168, Train Acc: 0.9975, Test Acc: 0.9951\n",
      "Epoch [22/200], Avg Loss: 0.01079, Avg IoU: 0.9060, Avg Dice: 0.9391,Test IoU: 0.8359, Test Dice: 0.8975, Train Acc: 0.9974, Test Acc: 0.9935\n",
      "Epoch [23/200], Avg Loss: 0.01079, Avg IoU: 0.8968, Avg Dice: 0.9337,Test IoU: 0.8726, Test Dice: 0.9184, Train Acc: 0.9971, Test Acc: 0.9953\n",
      "Epoch [24/200], Avg Loss: 0.01079, Avg IoU: 0.9090, Avg Dice: 0.9408,Test IoU: 0.8311, Test Dice: 0.8941, Train Acc: 0.9975, Test Acc: 0.9932\n",
      "Epoch [25/200], Avg Loss: 0.01079, Avg IoU: 0.8739, Avg Dice: 0.9184,Test IoU: 0.8644, Test Dice: 0.9134, Train Acc: 0.9962, Test Acc: 0.9948\n",
      "Epoch [26/200], Avg Loss: 0.01079, Avg IoU: 0.8935, Avg Dice: 0.9308,Test IoU: 0.8615, Test Dice: 0.9118, Train Acc: 0.9970, Test Acc: 0.9946\n",
      "Epoch [27/200], Avg Loss: 0.01079, Avg IoU: 0.8986, Avg Dice: 0.9342,Test IoU: 0.8603, Test Dice: 0.9115, Train Acc: 0.9972, Test Acc: 0.9946\n",
      "Epoch [28/200], Avg Loss: 0.01079, Avg IoU: 0.9018, Avg Dice: 0.9360,Test IoU: 0.8517, Test Dice: 0.9063, Train Acc: 0.9973, Test Acc: 0.9942\n",
      "Epoch [29/200], Avg Loss: 0.01079, Avg IoU: 0.9091, Avg Dice: 0.9406,Test IoU: 0.8783, Test Dice: 0.9214, Train Acc: 0.9975, Test Acc: 0.9956\n",
      "Epoch [30/200], Avg Loss: 0.01079, Avg IoU: 0.9144, Avg Dice: 0.9438,Test IoU: 0.8344, Test Dice: 0.8968, Train Acc: 0.9977, Test Acc: 0.9934\n",
      "Epoch [31/200], Avg Loss: 0.01079, Avg IoU: 0.9076, Avg Dice: 0.9401,Test IoU: 0.8544, Test Dice: 0.9079, Train Acc: 0.9974, Test Acc: 0.9944\n",
      "Epoch [32/200], Avg Loss: 0.01079, Avg IoU: 0.9185, Avg Dice: 0.9462,Test IoU: 0.8478, Test Dice: 0.9043, Train Acc: 0.9979, Test Acc: 0.9941\n",
      "Epoch [33/200], Avg Loss: 0.01078, Avg IoU: 0.9213, Avg Dice: 0.9477,Test IoU: 0.8692, Test Dice: 0.9164, Train Acc: 0.9980, Test Acc: 0.9951\n",
      "Epoch [34/200], Avg Loss: 0.01079, Avg IoU: 0.9228, Avg Dice: 0.9486,Test IoU: 0.8321, Test Dice: 0.8949, Train Acc: 0.9980, Test Acc: 0.9933\n",
      "Epoch [35/200], Avg Loss: 0.01079, Avg IoU: 0.9043, Avg Dice: 0.9384,Test IoU: 0.8666, Test Dice: 0.9152, Train Acc: 0.9973, Test Acc: 0.9951\n",
      "Epoch [36/200], Avg Loss: 0.01078, Avg IoU: 0.9194, Avg Dice: 0.9467,Test IoU: 0.8701, Test Dice: 0.9173, Train Acc: 0.9979, Test Acc: 0.9952\n",
      "Epoch [37/200], Avg Loss: 0.01079, Avg IoU: 0.9176, Avg Dice: 0.9457,Test IoU: 0.8593, Test Dice: 0.9109, Train Acc: 0.9978, Test Acc: 0.9946\n",
      "Epoch [38/200], Avg Loss: 0.01078, Avg IoU: 0.9242, Avg Dice: 0.9494,Test IoU: 0.8679, Test Dice: 0.9158, Train Acc: 0.9981, Test Acc: 0.9950\n",
      "Epoch [39/200], Avg Loss: 0.01078, Avg IoU: 0.9253, Avg Dice: 0.9500,Test IoU: 0.8695, Test Dice: 0.9168, Train Acc: 0.9981, Test Acc: 0.9951\n",
      "Epoch [40/200], Avg Loss: 0.01078, Avg IoU: 0.9259, Avg Dice: 0.9505,Test IoU: 0.8652, Test Dice: 0.9144, Train Acc: 0.9981, Test Acc: 0.9949\n",
      "Epoch [41/200], Avg Loss: 0.01078, Avg IoU: 0.9283, Avg Dice: 0.9516,Test IoU: 0.8830, Test Dice: 0.9245, Train Acc: 0.9982, Test Acc: 0.9959\n",
      "Epoch [42/200], Avg Loss: 0.01078, Avg IoU: 0.9297, Avg Dice: 0.9528,Test IoU: 0.8567, Test Dice: 0.9097, Train Acc: 0.9982, Test Acc: 0.9945\n",
      "Epoch [43/200], Avg Loss: 0.01079, Avg IoU: 0.9132, Avg Dice: 0.9431,Test IoU: 0.8567, Test Dice: 0.9087, Train Acc: 0.9977, Test Acc: 0.9945\n",
      "Epoch [44/200], Avg Loss: 0.01078, Avg IoU: 0.9224, Avg Dice: 0.9486,Test IoU: 0.8705, Test Dice: 0.9172, Train Acc: 0.9980, Test Acc: 0.9952\n",
      "Epoch [45/200], Avg Loss: 0.01078, Avg IoU: 0.9276, Avg Dice: 0.9513,Test IoU: 0.8702, Test Dice: 0.9171, Train Acc: 0.9982, Test Acc: 0.9952\n",
      "Epoch [46/200], Avg Loss: 0.01078, Avg IoU: 0.9340, Avg Dice: 0.9552,Test IoU: 0.8676, Test Dice: 0.9157, Train Acc: 0.9984, Test Acc: 0.9951\n",
      "Epoch [47/200], Avg Loss: 0.01078, Avg IoU: 0.9358, Avg Dice: 0.9558,Test IoU: 0.8592, Test Dice: 0.9107, Train Acc: 0.9985, Test Acc: 0.9947\n",
      "Epoch [48/200], Avg Loss: 0.01078, Avg IoU: 0.9360, Avg Dice: 0.9561,Test IoU: 0.8663, Test Dice: 0.9148, Train Acc: 0.9985, Test Acc: 0.9950\n",
      "Epoch [49/200], Avg Loss: 0.01078, Avg IoU: 0.9398, Avg Dice: 0.9582,Test IoU: 0.8505, Test Dice: 0.9059, Train Acc: 0.9986, Test Acc: 0.9943\n",
      "Epoch [50/200], Avg Loss: 0.01078, Avg IoU: 0.9256, Avg Dice: 0.9501,Test IoU: 0.8710, Test Dice: 0.9175, Train Acc: 0.9981, Test Acc: 0.9953\n",
      "Epoch [51/200], Avg Loss: 0.01078, Avg IoU: 0.9336, Avg Dice: 0.9545,Test IoU: 0.8794, Test Dice: 0.9222, Train Acc: 0.9984, Test Acc: 0.9956\n",
      "Epoch [52/200], Avg Loss: 0.01078, Avg IoU: 0.9337, Avg Dice: 0.9549,Test IoU: 0.8770, Test Dice: 0.9210, Train Acc: 0.9984, Test Acc: 0.9955\n",
      "Epoch [53/200], Avg Loss: 0.01078, Avg IoU: 0.9391, Avg Dice: 0.9578,Test IoU: 0.8680, Test Dice: 0.9161, Train Acc: 0.9986, Test Acc: 0.9951\n",
      "Epoch [54/200], Avg Loss: 0.01080, Avg IoU: 0.8807, Avg Dice: 0.9222,Test IoU: 0.8501, Test Dice: 0.9037, Train Acc: 0.9964, Test Acc: 0.9938\n",
      "Epoch [55/200], Avg Loss: 0.01080, Avg IoU: 0.8886, Avg Dice: 0.9272,Test IoU: 0.8434, Test Dice: 0.8969, Train Acc: 0.9967, Test Acc: 0.9938\n",
      "Epoch [56/200], Avg Loss: 0.01079, Avg IoU: 0.9000, Avg Dice: 0.9344,Test IoU: 0.8692, Test Dice: 0.9158, Train Acc: 0.9972, Test Acc: 0.9952\n",
      "Epoch [57/200], Avg Loss: 0.01079, Avg IoU: 0.9060, Avg Dice: 0.9386,Test IoU: 0.8711, Test Dice: 0.9179, Train Acc: 0.9975, Test Acc: 0.9954\n",
      "Epoch [58/200], Avg Loss: 0.01079, Avg IoU: 0.9161, Avg Dice: 0.9447,Test IoU: 0.8437, Test Dice: 0.9021, Train Acc: 0.9978, Test Acc: 0.9940\n",
      "Epoch [59/200], Avg Loss: 0.01079, Avg IoU: 0.9124, Avg Dice: 0.9428,Test IoU: 0.8709, Test Dice: 0.9178, Train Acc: 0.9976, Test Acc: 0.9953\n",
      "Epoch [60/200], Avg Loss: 0.01078, Avg IoU: 0.9271, Avg Dice: 0.9510,Test IoU: 0.8789, Test Dice: 0.9223, Train Acc: 0.9982, Test Acc: 0.9957\n",
      "Epoch [61/200], Avg Loss: 0.01078, Avg IoU: 0.9256, Avg Dice: 0.9502,Test IoU: 0.8706, Test Dice: 0.9176, Train Acc: 0.9981, Test Acc: 0.9953\n",
      "Epoch [62/200], Avg Loss: 0.01078, Avg IoU: 0.9325, Avg Dice: 0.9539,Test IoU: 0.8619, Test Dice: 0.9127, Train Acc: 0.9984, Test Acc: 0.9948\n",
      "Epoch [63/200], Avg Loss: 0.01078, Avg IoU: 0.9299, Avg Dice: 0.9522,Test IoU: 0.8742, Test Dice: 0.9196, Train Acc: 0.9983, Test Acc: 0.9954\n",
      "Epoch [64/200], Avg Loss: 0.01078, Avg IoU: 0.9349, Avg Dice: 0.9556,Test IoU: 0.8706, Test Dice: 0.9177, Train Acc: 0.9984, Test Acc: 0.9953\n",
      "Epoch [65/200], Avg Loss: 0.01078, Avg IoU: 0.9375, Avg Dice: 0.9568,Test IoU: 0.8636, Test Dice: 0.9137, Train Acc: 0.9985, Test Acc: 0.9950\n",
      "Epoch [66/200], Avg Loss: 0.01078, Avg IoU: 0.9388, Avg Dice: 0.9576,Test IoU: 0.8477, Test Dice: 0.9045, Train Acc: 0.9986, Test Acc: 0.9942\n",
      "Epoch [67/200], Avg Loss: 0.01080, Avg IoU: 0.8610, Avg Dice: 0.9087,Test IoU: 0.8577, Test Dice: 0.9094, Train Acc: 0.9957, Test Acc: 0.9947\n",
      "Epoch [68/200], Avg Loss: 0.01079, Avg IoU: 0.8934, Avg Dice: 0.9308,Test IoU: 0.8721, Test Dice: 0.9178, Train Acc: 0.9970, Test Acc: 0.9953\n",
      "Epoch [69/200], Avg Loss: 0.01079, Avg IoU: 0.9117, Avg Dice: 0.9422,Test IoU: 0.8472, Test Dice: 0.9037, Train Acc: 0.9976, Test Acc: 0.9941\n",
      "Epoch [70/200], Avg Loss: 0.01079, Avg IoU: 0.9108, Avg Dice: 0.9416,Test IoU: 0.8489, Test Dice: 0.9051, Train Acc: 0.9976, Test Acc: 0.9941\n",
      "Epoch [71/200], Avg Loss: 0.01079, Avg IoU: 0.9180, Avg Dice: 0.9457,Test IoU: 0.8685, Test Dice: 0.9162, Train Acc: 0.9979, Test Acc: 0.9951\n",
      "Epoch [72/200], Avg Loss: 0.01078, Avg IoU: 0.9237, Avg Dice: 0.9491,Test IoU: 0.8772, Test Dice: 0.9212, Train Acc: 0.9981, Test Acc: 0.9956\n",
      "Epoch [73/200], Avg Loss: 0.01078, Avg IoU: 0.9324, Avg Dice: 0.9541,Test IoU: 0.8686, Test Dice: 0.9164, Train Acc: 0.9984, Test Acc: 0.9953\n",
      "Epoch [74/200], Avg Loss: 0.01078, Avg IoU: 0.9334, Avg Dice: 0.9549,Test IoU: 0.8798, Test Dice: 0.9226, Train Acc: 0.9984, Test Acc: 0.9958\n",
      "Epoch [75/200], Avg Loss: 0.01078, Avg IoU: 0.9342, Avg Dice: 0.9552,Test IoU: 0.8667, Test Dice: 0.9151, Train Acc: 0.9984, Test Acc: 0.9952\n",
      "Epoch [76/200], Avg Loss: 0.01078, Avg IoU: 0.9209, Avg Dice: 0.9469,Test IoU: 0.8620, Test Dice: 0.9125, Train Acc: 0.9980, Test Acc: 0.9950\n",
      "Epoch [77/200], Avg Loss: 0.01078, Avg IoU: 0.9311, Avg Dice: 0.9534,Test IoU: 0.8466, Test Dice: 0.9031, Train Acc: 0.9983, Test Acc: 0.9943\n",
      "Epoch [78/200], Avg Loss: 0.01079, Avg IoU: 0.9005, Avg Dice: 0.9351,Test IoU: 0.8648, Test Dice: 0.9140, Train Acc: 0.9972, Test Acc: 0.9951\n",
      "Epoch [79/200], Avg Loss: 0.01078, Avg IoU: 0.9269, Avg Dice: 0.9509,Test IoU: 0.8764, Test Dice: 0.9206, Train Acc: 0.9982, Test Acc: 0.9956\n",
      "Epoch [80/200], Avg Loss: 0.01078, Avg IoU: 0.9351, Avg Dice: 0.9555,Test IoU: 0.8844, Test Dice: 0.9252, Train Acc: 0.9985, Test Acc: 0.9960\n",
      "Epoch [81/200], Avg Loss: 0.01078, Avg IoU: 0.9378, Avg Dice: 0.9569,Test IoU: 0.8783, Test Dice: 0.9218, Train Acc: 0.9986, Test Acc: 0.9957\n",
      "Epoch [82/200], Avg Loss: 0.01078, Avg IoU: 0.9429, Avg Dice: 0.9599,Test IoU: 0.8774, Test Dice: 0.9212, Train Acc: 0.9988, Test Acc: 0.9957\n",
      "Epoch [83/200], Avg Loss: 0.01078, Avg IoU: 0.9460, Avg Dice: 0.9615,Test IoU: 0.8754, Test Dice: 0.9202, Train Acc: 0.9989, Test Acc: 0.9956\n",
      "Epoch [84/200], Avg Loss: 0.01078, Avg IoU: 0.9450, Avg Dice: 0.9610,Test IoU: 0.8719, Test Dice: 0.9182, Train Acc: 0.9988, Test Acc: 0.9954\n",
      "Epoch [85/200], Avg Loss: 0.01078, Avg IoU: 0.9424, Avg Dice: 0.9597,Test IoU: 0.8787, Test Dice: 0.9220, Train Acc: 0.9987, Test Acc: 0.9957\n",
      "Epoch [86/200], Avg Loss: 0.01078, Avg IoU: 0.9439, Avg Dice: 0.9603,Test IoU: 0.8782, Test Dice: 0.9216, Train Acc: 0.9988, Test Acc: 0.9957\n",
      "Epoch [87/200], Avg Loss: 0.01078, Avg IoU: 0.9460, Avg Dice: 0.9616,Test IoU: 0.8750, Test Dice: 0.9200, Train Acc: 0.9989, Test Acc: 0.9955\n",
      "Epoch [88/200], Avg Loss: 0.01078, Avg IoU: 0.9458, Avg Dice: 0.9614,Test IoU: 0.8512, Test Dice: 0.9064, Train Acc: 0.9988, Test Acc: 0.9944\n",
      "Epoch [89/200], Avg Loss: 0.01079, Avg IoU: 0.9263, Avg Dice: 0.9506,Test IoU: 0.8567, Test Dice: 0.9095, Train Acc: 0.9981, Test Acc: 0.9946\n",
      "Epoch [90/200], Avg Loss: 0.01080, Avg IoU: 0.8672, Avg Dice: 0.9130,Test IoU: 0.8143, Test Dice: 0.8811, Train Acc: 0.9959, Test Acc: 0.9925\n",
      "Epoch [91/200], Avg Loss: 0.01080, Avg IoU: 0.8555, Avg Dice: 0.9051,Test IoU: 0.8579, Test Dice: 0.9096, Train Acc: 0.9955, Test Acc: 0.9947\n",
      "Epoch [92/200], Avg Loss: 0.01080, Avg IoU: 0.8788, Avg Dice: 0.9208,Test IoU: 0.8574, Test Dice: 0.9092, Train Acc: 0.9964, Test Acc: 0.9948\n",
      "Epoch [93/200], Avg Loss: 0.01079, Avg IoU: 0.9009, Avg Dice: 0.9353,Test IoU: 0.8742, Test Dice: 0.9196, Train Acc: 0.9973, Test Acc: 0.9955\n",
      "Epoch [94/200], Avg Loss: 0.01079, Avg IoU: 0.9093, Avg Dice: 0.9406,Test IoU: 0.8581, Test Dice: 0.9100, Train Acc: 0.9976, Test Acc: 0.9948\n",
      "Epoch [95/200], Avg Loss: 0.01078, Avg IoU: 0.9182, Avg Dice: 0.9456,Test IoU: 0.8690, Test Dice: 0.9167, Train Acc: 0.9979, Test Acc: 0.9953\n",
      "Epoch [96/200], Avg Loss: 0.01078, Avg IoU: 0.9237, Avg Dice: 0.9488,Test IoU: 0.8768, Test Dice: 0.9210, Train Acc: 0.9981, Test Acc: 0.9957\n",
      "Epoch [97/200], Avg Loss: 0.01078, Avg IoU: 0.9294, Avg Dice: 0.9522,Test IoU: 0.8763, Test Dice: 0.9207, Train Acc: 0.9983, Test Acc: 0.9956\n",
      "Epoch [98/200], Avg Loss: 0.01078, Avg IoU: 0.9332, Avg Dice: 0.9543,Test IoU: 0.8716, Test Dice: 0.9182, Train Acc: 0.9984, Test Acc: 0.9954\n",
      "Epoch [99/200], Avg Loss: 0.01078, Avg IoU: 0.9363, Avg Dice: 0.9560,Test IoU: 0.8732, Test Dice: 0.9192, Train Acc: 0.9985, Test Acc: 0.9954\n",
      "Epoch [100/200], Avg Loss: 0.01078, Avg IoU: 0.9377, Avg Dice: 0.9568,Test IoU: 0.8582, Test Dice: 0.9106, Train Acc: 0.9986, Test Acc: 0.9947\n",
      "Epoch [101/200], Avg Loss: 0.01078, Avg IoU: 0.9340, Avg Dice: 0.9549,Test IoU: 0.8700, Test Dice: 0.9173, Train Acc: 0.9984, Test Acc: 0.9952\n",
      "Epoch [102/200], Avg Loss: 0.01079, Avg IoU: 0.9060, Avg Dice: 0.9391,Test IoU: 0.8665, Test Dice: 0.9150, Train Acc: 0.9973, Test Acc: 0.9952\n",
      "Epoch [103/200], Avg Loss: 0.01078, Avg IoU: 0.9251, Avg Dice: 0.9498,Test IoU: 0.8803, Test Dice: 0.9229, Train Acc: 0.9981, Test Acc: 0.9958\n",
      "Epoch [104/200], Avg Loss: 0.01078, Avg IoU: 0.9311, Avg Dice: 0.9534,Test IoU: 0.8773, Test Dice: 0.9214, Train Acc: 0.9983, Test Acc: 0.9956\n",
      "Epoch [105/200], Avg Loss: 0.01078, Avg IoU: 0.9339, Avg Dice: 0.9547,Test IoU: 0.8758, Test Dice: 0.9205, Train Acc: 0.9984, Test Acc: 0.9956\n",
      "Epoch [106/200], Avg Loss: 0.01078, Avg IoU: 0.9378, Avg Dice: 0.9571,Test IoU: 0.8749, Test Dice: 0.9200, Train Acc: 0.9986, Test Acc: 0.9955\n",
      "Epoch [107/200], Avg Loss: 0.01078, Avg IoU: 0.9405, Avg Dice: 0.9585,Test IoU: 0.8732, Test Dice: 0.9191, Train Acc: 0.9987, Test Acc: 0.9954\n",
      "Epoch [108/200], Avg Loss: 0.01078, Avg IoU: 0.9425, Avg Dice: 0.9594,Test IoU: 0.8729, Test Dice: 0.9190, Train Acc: 0.9987, Test Acc: 0.9954\n",
      "Epoch [109/200], Avg Loss: 0.01078, Avg IoU: 0.9430, Avg Dice: 0.9598,Test IoU: 0.8763, Test Dice: 0.9208, Train Acc: 0.9987, Test Acc: 0.9956\n",
      "Epoch [110/200], Avg Loss: 0.01078, Avg IoU: 0.9443, Avg Dice: 0.9604,Test IoU: 0.8803, Test Dice: 0.9231, Train Acc: 0.9988, Test Acc: 0.9958\n",
      "Epoch [111/200], Avg Loss: 0.01078, Avg IoU: 0.9412, Avg Dice: 0.9577,Test IoU: 0.8862, Test Dice: 0.9264, Train Acc: 0.9988, Test Acc: 0.9960\n",
      "Epoch [112/200], Avg Loss: 0.01078, Avg IoU: 0.9410, Avg Dice: 0.9579,Test IoU: 0.8863, Test Dice: 0.9264, Train Acc: 0.9987, Test Acc: 0.9960\n",
      "Epoch [113/200], Avg Loss: 0.01078, Avg IoU: 0.9420, Avg Dice: 0.9586,Test IoU: 0.8820, Test Dice: 0.9240, Train Acc: 0.9988, Test Acc: 0.9958\n",
      "Epoch [114/200], Avg Loss: 0.01078, Avg IoU: 0.9450, Avg Dice: 0.9602,Test IoU: 0.8814, Test Dice: 0.9237, Train Acc: 0.9989, Test Acc: 0.9958\n",
      "Epoch [115/200], Avg Loss: 0.01078, Avg IoU: 0.9435, Avg Dice: 0.9596,Test IoU: 0.8733, Test Dice: 0.9191, Train Acc: 0.9988, Test Acc: 0.9954\n",
      "Epoch [116/200], Avg Loss: 0.01078, Avg IoU: 0.9448, Avg Dice: 0.9604,Test IoU: 0.8829, Test Dice: 0.9246, Train Acc: 0.9989, Test Acc: 0.9958\n",
      "Epoch [117/200], Avg Loss: 0.01077, Avg IoU: 0.9495, Avg Dice: 0.9630,Test IoU: 0.8874, Test Dice: 0.9270, Train Acc: 0.9990, Test Acc: 0.9961\n",
      "Epoch [118/200], Avg Loss: 0.01078, Avg IoU: 0.9496, Avg Dice: 0.9633,Test IoU: 0.8814, Test Dice: 0.9238, Train Acc: 0.9990, Test Acc: 0.9957\n",
      "Epoch [119/200], Avg Loss: 0.01078, Avg IoU: 0.9496, Avg Dice: 0.9632,Test IoU: 0.8628, Test Dice: 0.9129, Train Acc: 0.9990, Test Acc: 0.9948\n",
      "Epoch [120/200], Avg Loss: 0.01079, Avg IoU: 0.9030, Avg Dice: 0.9361,Test IoU: 0.8736, Test Dice: 0.9190, Train Acc: 0.9972, Test Acc: 0.9954\n",
      "Epoch [121/200], Avg Loss: 0.01079, Avg IoU: 0.9199, Avg Dice: 0.9465,Test IoU: 0.8670, Test Dice: 0.9153, Train Acc: 0.9979, Test Acc: 0.9951\n",
      "Epoch [122/200], Avg Loss: 0.01078, Avg IoU: 0.9315, Avg Dice: 0.9533,Test IoU: 0.8802, Test Dice: 0.9228, Train Acc: 0.9984, Test Acc: 0.9957\n",
      "Epoch [123/200], Avg Loss: 0.01078, Avg IoU: 0.9404, Avg Dice: 0.9582,Test IoU: 0.8791, Test Dice: 0.9222, Train Acc: 0.9987, Test Acc: 0.9957\n",
      "Epoch [124/200], Avg Loss: 0.01078, Avg IoU: 0.9398, Avg Dice: 0.9576,Test IoU: 0.8860, Test Dice: 0.9261, Train Acc: 0.9987, Test Acc: 0.9960\n",
      "Epoch [125/200], Avg Loss: 0.01078, Avg IoU: 0.9453, Avg Dice: 0.9608,Test IoU: 0.8794, Test Dice: 0.9226, Train Acc: 0.9989, Test Acc: 0.9956\n",
      "Epoch [126/200], Avg Loss: 0.01078, Avg IoU: 0.9458, Avg Dice: 0.9610,Test IoU: 0.8842, Test Dice: 0.9251, Train Acc: 0.9989, Test Acc: 0.9959\n",
      "Epoch [127/200], Avg Loss: 0.01078, Avg IoU: 0.9490, Avg Dice: 0.9627,Test IoU: 0.8789, Test Dice: 0.9222, Train Acc: 0.9990, Test Acc: 0.9957\n",
      "Epoch [128/200], Avg Loss: 0.01078, Avg IoU: 0.9472, Avg Dice: 0.9618,Test IoU: 0.8873, Test Dice: 0.9268, Train Acc: 0.9989, Test Acc: 0.9961\n",
      "Epoch [129/200], Avg Loss: 0.01078, Avg IoU: 0.9477, Avg Dice: 0.9621,Test IoU: 0.8794, Test Dice: 0.9222, Train Acc: 0.9989, Test Acc: 0.9957\n",
      "Epoch [130/200], Avg Loss: 0.01078, Avg IoU: 0.9214, Avg Dice: 0.9463,Test IoU: 0.8625, Test Dice: 0.9127, Train Acc: 0.9981, Test Acc: 0.9950\n",
      "Epoch [131/200], Avg Loss: 0.01078, Avg IoU: 0.9299, Avg Dice: 0.9518,Test IoU: 0.8698, Test Dice: 0.9164, Train Acc: 0.9983, Test Acc: 0.9954\n",
      "Epoch [132/200], Avg Loss: 0.01079, Avg IoU: 0.8986, Avg Dice: 0.9324,Test IoU: 0.8562, Test Dice: 0.9090, Train Acc: 0.9972, Test Acc: 0.9945\n",
      "Epoch [133/200], Avg Loss: 0.01078, Avg IoU: 0.9195, Avg Dice: 0.9459,Test IoU: 0.8679, Test Dice: 0.9158, Train Acc: 0.9980, Test Acc: 0.9951\n",
      "Epoch [134/200], Avg Loss: 0.01078, Avg IoU: 0.9316, Avg Dice: 0.9530,Test IoU: 0.8730, Test Dice: 0.9188, Train Acc: 0.9984, Test Acc: 0.9953\n",
      "Epoch [135/200], Avg Loss: 0.01078, Avg IoU: 0.9376, Avg Dice: 0.9559,Test IoU: 0.8668, Test Dice: 0.9154, Train Acc: 0.9986, Test Acc: 0.9950\n",
      "Epoch [136/200], Avg Loss: 0.01078, Avg IoU: 0.9415, Avg Dice: 0.9587,Test IoU: 0.8775, Test Dice: 0.9214, Train Acc: 0.9987, Test Acc: 0.9955\n",
      "Epoch [137/200], Avg Loss: 0.01078, Avg IoU: 0.9457, Avg Dice: 0.9609,Test IoU: 0.8729, Test Dice: 0.9188, Train Acc: 0.9989, Test Acc: 0.9953\n",
      "Epoch [138/200], Avg Loss: 0.01078, Avg IoU: 0.9491, Avg Dice: 0.9628,Test IoU: 0.8668, Test Dice: 0.9153, Train Acc: 0.9990, Test Acc: 0.9950\n",
      "Epoch [139/200], Avg Loss: 0.01078, Avg IoU: 0.9432, Avg Dice: 0.9589,Test IoU: 0.8677, Test Dice: 0.9157, Train Acc: 0.9988, Test Acc: 0.9952\n",
      "Epoch [140/200], Avg Loss: 0.01078, Avg IoU: 0.9190, Avg Dice: 0.9456,Test IoU: 0.8679, Test Dice: 0.9157, Train Acc: 0.9979, Test Acc: 0.9951\n",
      "Epoch [141/200], Avg Loss: 0.01078, Avg IoU: 0.9368, Avg Dice: 0.9561,Test IoU: 0.8635, Test Dice: 0.9137, Train Acc: 0.9986, Test Acc: 0.9949\n",
      "Epoch [142/200], Avg Loss: 0.01078, Avg IoU: 0.9376, Avg Dice: 0.9566,Test IoU: 0.8806, Test Dice: 0.9233, Train Acc: 0.9986, Test Acc: 0.9957\n",
      "Epoch [143/200], Avg Loss: 0.01078, Avg IoU: 0.9457, Avg Dice: 0.9609,Test IoU: 0.8740, Test Dice: 0.9194, Train Acc: 0.9989, Test Acc: 0.9954\n",
      "Epoch [144/200], Avg Loss: 0.01078, Avg IoU: 0.9484, Avg Dice: 0.9625,Test IoU: 0.8725, Test Dice: 0.9186, Train Acc: 0.9990, Test Acc: 0.9954\n",
      "Epoch [145/200], Avg Loss: 0.01078, Avg IoU: 0.9508, Avg Dice: 0.9638,Test IoU: 0.8745, Test Dice: 0.9198, Train Acc: 0.9990, Test Acc: 0.9954\n",
      "Epoch [146/200], Avg Loss: 0.01078, Avg IoU: 0.9512, Avg Dice: 0.9639,Test IoU: 0.8777, Test Dice: 0.9216, Train Acc: 0.9991, Test Acc: 0.9956\n",
      "Epoch [147/200], Avg Loss: 0.01078, Avg IoU: 0.9513, Avg Dice: 0.9639,Test IoU: 0.8714, Test Dice: 0.9180, Train Acc: 0.9991, Test Acc: 0.9953\n",
      "Epoch [148/200], Avg Loss: 0.01078, Avg IoU: 0.9534, Avg Dice: 0.9651,Test IoU: 0.8719, Test Dice: 0.9183, Train Acc: 0.9992, Test Acc: 0.9954\n",
      "Epoch [149/200], Avg Loss: 0.01078, Avg IoU: 0.9545, Avg Dice: 0.9656,Test IoU: 0.8759, Test Dice: 0.9206, Train Acc: 0.9992, Test Acc: 0.9955\n",
      "Epoch [150/200], Avg Loss: 0.01077, Avg IoU: 0.9562, Avg Dice: 0.9666,Test IoU: 0.8805, Test Dice: 0.9232, Train Acc: 0.9992, Test Acc: 0.9957\n",
      "Epoch [151/200], Avg Loss: 0.01077, Avg IoU: 0.9542, Avg Dice: 0.9654,Test IoU: 0.8756, Test Dice: 0.9205, Train Acc: 0.9992, Test Acc: 0.9955\n",
      "Epoch [152/200], Avg Loss: 0.01078, Avg IoU: 0.9555, Avg Dice: 0.9661,Test IoU: 0.8610, Test Dice: 0.9121, Train Acc: 0.9992, Test Acc: 0.9948\n",
      "Epoch [153/200], Avg Loss: 0.01078, Avg IoU: 0.9244, Avg Dice: 0.9491,Test IoU: 0.8598, Test Dice: 0.9114, Train Acc: 0.9981, Test Acc: 0.9948\n",
      "Epoch [154/200], Avg Loss: 0.01078, Avg IoU: 0.9390, Avg Dice: 0.9575,Test IoU: 0.8739, Test Dice: 0.9194, Train Acc: 0.9986, Test Acc: 0.9954\n",
      "Epoch [155/200], Avg Loss: 0.01078, Avg IoU: 0.9486, Avg Dice: 0.9627,Test IoU: 0.8657, Test Dice: 0.9148, Train Acc: 0.9990, Test Acc: 0.9950\n",
      "Epoch [156/200], Avg Loss: 0.01078, Avg IoU: 0.9455, Avg Dice: 0.9610,Test IoU: 0.8691, Test Dice: 0.9168, Train Acc: 0.9989, Test Acc: 0.9951\n",
      "Epoch [157/200], Avg Loss: 0.01078, Avg IoU: 0.9501, Avg Dice: 0.9634,Test IoU: 0.8737, Test Dice: 0.9193, Train Acc: 0.9990, Test Acc: 0.9954\n",
      "Epoch [158/200], Avg Loss: 0.01078, Avg IoU: 0.9538, Avg Dice: 0.9653,Test IoU: 0.8779, Test Dice: 0.9216, Train Acc: 0.9992, Test Acc: 0.9956\n",
      "Epoch [159/200], Avg Loss: 0.01078, Avg IoU: 0.9532, Avg Dice: 0.9651,Test IoU: 0.8790, Test Dice: 0.9222, Train Acc: 0.9991, Test Acc: 0.9957\n",
      "Epoch [160/200], Avg Loss: 0.01077, Avg IoU: 0.9554, Avg Dice: 0.9663,Test IoU: 0.8792, Test Dice: 0.9224, Train Acc: 0.9992, Test Acc: 0.9956\n",
      "Epoch [161/200], Avg Loss: 0.01078, Avg IoU: 0.9558, Avg Dice: 0.9667,Test IoU: 0.8759, Test Dice: 0.9205, Train Acc: 0.9992, Test Acc: 0.9955\n",
      "Epoch [162/200], Avg Loss: 0.01078, Avg IoU: 0.9576, Avg Dice: 0.9674,Test IoU: 0.8709, Test Dice: 0.9176, Train Acc: 0.9993, Test Acc: 0.9953\n",
      "Epoch [163/200], Avg Loss: 0.01078, Avg IoU: 0.9588, Avg Dice: 0.9681,Test IoU: 0.8734, Test Dice: 0.9190, Train Acc: 0.9993, Test Acc: 0.9954\n",
      "Epoch [164/200], Avg Loss: 0.01078, Avg IoU: 0.9574, Avg Dice: 0.9675,Test IoU: 0.8712, Test Dice: 0.9178, Train Acc: 0.9993, Test Acc: 0.9953\n",
      "Epoch [165/200], Avg Loss: 0.01078, Avg IoU: 0.9472, Avg Dice: 0.9620,Test IoU: 0.8731, Test Dice: 0.9188, Train Acc: 0.9989, Test Acc: 0.9953\n",
      "Epoch [166/200], Avg Loss: 0.01078, Avg IoU: 0.9521, Avg Dice: 0.9645,Test IoU: 0.8749, Test Dice: 0.9198, Train Acc: 0.9991, Test Acc: 0.9955\n",
      "Epoch [167/200], Avg Loss: 0.01078, Avg IoU: 0.9563, Avg Dice: 0.9669,Test IoU: 0.8724, Test Dice: 0.9185, Train Acc: 0.9992, Test Acc: 0.9954\n",
      "Epoch [168/200], Avg Loss: 0.01078, Avg IoU: 0.9462, Avg Dice: 0.9612,Test IoU: 0.8771, Test Dice: 0.9209, Train Acc: 0.9989, Test Acc: 0.9956\n",
      "Epoch [169/200], Avg Loss: 0.01078, Avg IoU: 0.9521, Avg Dice: 0.9645,Test IoU: 0.8732, Test Dice: 0.9190, Train Acc: 0.9991, Test Acc: 0.9954\n",
      "Epoch [170/200], Avg Loss: 0.01077, Avg IoU: 0.9556, Avg Dice: 0.9666,Test IoU: 0.8818, Test Dice: 0.9237, Train Acc: 0.9992, Test Acc: 0.9959\n",
      "Epoch [171/200], Avg Loss: 0.01077, Avg IoU: 0.9568, Avg Dice: 0.9670,Test IoU: 0.8790, Test Dice: 0.9222, Train Acc: 0.9993, Test Acc: 0.9957\n",
      "Epoch [172/200], Avg Loss: 0.01078, Avg IoU: 0.9587, Avg Dice: 0.9681,Test IoU: 0.8764, Test Dice: 0.9206, Train Acc: 0.9993, Test Acc: 0.9956\n",
      "Epoch [173/200], Avg Loss: 0.01077, Avg IoU: 0.9597, Avg Dice: 0.9687,Test IoU: 0.8667, Test Dice: 0.9152, Train Acc: 0.9994, Test Acc: 0.9952\n",
      "Epoch [174/200], Avg Loss: 0.01077, Avg IoU: 0.9565, Avg Dice: 0.9670,Test IoU: 0.8788, Test Dice: 0.9220, Train Acc: 0.9992, Test Acc: 0.9957\n",
      "Epoch [175/200], Avg Loss: 0.01078, Avg IoU: 0.9582, Avg Dice: 0.9679,Test IoU: 0.8820, Test Dice: 0.9237, Train Acc: 0.9993, Test Acc: 0.9959\n",
      "Epoch [176/200], Avg Loss: 0.01077, Avg IoU: 0.9594, Avg Dice: 0.9685,Test IoU: 0.8767, Test Dice: 0.9208, Train Acc: 0.9993, Test Acc: 0.9956\n",
      "Epoch [177/200], Avg Loss: 0.01077, Avg IoU: 0.9535, Avg Dice: 0.9653,Test IoU: 0.8804, Test Dice: 0.9228, Train Acc: 0.9991, Test Acc: 0.9958\n",
      "Epoch [178/200], Avg Loss: 0.01077, Avg IoU: 0.9575, Avg Dice: 0.9674,Test IoU: 0.8678, Test Dice: 0.9150, Train Acc: 0.9993, Test Acc: 0.9952\n",
      "Epoch [179/200], Avg Loss: 0.01079, Avg IoU: 0.9055, Avg Dice: 0.9363,Test IoU: 0.8696, Test Dice: 0.9167, Train Acc: 0.9974, Test Acc: 0.9953\n",
      "Epoch [180/200], Avg Loss: 0.01078, Avg IoU: 0.9317, Avg Dice: 0.9531,Test IoU: 0.8739, Test Dice: 0.9191, Train Acc: 0.9984, Test Acc: 0.9955\n",
      "Epoch [181/200], Avg Loss: 0.01078, Avg IoU: 0.9431, Avg Dice: 0.9596,Test IoU: 0.8747, Test Dice: 0.9197, Train Acc: 0.9988, Test Acc: 0.9955\n",
      "Epoch [182/200], Avg Loss: 0.01078, Avg IoU: 0.9474, Avg Dice: 0.9621,Test IoU: 0.8771, Test Dice: 0.9210, Train Acc: 0.9989, Test Acc: 0.9956\n",
      "Epoch [183/200], Avg Loss: 0.01078, Avg IoU: 0.9452, Avg Dice: 0.9610,Test IoU: 0.8735, Test Dice: 0.9189, Train Acc: 0.9988, Test Acc: 0.9954\n",
      "Epoch [184/200], Avg Loss: 0.01078, Avg IoU: 0.9526, Avg Dice: 0.9646,Test IoU: 0.8774, Test Dice: 0.9213, Train Acc: 0.9991, Test Acc: 0.9956\n",
      "Epoch [185/200], Avg Loss: 0.01078, Avg IoU: 0.9530, Avg Dice: 0.9649,Test IoU: 0.8746, Test Dice: 0.9194, Train Acc: 0.9991, Test Acc: 0.9955\n",
      "Epoch [186/200], Avg Loss: 0.01077, Avg IoU: 0.9519, Avg Dice: 0.9644,Test IoU: 0.8800, Test Dice: 0.9225, Train Acc: 0.9991, Test Acc: 0.9958\n",
      "Epoch [187/200], Avg Loss: 0.01077, Avg IoU: 0.9572, Avg Dice: 0.9672,Test IoU: 0.8761, Test Dice: 0.9203, Train Acc: 0.9993, Test Acc: 0.9956\n",
      "Epoch [188/200], Avg Loss: 0.01077, Avg IoU: 0.9574, Avg Dice: 0.9674,Test IoU: 0.8799, Test Dice: 0.9225, Train Acc: 0.9993, Test Acc: 0.9958\n",
      "Epoch [189/200], Avg Loss: 0.01078, Avg IoU: 0.9401, Avg Dice: 0.9576,Test IoU: 0.8688, Test Dice: 0.9163, Train Acc: 0.9987, Test Acc: 0.9952\n",
      "Epoch [190/200], Avg Loss: 0.01077, Avg IoU: 0.9485, Avg Dice: 0.9622,Test IoU: 0.8768, Test Dice: 0.9203, Train Acc: 0.9990, Test Acc: 0.9956\n",
      "Epoch [191/200], Avg Loss: 0.01078, Avg IoU: 0.9475, Avg Dice: 0.9617,Test IoU: 0.8535, Test Dice: 0.9025, Train Acc: 0.9990, Test Acc: 0.9949\n",
      "Epoch [192/200], Avg Loss: 0.01078, Avg IoU: 0.9428, Avg Dice: 0.9590,Test IoU: 0.8688, Test Dice: 0.9164, Train Acc: 0.9988, Test Acc: 0.9953\n",
      "Epoch [193/200], Avg Loss: 0.01078, Avg IoU: 0.9490, Avg Dice: 0.9628,Test IoU: 0.8766, Test Dice: 0.9207, Train Acc: 0.9990, Test Acc: 0.9956\n",
      "Epoch [194/200], Avg Loss: 0.01078, Avg IoU: 0.9520, Avg Dice: 0.9645,Test IoU: 0.8747, Test Dice: 0.9196, Train Acc: 0.9991, Test Acc: 0.9955\n",
      "Epoch [195/200], Avg Loss: 0.01078, Avg IoU: 0.9527, Avg Dice: 0.9649,Test IoU: 0.8780, Test Dice: 0.9215, Train Acc: 0.9991, Test Acc: 0.9957\n",
      "Epoch [196/200], Avg Loss: 0.01078, Avg IoU: 0.9566, Avg Dice: 0.9669,Test IoU: 0.8795, Test Dice: 0.9223, Train Acc: 0.9993, Test Acc: 0.9958\n",
      "Epoch [197/200], Avg Loss: 0.01078, Avg IoU: 0.9598, Avg Dice: 0.9686,Test IoU: 0.8692, Test Dice: 0.9167, Train Acc: 0.9994, Test Acc: 0.9953\n",
      "Epoch [198/200], Avg Loss: 0.01078, Avg IoU: 0.9512, Avg Dice: 0.9642,Test IoU: 0.8788, Test Dice: 0.9221, Train Acc: 0.9991, Test Acc: 0.9957\n",
      "Epoch [199/200], Avg Loss: 0.01077, Avg IoU: 0.9592, Avg Dice: 0.9684,Test IoU: 0.8721, Test Dice: 0.9183, Train Acc: 0.9993, Test Acc: 0.9954\n",
      "Epoch [200/200], Avg Loss: 0.01078, Avg IoU: 0.9435, Avg Dice: 0.9598,Test IoU: 0.8647, Test Dice: 0.9140, Train Acc: 0.9988, Test Acc: 0.9951\n",
      "-----------------------------------------\n",
      "Best Loss: 0.0108, Best Train IoU: 0.9598, Best Train Dice: 0.9687, Best Test IoU: 0.8874, Best Test Dice: 0.9270, Best Train Acc: 0.9994, Best Test Acc: 0.9961\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# -----------------------------\n",
    "# UNet++ Train and Test Set\n",
    "# -----------------------------\n",
    "\n",
    "# empty the cache for model and training process\n",
    "torch.cuda.empty_cache()\n",
    "\n",
    "# set the hyper parameters and the paths 设置超参数和路径\n",
    "data_path = \"./dataset/Spineweb_dataset15/train_9/\"\n",
    "test_path = \"./dataset/Spineweb_dataset15/test_1/\"\n",
    "\n",
    "#hyper parameters set\n",
    "batch_size = 64\n",
    "num_epochs = 200\n",
    "learning_rate = 1e-03\n",
    "confidence = 0.7\n",
    "poster_threshold = 200\n",
    "in_channels = 1  # according to demand to adjust the number of channels 根据实际情况修改通道数\n",
    "out_channels = 1  # according to demand to adjust the number of channels 根据实际情况修改通道数\n",
    "\n",
    "# 创建数据集和数据加载器\n",
    "transform = transforms.Compose([\n",
    "    # transforms.ToPILImage(),  # 转换为 PIL 图像对象\n",
    "    transforms.Resize((128, 128)),  # 调整大小为 128x128\n",
    "    transforms.ToTensor()  # 转换为张量\n",
    "])\n",
    "\n",
    "dataset = SpineWeb15(data_path=data_path, transform1=transform, transform2=transform)\n",
    "dataloader = DataLoader(dataset, batch_size=batch_size, shuffle=True)\n",
    "testset = SpineWeb15(data_path=test_path, transform1=transform, transform2=transform)\n",
    "test_loader = DataLoader(testset, batch_size=batch_size, shuffle=True)\n",
    "\n",
    "# 创建模型和优化器\n",
    "model = smp.UnetPlusPlus(encoder_name='timm-resnest200e', encoder_depth=5, encoder_weights=\"imagenet\", decoder_use_batchnorm=True, decoder_channels=(256, 128, 64, 32, 16), decoder_attention_type=\"scse\", in_channels=1, classes=1, activation=None, aux_params=None)\n",
    "optimizer = optim.Adam(model.parameters(), lr=learning_rate)\n",
    "criterion = nn.BCEWithLogitsLoss()  # 二分类任务可以使用BCEWithLogitsLoss\n",
    "# criterion = DiceLoss(mode=\"binary\")   #DiceLoss\n",
    "# criterion = FocalLoss(mode=\"binary\")\n",
    "# criterion = smp.losses.TverskyLoss(mode=\"binary\")\n",
    "# criterion = smp.losses.LovaszLoss(mode=\"binary\")\n",
    "\n",
    "# 设置设备\n",
    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
    "model = model.to(device)\n",
    "\n",
    "# check the parameters of the model could be trained 检查模型的参数是否设置为可训练\n",
    "# for name, param in model.named_parameters():\n",
    "#     print(f\"Parameter: {name}, Requires Grad: {param.requires_grad}\")\n",
    "\n",
    "best_loss_train = 100.0\n",
    "best_IoU_train = 0.0\n",
    "best_Dice_train = 0.0\n",
    "\n",
    "best_IoU_test = 0.0\n",
    "best_Dice_test = 0.0\n",
    "\n",
    "best_acc_train = 0.0\n",
    "best_acc_test = 0.0\n",
    "\n",
    "loss_train_list = []\n",
    "IoU_train_list = []\n",
    "Dice_train_list = []\n",
    "IoU_test_list = []\n",
    "Dice_test_list = []\n",
    "acc_train_list = []\n",
    "acc_test_list = []\n",
    "\n",
    "# 训练模型\n",
    "for epoch in range(num_epochs):\n",
    "    # torch.cuda.empty_cache()\n",
    "    model.train()\n",
    "    train_loss = 0.0\n",
    "    total_iou = 0.0\n",
    "    total_dice = 0.0\n",
    "    total_iou_test = 0.0\n",
    "    total_dice_test = 0.0\n",
    "    total_acc_train = 0.0\n",
    "    total_acc_test = 0.0\n",
    "    total_correct_pixels = 0\n",
    "    total_pixels = 0\n",
    "    total_correct_pixels_test = 0\n",
    "    total_pixels_test = 0\n",
    "\n",
    "    if epoch > 100:\n",
    "        confidence = 0.9\n",
    "\n",
    "    for images, labels in dataloader:\n",
    "        images = images.to(device)\n",
    "        labels = labels.to(device)\n",
    "    \n",
    "        optimizer.zero_grad()\n",
    "\n",
    "        outputs = model(images)\n",
    "        # squeeze the dimension that pytorch add for channels\n",
    "        outputs = outputs.squeeze(1)     # tensor size ([16,128,128])\n",
    "        labels = labels.squeeze(1)\n",
    "        labels = labels * 255\n",
    "        labels[labels <= poster_threshold] = 0\n",
    "        labels[labels > poster_threshold] = 1\n",
    "        # apply sigmoid on the outputs\n",
    "        outputs = torch.sigmoid(outputs)\n",
    "        # process the result, bigger than confidence is reliable\n",
    "        predicted = (outputs > confidence).float()     # tensor size ([16,128,128]), True->1.0 and False->0.0\n",
    "        intersection = torch.logical_and(predicted, labels).sum((1, 2))\n",
    "        union = torch.logical_or(predicted, labels).sum((1, 2))\n",
    "        iou = (intersection / (union + 1e-7))  # 每个样本的IoU\n",
    "        dice = (2 * intersection / (predicted.sum((1, 2)) + labels.sum((1, 2))+1e-7))  # 每个样本的Dice Coefficient\n",
    "        # 计算训练集的像素精度\n",
    "        batch_correct_pixels,bacth_total_pixels = calculate_pixel_accuracy(predicted, labels)\n",
    "\n",
    "        loss = criterion(outputs, labels)\n",
    "\n",
    "        train_loss += loss.item()\n",
    "        total_iou += torch.sum(iou).item()\n",
    "        total_dice += torch.sum(dice).item()\n",
    "        total_correct_pixels += batch_correct_pixels\n",
    "        total_pixels += bacth_total_pixels\n",
    "\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "\n",
    "    # train set results\n",
    "    avg_loss = train_loss / len(dataset)\n",
    "    avg_iou = total_iou / len(dataset)\n",
    "    avg_dice = total_dice / len(dataset)\n",
    "    avg_acc_train = total_correct_pixels / total_pixels\n",
    "\n",
    "    # test set results\n",
    "    with torch.no_grad():\n",
    "        for inputs, targets in test_loader:     \n",
    "            inputs = inputs.to(device)\n",
    "            targets = targets.to(device)\n",
    "            targets = targets * 255\n",
    "            targets[targets <= poster_threshold] = 0\n",
    "            targets[targets > poster_threshold] = 1\n",
    "            outputs = model(inputs)\n",
    "            # squeeze the dimension that pytorch add for channels\n",
    "            outputs = outputs.squeeze(1)     # tensor size ([16,128,128])\n",
    "            targets = targets.squeeze(1)\n",
    "            outputs = torch.sigmoid(outputs)\n",
    "            # process the result, bigger than 0.5 is reliable\n",
    "            predicted = (outputs > confidence).float()     # tensor size ([16,128,128])\n",
    "            intersection = torch.logical_and(predicted, targets).sum((1, 2))\n",
    "            union = torch.logical_or(predicted, targets).sum((1, 2))\n",
    "            iou = (intersection / (union + 1e-7))  # 每个样本的IoU\n",
    "            dice = (2 * intersection / (predicted.sum((1, 2)) + targets.sum((1, 2))+1e-7))  # 每个样本的Dice Coefficient\n",
    "\n",
    "\n",
    "            batch_correct_pixels,batch_total_pixels = calculate_pixel_accuracy(predicted, targets)\n",
    "            total_iou_test += torch.sum(iou).item()\n",
    "            total_dice_test += torch.sum(dice).item()\n",
    "            total_correct_pixels_test += batch_correct_pixels\n",
    "            total_pixels_test += batch_total_pixels\n",
    "\n",
    "    test_iou = total_iou_test / len(testset)\n",
    "    test_dice = total_dice_test / len(testset)\n",
    "    test_acc = total_correct_pixels_test / total_pixels_test\n",
    "\n",
    "    # get the best IoU and Dice\n",
    "    best_loss_train = best_loss_train if best_loss_train < avg_loss else avg_loss\n",
    "    best_IoU_train = best_IoU_train if best_IoU_train > avg_iou else avg_iou\n",
    "    best_Dice_train = best_Dice_train if best_Dice_train > avg_dice else avg_dice\n",
    "    best_IoU_test = best_IoU_test if best_IoU_test > test_iou else test_iou\n",
    "    # best_Dice_test = best_Dice_test if best_Dice_test > test_dice else test_dice\n",
    "\n",
    "    if(best_Dice_test <= test_dice):\n",
    "        best_Dice_test = test_dice\n",
    "        torch.save(model.state_dict(), f\"./model/UNet++Resnest200_checkpoint.pth\")\n",
    "\n",
    "    # get the best accuracy of the trainloader and testloader\n",
    "    best_acc_train = best_acc_train if best_acc_train > avg_acc_train else avg_acc_train\n",
    "    best_acc_test = best_acc_test if best_acc_test > test_acc else test_acc\n",
    "\n",
    "    # store the data in each epoch\n",
    "    loss_train_list.append(avg_loss)\n",
    "    IoU_train_list.append(avg_iou)\n",
    "    Dice_train_list.append(avg_dice)\n",
    "    IoU_test_list.append(test_iou)\n",
    "    Dice_test_list.append(test_dice)\n",
    "    acc_train_list.append(avg_acc_train)\n",
    "    acc_test_list.append(test_acc)\n",
    "\n",
    "    print(f\"Epoch [{epoch+1}/{num_epochs}], Avg Loss: {avg_loss:.5f}, Avg IoU: {avg_iou:.4f}, Avg Dice: {avg_dice:.4f},Test IoU: {test_iou:.4f}, Test Dice: {test_dice:.4f}, Train Acc: {avg_acc_train:.4f}, Test Acc: {test_acc:.4f}\")\n",
    "    \n",
    "print(\"-----------------------------------------\")\n",
    "print(f\"Best Loss: {best_loss_train:.4f}, Best Train IoU: {best_IoU_train:.4f}, Best Train Dice: {best_Dice_train:.4f}, Best Test IoU: {best_IoU_test:.4f}, Best Test Dice: {best_Dice_test:.4f}, Best Train Acc: {best_acc_train:.4f}, Best Test Acc: {best_acc_test:.4f}\")\n",
    "# draw the plot for the loss and dice\n",
    "# 定义epochs的数量，用于x轴坐标\n",
    "epochs = len(loss_train_list)\n",
    "fit,(ax1,ax2) = plt.subplots(2,1,figsize=(8,8))\n",
    "# 绘制训练损失曲线\n",
    "ax1.plot(range(1, epochs + 1), loss_train_list, label='Train Loss')\n",
    "# 设置图例1\n",
    "ax1.legend()\n",
    "ax1.set_xlabel('Epochs')\n",
    "ax1.set_ylabel('Loss')\n",
    "ax1.set_title('Training Loss')\n",
    "ax1.grid(True)\n",
    "# 绘制IoU和Dice\n",
    "ax2.plot(range(1,epochs +1),IoU_train_list,label='Train IoU')\n",
    "ax2.plot(range(1,epochs +1),Dice_train_list,label='Train Dice')\n",
    "# 设置图例2\n",
    "ax2.legend()\n",
    "ax2.set_xlabel('Epochs')\n",
    "ax2.set_ylabel('IoU and Dice')\n",
    "ax2.set_title('Training IoU and Dice')\n",
    "ax2.grid(True)# show the plots\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# empty the cache for model and training process\n",
    "torch.cuda.empty_cache()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Test IoU: 0.8938, Best Test Dice: 0.9306 Best Test Acc: 0.9963\n"
     ]
    }
   ],
   "source": [
    "# Using the best model to evaluate on the test set\n",
    "\n",
    "model = smp.UnetPlusPlus(encoder_name='timm-resnest200e', encoder_depth=5, encoder_weights=\"imagenet\", decoder_use_batchnorm=True, decoder_channels=(256, 128, 64, 32, 16), decoder_attention_type=\"scse\", in_channels=1, classes=1, activation=None, aux_params=None)\n",
    "\n",
    "# 设置设备\n",
    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
    "model = model.to(device)\n",
    "\n",
    "test_path = \"./dataset/Spineweb_dataset15/test_1/\"\n",
    "\n",
    "transform = transforms.Compose([\n",
    "    # transforms.ToPILImage(),  # 转换为 PIL 图像对象\n",
    "    transforms.Resize((128, 128)),  # 调整大小为 128x128\n",
    "    transforms.ToTensor()  # 转换为张量\n",
    "])\n",
    "\n",
    "# 加载已保存的模型参数\n",
    "checkpoint_path = \"./model/UNet++Resnest200_checkpoint.pth\"  # 模型文件路径\n",
    "checkpoint = torch.load(checkpoint_path)\n",
    "model.load_state_dict(checkpoint)\n",
    "\n",
    "# 设置模型为推理模式\n",
    "model.eval()\n",
    "\n",
    "batch_size = 64\n",
    "poster_threshold = 200\n",
    "confidence = 0.9\n",
    "\n",
    "testset = SpineWeb15(data_path=test_path, transform1=transform,transform2=transform)\n",
    "test_loader = DataLoader(testset, batch_size=batch_size, shuffle=True)\n",
    "\n",
    "# data_iter = iter(test_loader)\n",
    "# images, labels = next(data_iter)\n",
    "# print(\"加载的样本数量:\", images.shape[0])\n",
    "\n",
    "total_iou_test = 0.0\n",
    "total_dice_test = 0.0\n",
    "total_acc_test = 0.0\n",
    "total_correct_pixels_test = 0\n",
    "total_pixels_test = 0\n",
    "\n",
    "# test set results\n",
    "with torch.no_grad():\n",
    "    for inputs, targets in test_loader:\n",
    "        inputs = inputs.to(device)\n",
    "        targets = targets.to(device)\n",
    "        targets = targets * 255\n",
    "        targets[targets <= poster_threshold] = 0\n",
    "        targets[targets > poster_threshold] = 1\n",
    "        outputs = model(inputs)\n",
    "        # squeeze the dimension that pytorch add for channels\n",
    "        outputs = outputs.squeeze(1)     # tensor size ([16,128,128])\n",
    "        targets = targets.squeeze(1)\n",
    "        outputs = torch.sigmoid(outputs)\n",
    "        # process the result, bigger than 0.5 is reliable\n",
    "        predicted = (outputs > confidence).float()     # tensor size ([16,128,128])\n",
    "        intersection = torch.logical_and(predicted, targets).sum((1, 2))\n",
    "        union = torch.logical_or(predicted, targets).sum((1, 2))\n",
    "        iou = (intersection / (union + 1e-7))  # 平均每个样本的IoU\n",
    "        dice = (2 * intersection / (predicted.sum((1, 2)) + targets.sum((1, 2))+1e-7))  # 平均每个样本的Dice Coefficient\n",
    "\n",
    "\n",
    "        batch_correct_pixels,batch_total_pixels = calculate_pixel_accuracy(predicted, targets)\n",
    "        total_iou_test += torch.sum(iou).item()\n",
    "        total_dice_test += torch.sum(dice).item()\n",
    "        total_correct_pixels_test += batch_correct_pixels\n",
    "        total_pixels_test += batch_total_pixels\n",
    "        \n",
    "inputs_npimage = inputs.squeeze(1)[0].detach().cpu().numpy()\n",
    "predicted_npimage = predicted[0].detach().cpu().numpy()\n",
    "targets_npimage = targets[0].detach().cpu().numpy()\n",
    "fig, axes = plt.subplots(3, 1)\n",
    "axes[0].imshow(inputs_npimage, cmap='gray')\n",
    "axes[0].set_title('Original')\n",
    "axes[1].imshow(predicted_npimage, cmap='gray')\n",
    "axes[1].set_title('Predicted')\n",
    "axes[2].imshow(targets_npimage, cmap='gray')\n",
    "axes[2].set_title('Target')\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "test_iou = total_iou_test / len(testset)\n",
    "test_dice = total_dice_test / len(testset)\n",
    "test_acc = total_correct_pixels_test / total_pixels_test\n",
    "\n",
    "# output the result, the result may vary a little due to the resize function in transform\n",
    "print(f\"Best Test IoU: {test_iou:.4f}, Best Test Dice: {test_dice:.4f} Best Test Acc: {test_acc:.4f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Train IoU: 0.9510, Best Train Dice: 0.9640 Best Train Acc: 0.9991\n"
     ]
    }
   ],
   "source": [
    "# Using the best model to evaluate on the train set\n",
    "\n",
    "model = smp.UnetPlusPlus(encoder_name='timm-resnest200e', encoder_depth=5, encoder_weights=\"imagenet\", decoder_use_batchnorm=True, decoder_channels=(256, 128, 64, 32, 16), decoder_attention_type=\"scse\", in_channels=1, classes=1, activation=None, aux_params=None)\n",
    "\n",
    "# 设置设备\n",
    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
    "model = model.to(device)\n",
    "\n",
    "test_path = \"./dataset/Spineweb_dataset15/train_9/\"\n",
    "\n",
    "transform = transforms.Compose([\n",
    "    # transforms.ToPILImage(),  # 转换为 PIL 图像对象\n",
    "    transforms.Resize((128, 128)),  # 调整大小为 128x128\n",
    "    transforms.ToTensor()  # 转换为张量\n",
    "])\n",
    "\n",
    "# 加载已保存的模型参数\n",
    "checkpoint_path = \"./model/UNet++Resnest200_checkpoint.pth\"  # 模型文件路径\n",
    "checkpoint = torch.load(checkpoint_path)\n",
    "model.load_state_dict(checkpoint)\n",
    "\n",
    "# 设置模型为推理模式\n",
    "model.eval()\n",
    "\n",
    "batch_size = 64\n",
    "poster_threshold = 200\n",
    "confidence = 0.9\n",
    "\n",
    "testset = SpineWeb15(data_path=test_path, transform1=transform, transform2=transform)\n",
    "test_loader = DataLoader(testset, batch_size=batch_size, shuffle=True)\n",
    "\n",
    "# data_iter = iter(test_loader)\n",
    "# images, labels = next(data_iter)\n",
    "# print(\"加载的样本数量:\", images.shape[0])\n",
    "\n",
    "total_iou_test = 0.0\n",
    "total_dice_test = 0.0\n",
    "total_acc_test = 0.0\n",
    "total_correct_pixels_test = 0\n",
    "total_pixels_test = 0\n",
    "\n",
    "# test set results\n",
    "with torch.no_grad():\n",
    "    for inputs, targets in test_loader:\n",
    "        inputs = inputs.to(device)\n",
    "        targets = targets.to(device)\n",
    "        targets = targets * 255\n",
    "        targets[targets <= poster_threshold] = 0\n",
    "        targets[targets > poster_threshold] = 1\n",
    "        outputs = model(inputs)\n",
    "        # squeeze the dimension that pytorch add for channels\n",
    "        outputs = outputs.squeeze(1)     # tensor size ([16,128,128])\n",
    "        targets = targets.squeeze(1)\n",
    "        outputs = torch.sigmoid(outputs)\n",
    "        # process the result, bigger than 0.5 is reliable\n",
    "        predicted = (outputs > confidence).float()     # tensor size ([16,128,128])\n",
    "        intersection = torch.logical_and(predicted, targets).sum((1, 2))\n",
    "        union = torch.logical_or(predicted, targets).sum((1, 2))\n",
    "        iou = (intersection / (union + 1e-7))  # 平均每个样本的IoU\n",
    "        dice = (2 * intersection / (predicted.sum((1, 2)) + targets.sum((1, 2))+1e-7))  # 平均每个样本的Dice Coefficient\n",
    "\n",
    "\n",
    "        batch_correct_pixels,batch_total_pixels = calculate_pixel_accuracy(predicted, targets)\n",
    "        total_iou_test += torch.sum(iou).item()\n",
    "        total_dice_test += torch.sum(dice).item()\n",
    "        total_correct_pixels_test += batch_correct_pixels\n",
    "        total_pixels_test += batch_total_pixels\n",
    "        \n",
    "\n",
    "inputs_npimage = inputs.squeeze(1)[0].detach().cpu().numpy()\n",
    "predicted_npimage = predicted[0].detach().cpu().numpy()\n",
    "targets_npimage = targets[0].detach().cpu().numpy()\n",
    "fig, axes = plt.subplots(3, 1)\n",
    "axes[0].imshow(inputs_npimage, cmap='gray')\n",
    "axes[0].set_title('Original')\n",
    "axes[1].imshow(predicted_npimage, cmap='gray')\n",
    "axes[1].set_title('Predicted')\n",
    "axes[2].imshow(targets_npimage, cmap='gray')\n",
    "axes[2].set_title('Target')\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "\n",
    "test_iou = total_iou_test / len(testset)\n",
    "test_dice = total_dice_test / len(testset)\n",
    "test_acc = total_correct_pixels_test / total_pixels_test\n",
    "\n",
    "# output the result, the result may vary a little due to the resize function in transform\n",
    "print(f\"Best Train IoU: {test_iou:.4f}, Best Train Dice: {test_dice:.4f} Best Train Acc: {test_acc:.4f}\")"
   ]
  }
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