3_Validation.ipynb

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 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ENGdLb-vBpcb"
   },
   "source": [
    "# Import packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "nw3ipHHaCS6Q"
   },
   "outputs": [],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "from pathlib import Path\n",
    "from PIL import Image\n",
    "from skimage.metrics import structural_similarity as ssim\n",
    "from sklearn.metrics import accuracy_score, balanced_accuracy_score, precision_score, recall_score, f1_score, jaccard_score\n",
    "from tqdm import tqdm\n",
    "import numpy as np\n",
    "import pandas as pd\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "4jei73ZlIvnv"
   },
   "source": [
    "# Select model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "jZonbdnLxplt"
   },
   "source": [
    "## Select detection model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "wyrjZzDrvDwv"
   },
   "outputs": [],
   "source": [
    "#@markdown ### Enter the experiment name for detection model:\n",
    "detection_model_exp_name = 'exp' #@param {type:\"string\"}\n",
    "\n",
    "detection_model_path = f\"/root/autodl-tmp/DT_SegNet/Detection_Model_Output/{detection_model_exp_name}/weights/best.pt\"\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "FN5o8fe4xK8z"
   },
   "source": [
    "Or use our trained detection model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
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    "outputId": "6cf2c576-665b-4049-b9de-5f37e210bf79"
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   "outputs": [],
   "source": [
    "# download our trained best model\n",
    "\n",
    "Path('/root/autodl-tmp/DT_SegNet/Models').mkdir(exist_ok=True, parents=True)\n",
    "\n",
    "!wget -O /root/autodl-tmp/DT_SegNet/Models/detection.pt https://github.com/xiazeyu/DT_SegNet/releases/latest/download/detection.pt\n",
    "\n",
    "detection_model_path= \"/root/autodl-tmp/DT_SegNet/Models/detection.pt\"\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "d5SfJv3mxnvC"
   },
   "source": [
    "Then, ensure correct detection model is selected"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
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    "id": "jaK6N9E0xP_6",
    "outputId": "908c235c-62d5-4a5b-b7e7-726db9ff7a18"
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   "outputs": [],
   "source": [
    "!stat {detection_model_path}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "orTjpzub1SsV"
   },
   "source": [
    "## Select segmentation model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "PmkfSKuE1UE8"
   },
   "outputs": [],
   "source": [
    "#@markdown ### Enter the experiment name for segmentation model:\n",
    "segmentation_model_exp_name = 'B1' #@param {type:\"string\"}\n",
    "\n",
    "segmentation_model_path = f\"/root/autodl-tmp/DT_SegNet/Segmentation_Model_Output/{segmentation_model_exp_name}/best_model/model.pdparams\"\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "IRyUcOkq1qOE"
   },
   "source": [
    "Or use our trained segmentation model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "RH0zbUbn1qr2",
    "outputId": "99b3d82c-c24e-4c59-fc5a-640b85019bde"
   },
   "outputs": [],
   "source": [
    "# download our trained best model\n",
    "\n",
    "Path('/root/autodl-tmp/DT_SegNet/Models').mkdir(exist_ok=True, parents=True)\n",
    "\n",
    "!wget -O /root/autodl-tmp/DT_SegNet/Models/segmentation.pdparams https://github.com/xiazeyu/DT_SegNet/releases/latest/download/segmentation.pdparams\n",
    "\n",
    "segmentation_model_path= \"/root/autodl-tmp/DT_SegNet/Models/segmentation.pdparams\"\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "kyVa7-PH1kHf"
   },
   "source": [
    "Then, ensure correct segmentation model is selected"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
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     "base_uri": "https://localhost:8080/"
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    "id": "ExP7Z7YP1jzt",
    "outputId": "d4f388f8-76f7-460f-93d1-80f8a21b2cea"
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   "outputs": [],
   "source": [
    "!stat {segmentation_model_path}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_-1_Nh5417SI"
   },
   "source": [
    "# Validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
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    "id": "VMgqwi2V18QH",
    "outputId": "227fad58-e910-4396-e92e-42197529978b"
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   "outputs": [],
   "source": [
    "# Validate the detection model on test set\n",
    "%cd 1_Detection_Model\n",
    "!python val.py --task test --data dtsegnet.yaml --save-txt --save-conf --img 1280 --weights {detection_model_path}\n",
    "%cd ..\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "outputs": [],
   "source": [
    "# Validate the detection model on training set\n",
    "%cd 1_Detection_Model\n",
    "!python val.py --task train --data dtsegnet.yaml --save-txt --save-conf --img 1280 --weights {detection_model_path}\n",
    "%cd ..\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   },
   "outputs": [],
   "source": [
    "# Validate the detection model on validation set\n",
    "%cd 1_Detection_Model\n",
    "!python val.py --task val --data dtsegnet.yaml --save-txt --save-conf --img 1280 --weights {detection_model_path}\n",
    "%cd ..\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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    "outputId": "cfd8cf34-59d9-40ac-81f4-199fa7dd89d2"
   },
   "outputs": [],
   "source": [
    "# Validate the segmentation model on validation set\n",
    "%cd 3_Segmentation_Model\n",
    "!python val.py --config configs/dtsegnet/segformer_b1.yml --model_path {segmentation_model_path}\n",
    "%cd ..\n"
   ]
  }
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