Finetune_Colab.ipynb

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            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "d55fa94f74f54e63bd5361fc590c747e": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        }
      }
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "  # DT-SegNet\n",
        "\n",
        "  This notebook should be run in Google Colab"
      ],
      "metadata": {
        "id": "MmMGZsrbTLyn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Pull from GitHub\n",
        "%cd /content\n",
        "!git clone https://github.com/xiazeyu/DT_SegNet.git\n",
        "%cd /content/DT_SegNet"
      ],
      "metadata": {
        "id": "mO7IlajDTSbG",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "828cdf0a-f09a-4bae-f83b-b5ebe36f8df1"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content\n",
            "Cloning into 'DT_SegNet'...\n",
            "remote: Enumerating objects: 853, done.\u001b[K\n",
            "remote: Counting objects: 100% (215/215), done.\u001b[K\n",
            "remote: Compressing objects: 100% (174/174), done.\u001b[K\n",
            "remote: Total 853 (delta 61), reused 181 (delta 41), pack-reused 638\u001b[K\n",
            "Receiving objects: 100% (853/853), 17.09 MiB | 38.98 MiB/s, done.\n",
            "Resolving deltas: 100% (163/163), done.\n",
            "/content/DT_SegNet\n",
            "Branch 'finetune' set up to track remote branch 'finetune' from 'origin'.\n",
            "Switched to a new branch 'finetune'\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Check GPU status\n",
        "!nvidia-smi"
      ],
      "metadata": {
        "id": "Ujes6IBXTZDK",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2529ae12-abc7-4468-83ca-57847cc19c27"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fri May 19 12:26:45 2023       \n",
            "+-----------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 525.85.12    Driver Version: 525.85.12    CUDA Version: 12.0     |\n",
            "|-------------------------------+----------------------+----------------------+\n",
            "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
            "|                               |                      |               MIG M. |\n",
            "|===============================+======================+======================|\n",
            "|   0  NVIDIA A100-SXM...  Off  | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   31C    P0    41W / 400W |      0MiB / 40960MiB |      0%      Default |\n",
            "|                               |                      |             Disabled |\n",
            "+-------------------------------+----------------------+----------------------+\n",
            "                                                                               \n",
            "+-----------------------------------------------------------------------------+\n",
            "| Processes:                                                                  |\n",
            "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
            "|        ID   ID                                                   Usage      |\n",
            "|=============================================================================|\n",
            "|  No running processes found                                                 |\n",
            "+-----------------------------------------------------------------------------+\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Install dependencies\n",
        "\n",
        "%pip uninstall -y protobuf\n",
        "%pip install -r 1_Detection_Model/requirements.txt\n",
        "%pip install paddlepaddle-gpu==2.4.1.post112 -f https://www.paddlepaddle.org.cn/whl/linux/mkl/avx/stable.html\n",
        "%pip install -r 3_Segmentation_Model/requirements.txt"
      ],
      "metadata": {
        "id": "2YXbNW9gTiEC",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "db391066-e0b7-4a26-c0c2-3906a052850f"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found existing installation: protobuf 3.20.3\n",
            "Uninstalling protobuf-3.20.3:\n",
            "  Successfully uninstalled protobuf-3.20.3\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: matplotlib>=3.2.2 in /usr/local/lib/python3.10/dist-packages (from -r 1_Detection_Model/requirements.txt (line 5)) (3.7.1)\n",
            "Requirement already satisfied: numpy>=1.18.5 in /usr/local/lib/python3.10/dist-packages (from -r 1_Detection_Model/requirements.txt (line 6)) (1.22.4)\n",
            "Requirement already satisfied: opencv-python>=4.1.1 in /usr/local/lib/python3.10/dist-packages (from -r 1_Detection_Model/requirements.txt (line 7)) (4.7.0.72)\n",
            "Requirement already satisfied: Pillow>=7.1.2 in /usr/local/lib/python3.10/dist-packages (from -r 1_Detection_Model/requirements.txt (line 8)) (8.4.0)\n",
            "Requirement already satisfied: PyYAML>=5.3.1 in /usr/local/lib/python3.10/dist-packages (from -r 1_Detection_Model/requirements.txt (line 9)) (6.0)\n",
            "Requirement already satisfied: requests>=2.23.0 in /usr/local/lib/python3.10/dist-packages (from -r 1_Detection_Model/requirements.txt (line 10)) (2.27.1)\n",
            "Requirement already satisfied: scipy>=1.4.1 in /usr/local/lib/python3.10/dist-packages (from -r 1_Detection_Model/requirements.txt (line 11)) (1.10.1)\n",
            "Requirement already satisfied: torch>=1.7.0 in /usr/local/lib/python3.10/dist-packages (from -r 1_Detection_Model/requirements.txt (line 12)) (2.0.1+cu118)\n",
            "Requirement already satisfied: torchvision>=0.8.1 in /usr/local/lib/python3.10/dist-packages (from -r 1_Detection_Model/requirements.txt (line 13)) (0.15.2+cu118)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from -r 1_Detection_Model/requirements.txt (line 14)) (4.65.0)\n",
            "Collecting protobuf<=3.20.1 (from -r 1_Detection_Model/requirements.txt (line 15))\n",
            "  Downloading protobuf-3.20.1-cp310-cp310-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (1.1 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.1/1.1 MB\u001b[0m \u001b[31m14.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: tensorboard>=2.4.1 in /usr/local/lib/python3.10/dist-packages (from -r 1_Detection_Model/requirements.txt (line 18)) (2.12.2)\n",
            "Requirement already satisfied: pandas>=1.1.4 in /usr/local/lib/python3.10/dist-packages (from -r 1_Detection_Model/requirements.txt (line 23)) (1.5.3)\n",
            "Requirement already satisfied: seaborn>=0.11.0 in /usr/local/lib/python3.10/dist-packages (from -r 1_Detection_Model/requirements.txt (line 24)) (0.12.2)\n",
            "Requirement already satisfied: ipython in /usr/local/lib/python3.10/dist-packages (from -r 1_Detection_Model/requirements.txt (line 38)) (7.34.0)\n",
            "Requirement already satisfied: psutil in /usr/local/lib/python3.10/dist-packages (from -r 1_Detection_Model/requirements.txt (line 39)) (5.9.5)\n",
            "Collecting thop>=0.1.1 (from -r 1_Detection_Model/requirements.txt (line 40))\n",
            "  Downloading thop-0.1.1.post2209072238-py3-none-any.whl (15 kB)\n",
            "Requirement already satisfied: pycocotools>=2.0 in /usr/local/lib/python3.10/dist-packages (from -r 1_Detection_Model/requirements.txt (line 42)) (2.0.6)\n",
            "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.2.2->-r 1_Detection_Model/requirements.txt (line 5)) (1.0.7)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.2.2->-r 1_Detection_Model/requirements.txt (line 5)) (0.11.0)\n",
            "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.2.2->-r 1_Detection_Model/requirements.txt (line 5)) (4.39.3)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.2.2->-r 1_Detection_Model/requirements.txt (line 5)) (1.4.4)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.2.2->-r 1_Detection_Model/requirements.txt (line 5)) (23.1)\n",
            "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.2.2->-r 1_Detection_Model/requirements.txt (line 5)) (3.0.9)\n",
            "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.2.2->-r 1_Detection_Model/requirements.txt (line 5)) (2.8.2)\n",
            "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests>=2.23.0->-r 1_Detection_Model/requirements.txt (line 10)) (1.26.15)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests>=2.23.0->-r 1_Detection_Model/requirements.txt (line 10)) (2022.12.7)\n",
            "Requirement already satisfied: charset-normalizer~=2.0.0 in /usr/local/lib/python3.10/dist-packages (from requests>=2.23.0->-r 1_Detection_Model/requirements.txt (line 10)) (2.0.12)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests>=2.23.0->-r 1_Detection_Model/requirements.txt (line 10)) (3.4)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from torch>=1.7.0->-r 1_Detection_Model/requirements.txt (line 12)) (3.12.0)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.10/dist-packages (from torch>=1.7.0->-r 1_Detection_Model/requirements.txt (line 12)) (4.5.0)\n",
            "Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch>=1.7.0->-r 1_Detection_Model/requirements.txt (line 12)) (1.11.1)\n",
            "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch>=1.7.0->-r 1_Detection_Model/requirements.txt (line 12)) (3.1)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch>=1.7.0->-r 1_Detection_Model/requirements.txt (line 12)) (3.1.2)\n",
            "Requirement already satisfied: triton==2.0.0 in /usr/local/lib/python3.10/dist-packages (from torch>=1.7.0->-r 1_Detection_Model/requirements.txt (line 12)) (2.0.0)\n",
            "Requirement already satisfied: cmake in /usr/local/lib/python3.10/dist-packages (from triton==2.0.0->torch>=1.7.0->-r 1_Detection_Model/requirements.txt (line 12)) (3.25.2)\n",
            "Requirement already satisfied: lit in /usr/local/lib/python3.10/dist-packages (from triton==2.0.0->torch>=1.7.0->-r 1_Detection_Model/requirements.txt (line 12)) (16.0.5)\n",
            "Requirement already satisfied: absl-py>=0.4 in /usr/local/lib/python3.10/dist-packages (from tensorboard>=2.4.1->-r 1_Detection_Model/requirements.txt (line 18)) (1.4.0)\n",
            "Requirement already satisfied: grpcio>=1.48.2 in /usr/local/lib/python3.10/dist-packages (from tensorboard>=2.4.1->-r 1_Detection_Model/requirements.txt (line 18)) (1.54.0)\n",
            "Requirement already satisfied: google-auth<3,>=1.6.3 in /usr/local/lib/python3.10/dist-packages (from tensorboard>=2.4.1->-r 1_Detection_Model/requirements.txt (line 18)) (2.17.3)\n",
            "Requirement already satisfied: google-auth-oauthlib<1.1,>=0.5 in /usr/local/lib/python3.10/dist-packages (from tensorboard>=2.4.1->-r 1_Detection_Model/requirements.txt (line 18)) (1.0.0)\n",
            "Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.10/dist-packages (from tensorboard>=2.4.1->-r 1_Detection_Model/requirements.txt (line 18)) (3.4.3)\n",
            "Requirement already satisfied: setuptools>=41.0.0 in /usr/local/lib/python3.10/dist-packages (from tensorboard>=2.4.1->-r 1_Detection_Model/requirements.txt (line 18)) (67.7.2)\n",
            "Requirement already satisfied: tensorboard-data-server<0.8.0,>=0.7.0 in /usr/local/lib/python3.10/dist-packages (from tensorboard>=2.4.1->-r 1_Detection_Model/requirements.txt (line 18)) (0.7.0)\n",
            "Requirement already satisfied: tensorboard-plugin-wit>=1.6.0 in /usr/local/lib/python3.10/dist-packages (from tensorboard>=2.4.1->-r 1_Detection_Model/requirements.txt (line 18)) (1.8.1)\n",
            "Requirement already satisfied: werkzeug>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from tensorboard>=2.4.1->-r 1_Detection_Model/requirements.txt (line 18)) (2.3.0)\n",
            "Requirement already satisfied: wheel>=0.26 in /usr/local/lib/python3.10/dist-packages (from tensorboard>=2.4.1->-r 1_Detection_Model/requirements.txt (line 18)) (0.40.0)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=1.1.4->-r 1_Detection_Model/requirements.txt (line 23)) (2022.7.1)\n",
            "Collecting jedi>=0.16 (from ipython->-r 1_Detection_Model/requirements.txt (line 38))\n",
            "  Downloading jedi-0.18.2-py2.py3-none-any.whl (1.6 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.6/1.6 MB\u001b[0m \u001b[31m68.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: decorator in /usr/local/lib/python3.10/dist-packages (from ipython->-r 1_Detection_Model/requirements.txt (line 38)) (4.4.2)\n",
            "Requirement already satisfied: pickleshare in /usr/local/lib/python3.10/dist-packages (from ipython->-r 1_Detection_Model/requirements.txt (line 38)) (0.7.5)\n",
            "Requirement already satisfied: traitlets>=4.2 in /usr/local/lib/python3.10/dist-packages (from ipython->-r 1_Detection_Model/requirements.txt (line 38)) (5.7.1)\n",
            "Requirement already satisfied: prompt-toolkit!=3.0.0,!=3.0.1,<3.1.0,>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from ipython->-r 1_Detection_Model/requirements.txt (line 38)) (3.0.38)\n",
            "Requirement already satisfied: pygments in /usr/local/lib/python3.10/dist-packages (from ipython->-r 1_Detection_Model/requirements.txt (line 38)) (2.14.0)\n",
            "Requirement already satisfied: backcall in /usr/local/lib/python3.10/dist-packages (from ipython->-r 1_Detection_Model/requirements.txt (line 38)) (0.2.0)\n",
            "Requirement already satisfied: matplotlib-inline in /usr/local/lib/python3.10/dist-packages (from ipython->-r 1_Detection_Model/requirements.txt (line 38)) (0.1.6)\n",
            "Requirement already satisfied: pexpect>4.3 in /usr/local/lib/python3.10/dist-packages (from ipython->-r 1_Detection_Model/requirements.txt (line 38)) (4.8.0)\n",
            "Requirement already satisfied: cachetools<6.0,>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from google-auth<3,>=1.6.3->tensorboard>=2.4.1->-r 1_Detection_Model/requirements.txt (line 18)) (5.3.0)\n",
            "Requirement already satisfied: pyasn1-modules>=0.2.1 in /usr/local/lib/python3.10/dist-packages (from google-auth<3,>=1.6.3->tensorboard>=2.4.1->-r 1_Detection_Model/requirements.txt (line 18)) (0.3.0)\n",
            "Requirement already satisfied: six>=1.9.0 in /usr/local/lib/python3.10/dist-packages (from google-auth<3,>=1.6.3->tensorboard>=2.4.1->-r 1_Detection_Model/requirements.txt (line 18)) (1.16.0)\n",
            "Requirement already satisfied: rsa<5,>=3.1.4 in /usr/local/lib/python3.10/dist-packages (from google-auth<3,>=1.6.3->tensorboard>=2.4.1->-r 1_Detection_Model/requirements.txt (line 18)) (4.9)\n",
            "Requirement already satisfied: requests-oauthlib>=0.7.0 in /usr/local/lib/python3.10/dist-packages (from google-auth-oauthlib<1.1,>=0.5->tensorboard>=2.4.1->-r 1_Detection_Model/requirements.txt (line 18)) (1.3.1)\n",
            "Requirement already satisfied: parso<0.9.0,>=0.8.0 in /usr/local/lib/python3.10/dist-packages (from jedi>=0.16->ipython->-r 1_Detection_Model/requirements.txt (line 38)) (0.8.3)\n",
            "Requirement already satisfied: ptyprocess>=0.5 in /usr/local/lib/python3.10/dist-packages (from pexpect>4.3->ipython->-r 1_Detection_Model/requirements.txt (line 38)) (0.7.0)\n",
            "Requirement already satisfied: wcwidth in /usr/local/lib/python3.10/dist-packages (from prompt-toolkit!=3.0.0,!=3.0.1,<3.1.0,>=2.0.0->ipython->-r 1_Detection_Model/requirements.txt (line 38)) (0.2.6)\n",
            "Requirement already satisfied: MarkupSafe>=2.1.1 in /usr/local/lib/python3.10/dist-packages (from werkzeug>=1.0.1->tensorboard>=2.4.1->-r 1_Detection_Model/requirements.txt (line 18)) (2.1.2)\n",
            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch>=1.7.0->-r 1_Detection_Model/requirements.txt (line 12)) (1.3.0)\n",
            "Requirement already satisfied: pyasn1<0.6.0,>=0.4.6 in /usr/local/lib/python3.10/dist-packages (from pyasn1-modules>=0.2.1->google-auth<3,>=1.6.3->tensorboard>=2.4.1->-r 1_Detection_Model/requirements.txt (line 18)) (0.5.0)\n",
            "Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.10/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<1.1,>=0.5->tensorboard>=2.4.1->-r 1_Detection_Model/requirements.txt (line 18)) (3.2.2)\n",
            "Installing collected packages: protobuf, jedi, thop\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "google-api-core 2.11.0 requires protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.19.5, but you have protobuf 3.20.1 which is incompatible.\n",
            "google-cloud-bigquery 3.9.0 requires protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.19.5, but you have protobuf 3.20.1 which is incompatible.\n",
            "google-cloud-bigquery-storage 2.19.1 requires protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.19.5, but you have protobuf 3.20.1 which is incompatible.\n",
            "google-cloud-datastore 2.15.1 requires protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.19.5, but you have protobuf 3.20.1 which is incompatible.\n",
            "google-cloud-firestore 2.11.0 requires protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.19.5, but you have protobuf 3.20.1 which is incompatible.\n",
            "google-cloud-language 2.9.1 requires protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.19.5, but you have protobuf 3.20.1 which is incompatible.\n",
            "google-cloud-translate 3.11.1 requires protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.19.5, but you have protobuf 3.20.1 which is incompatible.\n",
            "googleapis-common-protos 1.59.0 requires protobuf!=3.20.0,!=3.20.1,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.19.5, but you have protobuf 3.20.1 which is incompatible.\n",
            "tensorflow 2.12.0 requires protobuf!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.20.3, but you have protobuf 3.20.1 which is incompatible.\n",
            "tensorflow-metadata 1.13.1 requires protobuf<5,>=3.20.3, but you have protobuf 3.20.1 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0mSuccessfully installed jedi-0.18.2 protobuf-3.20.1 thop-0.1.1.post2209072238\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "google"
                ]
              }
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Looking in links: https://www.paddlepaddle.org.cn/whl/linux/mkl/avx/stable.html\n",
            "Collecting paddlepaddle-gpu==2.4.1.post112\n",
            "  Downloading https://paddle-wheel.bj.bcebos.com/2.4.1/linux/linux-gpu-cuda11.2-cudnn8-mkl-gcc8.2-avx/paddlepaddle_gpu-2.4.1.post112-cp310-cp310-linux_x86_64.whl (547.9 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m547.9/547.9 MB\u001b[0m \u001b[31m1.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: requests>=2.20.0 in /usr/local/lib/python3.10/dist-packages (from paddlepaddle-gpu==2.4.1.post112) (2.27.1)\n",
            "Requirement already satisfied: numpy>=1.13 in /usr/local/lib/python3.10/dist-packages (from paddlepaddle-gpu==2.4.1.post112) (1.22.4)\n",
            "Collecting protobuf<=3.20.0,>=3.1.0 (from paddlepaddle-gpu==2.4.1.post112)\n",
            "  Downloading protobuf-3.20.0-cp310-cp310-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (1.1 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.1/1.1 MB\u001b[0m \u001b[31m15.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: Pillow in /usr/local/lib/python3.10/dist-packages (from paddlepaddle-gpu==2.4.1.post112) (8.4.0)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.10/dist-packages (from paddlepaddle-gpu==2.4.1.post112) (1.16.0)\n",
            "Requirement already satisfied: decorator in /usr/local/lib/python3.10/dist-packages (from paddlepaddle-gpu==2.4.1.post112) (4.4.2)\n",
            "Collecting astor (from paddlepaddle-gpu==2.4.1.post112)\n",
            "  Downloading astor-0.8.1-py2.py3-none-any.whl (27 kB)\n",
            "Collecting paddle-bfloat==0.1.7 (from paddlepaddle-gpu==2.4.1.post112)\n",
            "  Downloading paddle_bfloat-0.1.7-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (383 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m383.2/383.2 kB\u001b[0m \u001b[31m37.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: opt-einsum==3.3.0 in /usr/local/lib/python3.10/dist-packages (from paddlepaddle-gpu==2.4.1.post112) (3.3.0)\n",
            "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests>=2.20.0->paddlepaddle-gpu==2.4.1.post112) (1.26.15)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests>=2.20.0->paddlepaddle-gpu==2.4.1.post112) (2022.12.7)\n",
            "Requirement already satisfied: charset-normalizer~=2.0.0 in /usr/local/lib/python3.10/dist-packages (from requests>=2.20.0->paddlepaddle-gpu==2.4.1.post112) (2.0.12)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests>=2.20.0->paddlepaddle-gpu==2.4.1.post112) (3.4)\n",
            "Installing collected packages: paddle-bfloat, protobuf, astor, paddlepaddle-gpu\n",
            "  Attempting uninstall: protobuf\n",
            "    Found existing installation: protobuf 3.20.1\n",
            "    Uninstalling protobuf-3.20.1:\n",
            "      Successfully uninstalled protobuf-3.20.1\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "google-api-core 2.11.0 requires protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.19.5, but you have protobuf 3.20.0 which is incompatible.\n",
            "google-cloud-bigquery 3.9.0 requires protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.19.5, but you have protobuf 3.20.0 which is incompatible.\n",
            "google-cloud-bigquery-storage 2.19.1 requires protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.19.5, but you have protobuf 3.20.0 which is incompatible.\n",
            "google-cloud-datastore 2.15.1 requires protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.19.5, but you have protobuf 3.20.0 which is incompatible.\n",
            "google-cloud-firestore 2.11.0 requires protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.19.5, but you have protobuf 3.20.0 which is incompatible.\n",
            "google-cloud-language 2.9.1 requires protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.19.5, but you have protobuf 3.20.0 which is incompatible.\n",
            "google-cloud-translate 3.11.1 requires protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.19.5, but you have protobuf 3.20.0 which is incompatible.\n",
            "googleapis-common-protos 1.59.0 requires protobuf!=3.20.0,!=3.20.1,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.19.5, but you have protobuf 3.20.0 which is incompatible.\n",
            "tensorflow 2.12.0 requires protobuf!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.20.3, but you have protobuf 3.20.0 which is incompatible.\n",
            "tensorflow-metadata 1.13.1 requires protobuf<5,>=3.20.3, but you have protobuf 3.20.0 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0mSuccessfully installed astor-0.8.1 paddle-bfloat-0.1.7 paddlepaddle-gpu-2.4.1.post112 protobuf-3.20.0\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "google"
                ]
              }
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from -r 3_Segmentation_Model/requirements.txt (line 1)) (6.0)\n",
            "Collecting visualdl>=2.2.0 (from -r 3_Segmentation_Model/requirements.txt (line 2))\n",
            "  Downloading visualdl-2.5.2-py3-none-any.whl (6.3 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m6.3/6.3 MB\u001b[0m \u001b[31m55.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: opencv-python in /usr/local/lib/python3.10/dist-packages (from -r 3_Segmentation_Model/requirements.txt (line 3)) (4.7.0.72)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from -r 3_Segmentation_Model/requirements.txt (line 4)) (4.65.0)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from -r 3_Segmentation_Model/requirements.txt (line 5)) (3.12.0)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.10/dist-packages (from -r 3_Segmentation_Model/requirements.txt (line 6)) (1.10.1)\n",
            "Requirement already satisfied: prettytable in /usr/local/lib/python3.10/dist-packages (from -r 3_Segmentation_Model/requirements.txt (line 7)) (0.7.2)\n",
            "Collecting sklearn==0.0 (from -r 3_Segmentation_Model/requirements.txt (line 8))\n",
            "  Downloading sklearn-0.0.tar.gz (1.1 kB)\n",
            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.10/dist-packages (from sklearn==0.0->-r 3_Segmentation_Model/requirements.txt (line 8)) (1.2.2)\n",
            "Collecting bce-python-sdk (from visualdl>=2.2.0->-r 3_Segmentation_Model/requirements.txt (line 2))\n",
            "  Downloading bce_python_sdk-0.8.83-py3-none-any.whl (210 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m210.5/210.5 kB\u001b[0m \u001b[31m26.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: flask>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from visualdl>=2.2.0->-r 3_Segmentation_Model/requirements.txt (line 2)) (2.2.4)\n",
            "Collecting Flask-Babel>=3.0.0 (from visualdl>=2.2.0->-r 3_Segmentation_Model/requirements.txt (line 2))\n",
            "  Downloading flask_babel-3.1.0-py3-none-any.whl (9.6 kB)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from visualdl>=2.2.0->-r 3_Segmentation_Model/requirements.txt (line 2)) (1.22.4)\n",
            "Requirement already satisfied: Pillow>=7.0.0 in /usr/local/lib/python3.10/dist-packages (from visualdl>=2.2.0->-r 3_Segmentation_Model/requirements.txt (line 2)) (8.4.0)\n",
            "Requirement already satisfied: protobuf>=3.20.0 in /usr/local/lib/python3.10/dist-packages (from visualdl>=2.2.0->-r 3_Segmentation_Model/requirements.txt (line 2)) (3.20.0)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from visualdl>=2.2.0->-r 3_Segmentation_Model/requirements.txt (line 2)) (2.27.1)\n",
            "Requirement already satisfied: six>=1.14.0 in /usr/local/lib/python3.10/dist-packages (from visualdl>=2.2.0->-r 3_Segmentation_Model/requirements.txt (line 2)) (1.16.0)\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.10/dist-packages (from visualdl>=2.2.0->-r 3_Segmentation_Model/requirements.txt (line 2)) (3.7.1)\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from visualdl>=2.2.0->-r 3_Segmentation_Model/requirements.txt (line 2)) (1.5.3)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from visualdl>=2.2.0->-r 3_Segmentation_Model/requirements.txt (line 2)) (23.1)\n",
            "Collecting x2paddle>=1.4.0 (from visualdl>=2.2.0->-r 3_Segmentation_Model/requirements.txt (line 2))\n",
            "  Downloading x2paddle-1.4.1-py3-none-any.whl (316 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m316.2/316.2 kB\u001b[0m \u001b[31m35.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting paddle2onnx>=1.0.5 (from visualdl>=2.2.0->-r 3_Segmentation_Model/requirements.txt (line 2))\n",
            "  Downloading paddle2onnx-1.0.6-cp310-cp310-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (3.1 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.1/3.1 MB\u001b[0m \u001b[31m102.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting rarfile (from visualdl>=2.2.0->-r 3_Segmentation_Model/requirements.txt (line 2))\n",
            "  Downloading rarfile-4.0-py3-none-any.whl (28 kB)\n",
            "Collecting gradio==3.11.0 (from visualdl>=2.2.0->-r 3_Segmentation_Model/requirements.txt (line 2))\n",
            "  Downloading gradio-3.11.0-py3-none-any.whl (11.6 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m11.6/11.6 MB\u001b[0m \u001b[31m103.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting tritonclient[all] (from visualdl>=2.2.0->-r 3_Segmentation_Model/requirements.txt (line 2))\n",
            "  Downloading tritonclient-2.33.0-py3-none-manylinux1_x86_64.whl (11.8 MB)\n",
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            "\u001b[?25hRequirement already satisfied: psutil in /usr/local/lib/python3.10/dist-packages (from visualdl>=2.2.0->-r 3_Segmentation_Model/requirements.txt (line 2)) (5.9.5)\n",
            "Collecting onnx>=1.6.0 (from visualdl>=2.2.0->-r 3_Segmentation_Model/requirements.txt (line 2))\n",
            "  Downloading onnx-1.14.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (14.6 MB)\n",
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            "\u001b[?25hCollecting aiohttp (from gradio==3.11.0->visualdl>=2.2.0->-r 3_Segmentation_Model/requirements.txt (line 2))\n",
            "  Downloading aiohttp-3.8.4-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.0 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.0/1.0 MB\u001b[0m \u001b[31m78.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting fastapi (from gradio==3.11.0->visualdl>=2.2.0->-r 3_Segmentation_Model/requirements.txt (line 2))\n",
            "  Downloading fastapi-0.95.2-py3-none-any.whl (56 kB)\n",
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            "\u001b[?25hCollecting ffmpy (from gradio==3.11.0->visualdl>=2.2.0->-r 3_Segmentation_Model/requirements.txt (line 2))\n",
            "  Downloading ffmpy-0.3.0.tar.gz (4.8 kB)\n",
            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from gradio==3.11.0->visualdl>=2.2.0->-r 3_Segmentation_Model/requirements.txt (line 2)) (2023.4.0)\n",
            "Collecting h11<0.13,>=0.11 (from gradio==3.11.0->visualdl>=2.2.0->-r 3_Segmentation_Model/requirements.txt (line 2))\n",
            "  Downloading h11-0.12.0-py3-none-any.whl (54 kB)\n",
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            "\u001b[?25hCollecting httpx (from gradio==3.11.0->visualdl>=2.2.0->-r 3_Segmentation_Model/requirements.txt (line 2))\n",
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            "Collecting uc-micro-py (from linkify-it-py<3,>=1->markdown-it-py[linkify,plugins]->gradio==3.11.0->visualdl>=2.2.0->-r 3_Segmentation_Model/requirements.txt (line 2))\n",
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            "Building wheels for collected packages: sklearn, ffmpy\n",
            "  Building wheel for sklearn (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for sklearn: filename=sklearn-0.0-py2.py3-none-any.whl size=1302 sha256=791f8c21f0cf6229166061e7310432552de2810aae3f8768e81596ac222221d3\n",
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            "Successfully built sklearn ffmpy\n",
            "Installing collected packages: rarfile, pydub, paddle2onnx, ffmpy, brotli, zope.interface, zope.event, websockets, uc-micro-py, python-rapidjson, python-multipart, pycryptodome, protobuf, orjson, multidict, h11, frozenlist, bcrypt, async-timeout, yarl, x2paddle, uvicorn, tritonclient, starlette, pynacl, onnx, mdit-py-plugins, linkify-it-py, httpcore, gevent, bce-python-sdk, aiosignal, sklearn, paramiko, httpx, geventhttpclient, Flask-Babel, fastapi, aiohttp, gradio, visualdl\n",
            "  Attempting uninstall: protobuf\n",
            "    Found existing installation: protobuf 3.20.0\n",
            "    Uninstalling protobuf-3.20.0:\n",
            "      Successfully uninstalled protobuf-3.20.0\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
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            "\u001b[0mSuccessfully installed Flask-Babel-3.1.0 aiohttp-3.8.4 aiosignal-1.3.1 async-timeout-4.0.2 bce-python-sdk-0.8.83 bcrypt-4.0.1 brotli-1.0.9 fastapi-0.95.2 ffmpy-0.3.0 frozenlist-1.3.3 gevent-22.10.2 geventhttpclient-2.0.2 gradio-3.11.0 h11-0.12.0 httpcore-0.15.0 httpx-0.24.1 linkify-it-py-2.0.2 mdit-py-plugins-0.3.5 multidict-6.0.4 onnx-1.14.0 orjson-3.8.12 paddle2onnx-1.0.6 paramiko-3.1.0 protobuf-3.20.3 pycryptodome-3.18.0 pydub-0.25.1 pynacl-1.5.0 python-multipart-0.0.6 python-rapidjson-1.10 rarfile-4.0 sklearn-0.0 starlette-0.27.0 tritonclient-2.33.0 uc-micro-py-1.0.2 uvicorn-0.22.0 visualdl-2.5.2 websockets-11.0.3 x2paddle-1.4.1 yarl-1.9.2 zope.event-4.6 zope.interface-6.0\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "google"
                ]
              }
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Sanity check - 1\n",
        "\n",
        "%cd /content/DT_SegNet/1_Detection_Model\n",
        "\n",
        "import torch\n",
        "import utils\n",
        "\n",
        "utils.notebook_init()\n"
      ],
      "metadata": {
        "id": "-NIUcvRMTn2N",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "45ea7243-6de8-4987-9e24-f2f713d8bc5f"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "YOLOv5 🚀 2023-5-19 Python-3.10.11 torch-2.0.1+cu118 CUDA:0 (NVIDIA A100-SXM4-40GB, 40514MiB)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Setup complete ✅ (12 CPUs, 83.5 GB RAM, 26.4/78.2 GB disk)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<module 'IPython.display' from '/usr/local/lib/python3.10/dist-packages/IPython/display.py'>"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Sanity check - 2\n",
        "\n",
        "import paddle\n",
        "\n",
        "paddle.utils.run_check()\n",
        "print(paddle.__version__)\n"
      ],
      "metadata": {
        "id": "EjpwpA54Wa_I",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "14102a9e-48a4-4912-a3a1-59693ded5f61"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING: OMP_NUM_THREADS set to 8, not 1. The computation speed will not be optimized if you use data parallel. It will fail if this PaddlePaddle binary is compiled with OpenBlas since OpenBlas does not support multi-threads.\n",
            "PLEASE USE OMP_NUM_THREADS WISELY.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Running verify PaddlePaddle program ... \n",
            "PaddlePaddle works well on 1 GPU.\n",
            "PaddlePaddle works well on 1 GPUs.\n",
            "PaddlePaddle is installed successfully! Let's start deep learning with PaddlePaddle now.\n",
            "2.4.1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Import packages\n",
        "from pathlib import Path\n",
        "from PIL import Image\n",
        "from tqdm.notebook import tqdm\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "from matplotlib import pyplot as plt\n",
        "from google.colab import files, drive"
      ],
      "metadata": {
        "id": "Ud9uqddkcXIH",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "980401d0-5e97-4cc6-a4c1-b03c3e027741"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Select model\n",
        "\n",
        "detection_model = \"https://github.com/xiazeyu/DT_SegNet/releases/latest/download/detection.pt\" #@param [\"https://github.com/xiazeyu/DT_SegNet/releases/latest/download/detection.pt\"] {allow-input: true}\n",
        "segmentation_model = \"https://github.com/xiazeyu/DT_SegNet/releases/latest/download/segmentation.pdparams\" #@param [\"https://github.com/xiazeyu/DT_SegNet/releases/latest/download/segmentation.pdparams\"] {allow-input: true}\n"
      ],
      "metadata": {
        "id": "GrwoxiHuWk8t"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Download model\n",
        "Path('/content/DT_SegNet/Models').mkdir(exist_ok=True, parents=True)\n",
        "\n",
        "detection_model_path = \"/content/DT_SegNet/Models/detection.pt\"\n",
        "segmentation_model_path = \"/content/DT_SegNet/Models/segmentation.pdparams\"\n",
        "\n",
        "\n",
        "!wget -O {detection_model_path} {detection_model}\n",
        "!wget -O {segmentation_model_path} {segmentation_model}\n",
        "\n",
        "!stat {detection_model_path}\n",
        "!stat {segmentation_model_path}\n"
      ],
      "metadata": {
        "id": "z6XrRMuYXjkC",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ea40db43-6ef5-450b-a5a1-54c54c68c506"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "Access: 2023-05-19 12:29:07.000000000 +0000\n",
            "Modify: 2023-03-13 16:47:02.000000000 +0000\n",
            "Change: 2023-05-19 12:29:07.032469148 +0000\n",
            " Birth: -\n",
            "  File: /content/DT_SegNet/Models/segmentation.pdparams\n",
            "  Size: 54730932  \tBlocks: 106904     IO Block: 4096   regular file\n",
            "Device: 32h/50d\tInode: 1186040     Links: 1\n",
            "Access: (0644/-rw-r--r--)  Uid: (    0/    root)   Gid: (    0/    root)\n",
            "Access: 2023-05-19 12:29:07.000000000 +0000\n",
            "Modify: 2023-03-13 16:47:16.000000000 +0000\n",
            "Change: 2023-05-19 12:29:07.958553725 +0000\n",
            " Birth: -\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Upload image file from local filesystem\n",
        "\n",
        "Path('/content/DT_SegNet/FineTuneDataset/images').mkdir(exist_ok=True, parents=True)\n",
        "%cd /content/DT_SegNet/FineTuneDataset/images\n",
        "\n",
        "uploaded = files.upload()\n",
        "\n",
        "for fn in uploaded.keys():\n",
        "  print('User uploaded file \"{name}\" with length {length} bytes'.format(\n",
        "      name=fn, length=len(uploaded[fn])))\n",
        "\n",
        "%cd /content/DT_SegNet"
      ],
      "metadata": {
        "id": "pbfdZm1kYjmT",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 239
        },
        "outputId": "e797befb-2db6-4c28-8ce0-d2904854f5b0"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/DT_SegNet/FineTuneDataset/images\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-d27270a5-73d0-4abb-8dca-7d28910d53c7\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-d27270a5-73d0-4abb-8dca-7d28910d53c7\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script>// Copyright 2017 Google LLC\n",
              "//\n",
              "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
              "// you may not use this file except in compliance with the License.\n",
              "// You may obtain a copy of the License at\n",
              "//\n",
              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
              "//\n",
              "// Unless required by applicable law or agreed to in writing, software\n",
              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
              "// See the License for the specific language governing permissions and\n",
              "// limitations under the License.\n",
              "\n",
              "/**\n",
              " * @fileoverview Helpers for google.colab Python module.\n",
              " */\n",
              "(function(scope) {\n",
              "function span(text, styleAttributes = {}) {\n",
              "  const element = document.createElement('span');\n",
              "  element.textContent = text;\n",
              "  for (const key of Object.keys(styleAttributes)) {\n",
              "    element.style[key] = styleAttributes[key];\n",
              "  }\n",
              "  return element;\n",
              "}\n",
              "\n",
              "// Max number of bytes which will be uploaded at a time.\n",
              "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
              "\n",
              "function _uploadFiles(inputId, outputId) {\n",
              "  const steps = uploadFilesStep(inputId, outputId);\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  // Cache steps on the outputElement to make it available for the next call\n",
              "  // to uploadFilesContinue from Python.\n",
              "  outputElement.steps = steps;\n",
              "\n",
              "  return _uploadFilesContinue(outputId);\n",
              "}\n",
              "\n",
              "// This is roughly an async generator (not supported in the browser yet),\n",
              "// where there are multiple asynchronous steps and the Python side is going\n",
              "// to poll for completion of each step.\n",
              "// This uses a Promise to block the python side on completion of each step,\n",
              "// then passes the result of the previous step as the input to the next step.\n",
              "function _uploadFilesContinue(outputId) {\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  const steps = outputElement.steps;\n",
              "\n",
              "  const next = steps.next(outputElement.lastPromiseValue);\n",
              "  return Promise.resolve(next.value.promise).then((value) => {\n",
              "    // Cache the last promise value to make it available to the next\n",
              "    // step of the generator.\n",
              "    outputElement.lastPromiseValue = value;\n",
              "    return next.value.response;\n",
              "  });\n",
              "}\n",
              "\n",
              "/**\n",
              " * Generator function which is called between each async step of the upload\n",
              " * process.\n",
              " * @param {string} inputId Element ID of the input file picker element.\n",
              " * @param {string} outputId Element ID of the output display.\n",
              " * @return {!Iterable<!Object>} Iterable of next steps.\n",
              " */\n",
              "function* uploadFilesStep(inputId, outputId) {\n",
              "  const inputElement = document.getElementById(inputId);\n",
              "  inputElement.disabled = false;\n",
              "\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  outputElement.innerHTML = '';\n",
              "\n",
              "  const pickedPromise = new Promise((resolve) => {\n",
              "    inputElement.addEventListener('change', (e) => {\n",
              "      resolve(e.target.files);\n",
              "    });\n",
              "  });\n",
              "\n",
              "  const cancel = document.createElement('button');\n",
              "  inputElement.parentElement.appendChild(cancel);\n",
              "  cancel.textContent = 'Cancel upload';\n",
              "  const cancelPromise = new Promise((resolve) => {\n",
              "    cancel.onclick = () => {\n",
              "      resolve(null);\n",
              "    };\n",
              "  });\n",
              "\n",
              "  // Wait for the user to pick the files.\n",
              "  const files = yield {\n",
              "    promise: Promise.race([pickedPromise, cancelPromise]),\n",
              "    response: {\n",
              "      action: 'starting',\n",
              "    }\n",
              "  };\n",
              "\n",
              "  cancel.remove();\n",
              "\n",
              "  // Disable the input element since further picks are not allowed.\n",
              "  inputElement.disabled = true;\n",
              "\n",
              "  if (!files) {\n",
              "    return {\n",
              "      response: {\n",
              "        action: 'complete',\n",
              "      }\n",
              "    };\n",
              "  }\n",
              "\n",
              "  for (const file of files) {\n",
              "    const li = document.createElement('li');\n",
              "    li.append(span(file.name, {fontWeight: 'bold'}));\n",
              "    li.append(span(\n",
              "        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
              "        `last modified: ${\n",
              "            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
              "                                    'n/a'} - `));\n",
              "    const percent = span('0% done');\n",
              "    li.appendChild(percent);\n",
              "\n",
              "    outputElement.appendChild(li);\n",
              "\n",
              "    const fileDataPromise = new Promise((resolve) => {\n",
              "      const reader = new FileReader();\n",
              "      reader.onload = (e) => {\n",
              "        resolve(e.target.result);\n",
              "      };\n",
              "      reader.readAsArrayBuffer(file);\n",
              "    });\n",
              "    // Wait for the data to be ready.\n",
              "    let fileData = yield {\n",
              "      promise: fileDataPromise,\n",
              "      response: {\n",
              "        action: 'continue',\n",
              "      }\n",
              "    };\n",
              "\n",
              "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
              "    let position = 0;\n",
              "    do {\n",
              "      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
              "      const chunk = new Uint8Array(fileData, position, length);\n",
              "      position += length;\n",
              "\n",
              "      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
              "      yield {\n",
              "        response: {\n",
              "          action: 'append',\n",
              "          file: file.name,\n",
              "          data: base64,\n",
              "        },\n",
              "      };\n",
              "\n",
              "      let percentDone = fileData.byteLength === 0 ?\n",
              "          100 :\n",
              "          Math.round((position / fileData.byteLength) * 100);\n",
              "      percent.textContent = `${percentDone}% done`;\n",
              "\n",
              "    } while (position < fileData.byteLength);\n",
              "  }\n",
              "\n",
              "  // All done.\n",
              "  yield {\n",
              "    response: {\n",
              "      action: 'complete',\n",
              "    }\n",
              "  };\n",
              "}\n",
              "\n",
              "scope.google = scope.google || {};\n",
              "scope.google.colab = scope.google.colab || {};\n",
              "scope.google.colab._files = {\n",
              "  _uploadFiles,\n",
              "  _uploadFilesContinue,\n",
              "};\n",
              "})(self);\n",
              "</script> "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving 1.png to 1.png\n",
            "Saving 5.png to 5.png\n",
            "Saving 9.png to 9.png\n",
            "User uploaded file \"1.png\" with length 331450 bytes\n",
            "User uploaded file \"5.png\" with length 181865 bytes\n",
            "User uploaded file \"9.png\" with length 296689 bytes\n",
            "/content/DT_SegNet\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Upload segmentation labels generated from EISeg from local filesystem\n",
        "\n",
        "Path('/content/DT_SegNet/FineTuneDataset/labels').mkdir(exist_ok=True, parents=True)\n",
        "%cd /content/DT_SegNet/FineTuneDataset/labels\n",
        "\n",
        "uploaded = files.upload()\n",
        "\n",
        "for fn in uploaded.keys():\n",
        "  print('User uploaded file \"{name}\" with length {length} bytes'.format(\n",
        "      name=fn, length=len(uploaded[fn])))\n",
        "%cd /content/DT_SegNet"
      ],
      "metadata": {
        "id": "oqITAgLmQcZO",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 726
        },
        "outputId": "b47e8c05-a4c5-44b4-fd1f-7c5e616202f7"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/DT_SegNet/FineTuneDataset/labels\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-d126e992-e926-4c17-a627-85c7de915827\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-d126e992-e926-4c17-a627-85c7de915827\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script>// Copyright 2017 Google LLC\n",
              "//\n",
              "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
              "// you may not use this file except in compliance with the License.\n",
              "// You may obtain a copy of the License at\n",
              "//\n",
              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
              "//\n",
              "// Unless required by applicable law or agreed to in writing, software\n",
              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
              "// See the License for the specific language governing permissions and\n",
              "// limitations under the License.\n",
              "\n",
              "/**\n",
              " * @fileoverview Helpers for google.colab Python module.\n",
              " */\n",
              "(function(scope) {\n",
              "function span(text, styleAttributes = {}) {\n",
              "  const element = document.createElement('span');\n",
              "  element.textContent = text;\n",
              "  for (const key of Object.keys(styleAttributes)) {\n",
              "    element.style[key] = styleAttributes[key];\n",
              "  }\n",
              "  return element;\n",
              "}\n",
              "\n",
              "// Max number of bytes which will be uploaded at a time.\n",
              "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
              "\n",
              "function _uploadFiles(inputId, outputId) {\n",
              "  const steps = uploadFilesStep(inputId, outputId);\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  // Cache steps on the outputElement to make it available for the next call\n",
              "  // to uploadFilesContinue from Python.\n",
              "  outputElement.steps = steps;\n",
              "\n",
              "  return _uploadFilesContinue(outputId);\n",
              "}\n",
              "\n",
              "// This is roughly an async generator (not supported in the browser yet),\n",
              "// where there are multiple asynchronous steps and the Python side is going\n",
              "// to poll for completion of each step.\n",
              "// This uses a Promise to block the python side on completion of each step,\n",
              "// then passes the result of the previous step as the input to the next step.\n",
              "function _uploadFilesContinue(outputId) {\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  const steps = outputElement.steps;\n",
              "\n",
              "  const next = steps.next(outputElement.lastPromiseValue);\n",
              "  return Promise.resolve(next.value.promise).then((value) => {\n",
              "    // Cache the last promise value to make it available to the next\n",
              "    // step of the generator.\n",
              "    outputElement.lastPromiseValue = value;\n",
              "    return next.value.response;\n",
              "  });\n",
              "}\n",
              "\n",
              "/**\n",
              " * Generator function which is called between each async step of the upload\n",
              " * process.\n",
              " * @param {string} inputId Element ID of the input file picker element.\n",
              " * @param {string} outputId Element ID of the output display.\n",
              " * @return {!Iterable<!Object>} Iterable of next steps.\n",
              " */\n",
              "function* uploadFilesStep(inputId, outputId) {\n",
              "  const inputElement = document.getElementById(inputId);\n",
              "  inputElement.disabled = false;\n",
              "\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  outputElement.innerHTML = '';\n",
              "\n",
              "  const pickedPromise = new Promise((resolve) => {\n",
              "    inputElement.addEventListener('change', (e) => {\n",
              "      resolve(e.target.files);\n",
              "    });\n",
              "  });\n",
              "\n",
              "  const cancel = document.createElement('button');\n",
              "  inputElement.parentElement.appendChild(cancel);\n",
              "  cancel.textContent = 'Cancel upload';\n",
              "  const cancelPromise = new Promise((resolve) => {\n",
              "    cancel.onclick = () => {\n",
              "      resolve(null);\n",
              "    };\n",
              "  });\n",
              "\n",
              "  // Wait for the user to pick the files.\n",
              "  const files = yield {\n",
              "    promise: Promise.race([pickedPromise, cancelPromise]),\n",
              "    response: {\n",
              "      action: 'starting',\n",
              "    }\n",
              "  };\n",
              "\n",
              "  cancel.remove();\n",
              "\n",
              "  // Disable the input element since further picks are not allowed.\n",
              "  inputElement.disabled = true;\n",
              "\n",
              "  if (!files) {\n",
              "    return {\n",
              "      response: {\n",
              "        action: 'complete',\n",
              "      }\n",
              "    };\n",
              "  }\n",
              "\n",
              "  for (const file of files) {\n",
              "    const li = document.createElement('li');\n",
              "    li.append(span(file.name, {fontWeight: 'bold'}));\n",
              "    li.append(span(\n",
              "        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
              "        `last modified: ${\n",
              "            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
              "                                    'n/a'} - `));\n",
              "    const percent = span('0% done');\n",
              "    li.appendChild(percent);\n",
              "\n",
              "    outputElement.appendChild(li);\n",
              "\n",
              "    const fileDataPromise = new Promise((resolve) => {\n",
              "      const reader = new FileReader();\n",
              "      reader.onload = (e) => {\n",
              "        resolve(e.target.result);\n",
              "      };\n",
              "      reader.readAsArrayBuffer(file);\n",
              "    });\n",
              "    // Wait for the data to be ready.\n",
              "    let fileData = yield {\n",
              "      promise: fileDataPromise,\n",
              "      response: {\n",
              "        action: 'continue',\n",
              "      }\n",
              "    };\n",
              "\n",
              "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
              "    let position = 0;\n",
              "    do {\n",
              "      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
              "      const chunk = new Uint8Array(fileData, position, length);\n",
              "      position += length;\n",
              "\n",
              "      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
              "      yield {\n",
              "        response: {\n",
              "          action: 'append',\n",
              "          file: file.name,\n",
              "          data: base64,\n",
              "        },\n",
              "      };\n",
              "\n",
              "      let percentDone = fileData.byteLength === 0 ?\n",
              "          100 :\n",
              "          Math.round((position / fileData.byteLength) * 100);\n",
              "      percent.textContent = `${percentDone}% done`;\n",
              "\n",
              "    } while (position < fileData.byteLength);\n",
              "  }\n",
              "\n",
              "  // All done.\n",
              "  yield {\n",
              "    response: {\n",
              "      action: 'complete',\n",
              "    }\n",
              "  };\n",
              "}\n",
              "\n",
              "scope.google = scope.google || {};\n",
              "scope.google.colab = scope.google.colab || {};\n",
              "scope.google.colab._files = {\n",
              "  _uploadFiles,\n",
              "  _uploadFilesContinue,\n",
              "};\n",
              "})(self);\n",
              "</script> "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving 1.json to 1.json\n",
            "Saving 1.png to 1.png\n",
            "Saving 1_cutout.png to 1_cutout.png\n",
            "Saving 1_pseudo.png to 1_pseudo.png\n",
            "Saving 5.json to 5.json\n",
            "Saving 5.png to 5.png\n",
            "Saving 5_cutout.png to 5_cutout.png\n",
            "Saving 5_pseudo.png to 5_pseudo.png\n",
            "Saving 9.json to 9.json\n",
            "Saving 9.png to 9.png\n",
            "Saving 9_cutout.png to 9_cutout.png\n",
            "Saving 9_pseudo.png to 9_pseudo.png\n",
            "User uploaded file \"1.json\" with length 15961 bytes\n",
            "User uploaded file \"1.png\" with length 4837 bytes\n",
            "User uploaded file \"1_cutout.png\" with length 53370 bytes\n",
            "User uploaded file \"1_pseudo.png\" with length 7219 bytes\n",
            "User uploaded file \"5.json\" with length 28162 bytes\n",
            "User uploaded file \"5.png\" with length 7628 bytes\n",
            "User uploaded file \"5_cutout.png\" with length 77261 bytes\n",
            "User uploaded file \"5_pseudo.png\" with length 11203 bytes\n",
            "User uploaded file \"9.json\" with length 24859 bytes\n",
            "User uploaded file \"9.png\" with length 11823 bytes\n",
            "User uploaded file \"9_cutout.png\" with length 99701 bytes\n",
            "User uploaded file \"9_pseudo.png\" with length 18066 bytes\n",
            "/content/DT_SegNet\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%cd /content/DT_SegNet\n",
        "!python 0_Labelling_Tools/1_Segmentation_Label_Flood_Fill.py /content/DT_SegNet/FineTuneDataset/labels /content/DT_SegNet/FineTuneDataset/images\n",
        "!python 0_Labelling_Tools/1_Segmentation_Label_Flood_Fill.py /content/DT_SegNet/FineTuneDataset/labels /content/DT_SegNet/FineTuneDataset/labels\n",
        "!python 0_Labelling_Tools/3_gray2pseudo_color.py /content/DT_SegNet/FineTuneDataset/labels /content/DT_SegNet/FineTuneDataset/segmentation_labels"
      ],
      "metadata": {
        "id": "yA44htktR3Bo",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6c236320-a78a-4c37-f5df-ccfb33681c13"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/DT_SegNet\n",
            "now processing /content/DT_SegNet/FineTuneDataset/labels/9.png.\n",
            "now processing /content/DT_SegNet/FineTuneDataset/labels/5.png.\n",
            "now processing /content/DT_SegNet/FineTuneDataset/labels/1.png.\n",
            "now processing /content/DT_SegNet/FineTuneDataset/labels/9.png.\n",
            "now processing /content/DT_SegNet/FineTuneDataset/labels/5.png.\n",
            "now processing /content/DT_SegNet/FineTuneDataset/labels/1.png.\n",
            "Creating colorful label directory: /content/DT_SegNet/FineTuneDataset/segmentation_labels\n",
            "New label path: /content/DT_SegNet/FineTuneDataset/segmentation_labels/9.png\n",
            "New label path: /content/DT_SegNet/FineTuneDataset/segmentation_labels/5.png\n",
            "New label path: /content/DT_SegNet/FineTuneDataset/segmentation_labels/1.png\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# read YOLO format txt label, and perform dilation\n",
        "def read_labels(label_dir, img, dilation = 1.5):\n",
        "    data = pd.read_csv(str(label_dir), sep=\" \", header=None,\n",
        "                    names=[\"class\", \"x_center\", \"y_center\", \"width\", \"height\"])\n",
        "    img_x, img_y = img.size\n",
        "    data['x0'] = ((data['x_center'] - dilation * data['width'] / 2) * img_x).astype(\"int\")\n",
        "    data['x1'] = ((data['x_center'] + dilation * data['width'] / 2) * img_x).astype(\"int\")\n",
        "    data['y0'] = ((data['y_center'] - dilation * data['height'] / 2) * img_y).astype(\"int\")\n",
        "    data['y1'] = ((data['y_center'] + dilation * data['height'] / 2) * img_y).astype(\"int\")\n",
        "    return data\n",
        "\n",
        "data_dir = Path('/content/DT_SegNet/FineTuneDataset/images')\n",
        "label_dir = Path('/content/DT_SegNet/FineTuneDataset/segmentation_labels')\n",
        "\n",
        "output_root = Path('/content/DT_SegNet/FineTuneDataset/Segmentation_Dataset/')\n",
        "output_data_dir = output_root / 'images'\n",
        "output_label_dir = output_root / 'labels'\n",
        "\n",
        "output_data_dir.mkdir(parents=True, exist_ok=True)\n",
        "output_label_dir.mkdir(parents=True, exist_ok=True)\n",
        "\n",
        "for img_path in list(data_dir.glob('**/*.png')):\n",
        "    print(f'Processing {str(img_path)}')\n",
        "\n",
        "    # process cropped image\n",
        "    img = Image.open(img_path)\n",
        "    img = img.convert(\"L\")\n",
        "    # print(img.format, img.size, img.mode)\n",
        "    labels = read_labels(img_path.with_suffix('.txt'), img)\n",
        "    with tqdm(total=len(labels)) as pbar:\n",
        "        for index, r in labels.iterrows():\n",
        "            box = (r.x0, r.y0, r.x1, r.y1)\n",
        "            region = img.crop(box)\n",
        "            # region.show()\n",
        "            croped_savepath = output_data_dir / f'{img_path.stem}_{index}{img_path.suffix}'\n",
        "            # print(croped_savepath, box)\n",
        "            region.save(croped_savepath)\n",
        "            pbar.update(1)\n",
        "    \n",
        "    # process cropped label\n",
        "    img = Image.open(label_dir / f'{img_path.stem}{img_path.suffix}')\n",
        "    img = img.convert(\"P\")\n",
        "    # print(img.format, img.size, img.mode)\n",
        "    with tqdm(total=len(labels)) as pbar:\n",
        "        for index, r in labels.iterrows():\n",
        "            box = (r.x0, r.y0, r.x1, r.y1)\n",
        "            region = img.crop(box)\n",
        "            # region.show()\n",
        "            croped_savepath = output_label_dir / f'{img_path.stem}_{index}{img_path.suffix}'\n",
        "            # print(croped_savepath, box)\n",
        "            region.save(croped_savepath)\n",
        "            pbar.update(1)\n",
        "\n",
        "datas = pd.DataFrame({\n",
        "    'data': [f'images/{x.name}' for x in sorted(output_data_dir.glob('*.png'))],\n",
        "    'label': [f'labels/{x.name}' for x in sorted(output_label_dir.glob('*.png'))],\n",
        "})\n",
        "\n",
        "datas.to_csv(output_root / f'train.txt', header=None, index=None, sep=' ')"
      ],
      "metadata": {
        "id": "jaEPBm1kQ6fv",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 264,
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        "outputId": "3221c5e9-af03-486c-c76f-951149496a48"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Processing /content/DT_SegNet/FineTuneDataset/images/9.png\n"
          ]
        },
        {
          "output_type": "display_data",
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        },
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Processing /content/DT_SegNet/FineTuneDataset/images/5.png\n"
          ]
        },
        {
          "output_type": "display_data",
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Processing /content/DT_SegNet/FineTuneDataset/images/1.png\n"
          ]
        },
        {
          "output_type": "display_data",
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            "application/vnd.jupyter.widget-view+json": {
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        },
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            "application/vnd.jupyter.widget-view+json": {
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          },
          "metadata": {}
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Finetune detection model"
      ],
      "metadata": {
        "id": "d9Jw325yIubB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Evaluation before finetune\n",
        "%cd /content/DT_SegNet/1_Detection_Model\n",
        "!python val.py --task test --data dtsegnet_finetune.yaml --save-txt --save-conf --img 1280 --weights {detection_model_path}"
      ],
      "metadata": {
        "id": "AkZjwEsQq1B2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1abcc5d7-f95e-47f1-dce6-978c91a6653d"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/DT_SegNet/1_Detection_Model\n",
            "\u001b[34m\u001b[1mval: \u001b[0mdata=/content/DT_SegNet/1_Detection_Model/data/dtsegnet_finetune.yaml, weights=['/content/DT_SegNet/Models/detection.pt'], batch_size=32, imgsz=1280, conf_thres=0.001, iou_thres=0.6, task=test, device=, workers=8, single_cls=False, augment=False, verbose=False, save_txt=True, save_hybrid=False, save_conf=True, save_json=False, project=runs/val, name=exp, exist_ok=False, half=False, dnn=False\n",
            "\u001b[31m\u001b[1mrequirements:\u001b[0m protobuf<=3.20.1 not found and is required by YOLOv5, attempting auto-update...\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "google-api-core 2.11.0 requires protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.19.5, but you have protobuf 3.20.1 which is incompatible.\n",
            "google-cloud-bigquery 3.9.0 requires protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.19.5, but you have protobuf 3.20.1 which is incompatible.\n",
            "google-cloud-bigquery-storage 2.19.1 requires protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.19.5, but you have protobuf 3.20.1 which is incompatible.\n",
            "google-cloud-datastore 2.15.1 requires protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.19.5, but you have protobuf 3.20.1 which is incompatible.\n",
            "google-cloud-firestore 2.11.0 requires protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.19.5, but you have protobuf 3.20.1 which is incompatible.\n",
            "google-cloud-language 2.9.1 requires protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.19.5, but you have protobuf 3.20.1 which is incompatible.\n",
            "google-cloud-translate 3.11.1 requires protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.19.5, but you have protobuf 3.20.1 which is incompatible.\n",
            "googleapis-common-protos 1.59.0 requires protobuf!=3.20.0,!=3.20.1,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.19.5, but you have protobuf 3.20.1 which is incompatible.\n",
            "onnx 1.14.0 requires protobuf>=3.20.2, but you have protobuf 3.20.1 which is incompatible.\n",
            "paddlepaddle-gpu 2.4.1.post112 requires protobuf<=3.20.0,>=3.1.0, but you have protobuf 3.20.1 which is incompatible.\n",
            "tensorflow 2.12.0 requires protobuf!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.20.3, but you have protobuf 3.20.1 which is incompatible.\n",
            "tensorflow-metadata 1.13.1 requires protobuf<5,>=3.20.3, but you have protobuf 3.20.1 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0mLooking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting protobuf<=3.20.1\n",
            "  Using cached protobuf-3.20.1-cp310-cp310-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (1.1 MB)\n",
            "Installing collected packages: protobuf\n",
            "  Attempting uninstall: protobuf\n",
            "    Found existing installation: protobuf 3.20.3\n",
            "    Uninstalling protobuf-3.20.3:\n",
            "      Successfully uninstalled protobuf-3.20.3\n",
            "Successfully installed protobuf-3.20.1\n",
            "\n",
            "\u001b[31m\u001b[1mrequirements:\u001b[0m 1 package updated per /content/DT_SegNet/1_Detection_Model/requirements.txt\n",
            "\u001b[31m\u001b[1mrequirements:\u001b[0m ⚠️ \u001b[1mRestart runtime or rerun command for updates to take effect\u001b[0m\n",
            "\n",
            "YOLOv5 🚀 2023-5-19 Python-3.10.11 torch-2.0.1+cu118 CUDA:0 (NVIDIA A100-SXM4-40GB, 40514MiB)\n",
            "\n",
            "Fusing layers... \n",
            "Model summary: 476 layers, 76118664 parameters, 0 gradients, 109.9 GFLOPs\n",
            "Downloading https://ultralytics.com/assets/Arial.ttf to /root/.config/Ultralytics/Arial.ttf...\n",
            "100% 755k/755k [00:00<00:00, 16.3MB/s]\n",
            "\u001b[34m\u001b[1mtest: \u001b[0mScanning '/content/DT_SegNet/FineTuneDataset/labels' images and labels...3 found, 0 missing, 0 empty, 0 corrupt: 100% 3/3 [00:00<00:00, 281.93it/s]\n",
            "\u001b[34m\u001b[1mtest: \u001b[0mNew cache created: /content/DT_SegNet/FineTuneDataset/labels.cache\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:01<00:00,  1.75s/it]\n",
            "                 all          3        169      0.794      0.763      0.744       0.25\n",
            "Speed: 0.7ms pre-process, 62.8ms inference, 43.2ms NMS per image at shape (32, 3, 1280, 1280)\n",
            "Results saved to \u001b[1mruns/val/exp\u001b[0m\n",
            "3 labels saved to runs/val/exp/labels\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Finetune with all layers\n",
        "%cd /content/DT_SegNet/1_Detection_Model\n",
        "# Tune number of epochs if needed\n",
        "!python train.py --batch -1 --weights {detection_model_path} --data dtsegnet_finetune.yaml --epochs 50 --cache --img 1280"
      ],
      "metadata": {
        "id": "iHCUxVtqQ6a2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4a887efb-1b40-4efa-8a3d-3daafe58813e"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/DT_SegNet/1_Detection_Model\n",
            "2023-05-19 12:34:28.119924: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n",
            "2023-05-19 12:34:28.174373: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
            "To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
            "2023-05-19 12:34:29.266959: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n",
            "\u001b[34m\u001b[1mtrain: \u001b[0mweights=/content/DT_SegNet/Models/detection.pt, cfg=, data=dtsegnet_finetune.yaml, hyp=data/hyps/hyp.scratch-low.yaml, epochs=50, batch_size=-1, imgsz=1280, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, noplots=False, evolve=None, bucket=, cache=ram, image_weights=False, device=, multi_scale=False, single_cls=False, optimizer=SGD, sync_bn=False, workers=8, project=runs/train, name=exp, exist_ok=False, quad=False, cos_lr=False, label_smoothing=0.0, patience=100, freeze=[0], save_period=-1, seed=0, local_rank=-1, entity=None, upload_dataset=False, bbox_interval=-1, artifact_alias=latest\n",
            "\u001b[34m\u001b[1mgithub: \u001b[0mskipping check (not a git repository), for updates see https://github.com/ultralytics/yolov5\n",
            "YOLOv5 🚀 2023-5-19 Python-3.10.11 torch-2.0.1+cu118 CUDA:0 (NVIDIA A100-SXM4-40GB, 40514MiB)\n",
            "\n",
            "\u001b[34m\u001b[1mhyperparameters: \u001b[0mlr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=0.05, cls=0.5, cls_pw=1.0, obj=1.0, obj_pw=1.0, iou_t=0.2, anchor_t=4.0, fl_gamma=0.0, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0, copy_paste=0.0\n",
            "\u001b[34m\u001b[1mWeights & Biases: \u001b[0mrun 'pip install wandb' to automatically track and visualize YOLOv5 🚀 runs in Weights & Biases\n",
            "\u001b[34m\u001b[1mClearML: \u001b[0mrun 'pip install clearml' to automatically track, visualize and remotely train YOLOv5 🚀 in ClearML\n",
            "\u001b[34m\u001b[1mTensorBoard: \u001b[0mStart with 'tensorboard --logdir runs/train', view at http://localhost:6006/\n",
            "\n",
            "                 from  n    params  module                                  arguments                     \n",
            "  0                -1  1      7040  models.common.Conv                      [3, 64, 6, 2, 2]              \n",
            "  1                -1  1     73984  models.common.Conv                      [64, 128, 3, 2]               \n",
            "  2                -1  3    156928  models.common.C3                        [128, 128, 3]                 \n",
            "  3                -1  1    295424  models.common.Conv                      [128, 256, 3, 2]              \n",
            "  4                -1  6   1118208  models.common.C3                        [256, 256, 6]                 \n",
            "  5                -1  1   1180672  models.common.Conv                      [256, 512, 3, 2]              \n",
            "  6                -1  9   6433792  models.common.C3                        [512, 512, 9]                 \n",
            "  7                -1  1   3540480  models.common.Conv                      [512, 768, 3, 2]              \n",
            "  8                -1  3   5611008  models.common.C3                        [768, 768, 3]                 \n",
            "  9                -1  1   7079936  models.common.Conv                      [768, 1024, 3, 2]             \n",
            " 10                -1  3   9971712  models.common.C3                        [1024, 1024, 3]               \n",
            " 11                -1  1   2624512  models.common.SPPF                      [1024, 1024, 5]               \n",
            " 12                -1  1    787968  models.common.Conv                      [1024, 768, 1, 1]             \n",
            " 13                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
            " 14           [-1, 8]  1         0  models.common.Concat                    [1]                           \n",
            " 15                -1  3   6200832  models.common.C3                        [1536, 768, 3, False]         \n",
            " 16                -1  1    394240  models.common.Conv                      [768, 512, 1, 1]              \n",
            " 17                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
            " 18           [-1, 6]  1         0  models.common.Concat                    [1]                           \n",
            " 19                -1  3   2757632  models.common.C3                        [1024, 512, 3, False]         \n",
            " 20                -1  1    131584  models.common.Conv                      [512, 256, 1, 1]              \n",
            " 21                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
            " 22           [-1, 4]  1         0  models.common.Concat                    [1]                           \n",
            " 23                -1  3    690688  models.common.C3                        [512, 256, 3, False]          \n",
            " 24                -1  1    590336  models.common.Conv                      [256, 256, 3, 2]              \n",
            " 25          [-1, 20]  1         0  models.common.Concat                    [1]                           \n",
            " 26                -1  3   2495488  models.common.C3                        [512, 512, 3, False]          \n",
            " 27                -1  1   2360320  models.common.Conv                      [512, 512, 3, 2]              \n",
            " 28          [-1, 16]  1         0  models.common.Concat                    [1]                           \n",
            " 29                -1  3   5807616  models.common.C3                        [1024, 768, 3, False]         \n",
            " 30                -1  1   5309952  models.common.Conv                      [768, 768, 3, 2]              \n",
            " 31          [-1, 12]  1         0  models.common.Concat                    [1]                           \n",
            " 32                -1  3  10496000  models.common.C3                        [1536, 1024, 3, False]        \n",
            " 33  [23, 26, 29, 32]  1     46152  models.yolo.Detect                      [1, [[19, 27, 44, 40, 38, 94], [96, 68, 86, 152, 180, 137], [140, 301, 303, 264, 238, 542], [436, 615, 739, 380, 925, 792]], [256, 512, 768, 1024]]\n",
            "Model summary: 607 layers, 76162504 parameters, 76162504 gradients, 110.5 GFLOPs\n",
            "\n",
            "Transferred 795/795 items from /content/DT_SegNet/Models/detection.pt\n",
            "\u001b[34m\u001b[1mAMP: \u001b[0mchecks failed ❌, disabling Automatic Mixed Precision. See https://github.com/ultralytics/yolov5/issues/7908\n",
            "\u001b[34m\u001b[1mAutoBatch: \u001b[0mComputing optimal batch size for --imgsz 1280\n",
            "\u001b[34m\u001b[1mAutoBatch: \u001b[0mCUDA:0 (NVIDIA A100-SXM4-40GB) 39.56G total, 0.60G reserved, 0.58G allocated, 38.39G free\n",
            "      Params      GFLOPs  GPU_mem (GB)  forward (ms) backward (ms)                   input                  output\n",
            "    76162504       441.9         5.237         54.13         178.9      (1, 3, 1280, 1280)                    list\n",
            "    76162504       883.8        10.133         34.62         100.5      (2, 3, 1280, 1280)                    list\n",
            "    76162504        1768        19.422         59.37         137.4      (4, 3, 1280, 1280)                    list\n",
            "    76162504        3535        38.550         110.4         229.9      (8, 3, 1280, 1280)                    list\n",
            "CUDA out of memory. Tried to allocate 100.00 MiB (GPU 0; 39.56 GiB total capacity; 36.73 GiB already allocated; 12.56 MiB free; 37.65 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF\n",
            "\u001b[34m\u001b[1mAutoBatch: \u001b[0mUsing batch-size 7 for CUDA:0 33.77G/39.56G (85%) ✅\n",
            "\u001b[34m\u001b[1moptimizer:\u001b[0m SGD(lr=0.01) with parameter groups 131 weight(decay=0.0), 135 weight(decay=0.0004921875), 135 bias\n",
            "\u001b[34m\u001b[1malbumentations: \u001b[0mBlur(p=0.01, blur_limit=(3, 7)), MedianBlur(p=0.01, blur_limit=(3, 7)), ToGray(p=0.01), CLAHE(p=0.01, clip_limit=(1, 4.0), tile_grid_size=(8, 8))\n",
            "\u001b[34m\u001b[1mtrain: \u001b[0mScanning '/content/DT_SegNet/FineTuneDataset/labels.cache' images and labels... 3 found, 0 missing, 0 empty, 0 corrupt: 100% 3/3 [00:00<?, ?it/s]\n",
            "\u001b[34m\u001b[1mtrain: \u001b[0mCaching images (0.0GB ram): 100% 3/3 [00:00<00:00, 107.00it/s]\n",
            "\u001b[34m\u001b[1mval: \u001b[0mScanning '/content/DT_SegNet/FineTuneDataset/labels.cache' images and labels... 3 found, 0 missing, 0 empty, 0 corrupt: 100% 3/3 [00:00<?, ?it/s]\n",
            "\u001b[34m\u001b[1mval: \u001b[0mCaching images (0.0GB ram): 100% 3/3 [00:00<00:00, 104.78it/s]\n",
            "Plotting labels to runs/train/exp/labels.jpg... \n",
            "\n",
            "\u001b[34m\u001b[1mAutoAnchor: \u001b[0m5.24 anchors/target, 0.994 Best Possible Recall (BPR). Current anchors are a good fit to dataset ✅\n",
            "Image sizes 1280 train, 1280 val\n",
            "Using 3 dataloader workers\n",
            "Logging results to \u001b[1mruns/train/exp\u001b[0m\n",
            "Starting training for 50 epochs...\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "      0/49     15.3G   0.04331    0.2966         0       225      1280: 100% 1/1 [00:04<00:00,  4.68s/it]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:01<00:00,  1.42s/it]\n",
            "                 all          3        169      0.793      0.763      0.742      0.251\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "      1/49     15.3G   0.02848    0.2098         0       141      1280: 100% 1/1 [00:00<00:00,  4.94it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00, 14.86it/s]\n",
            "                 all          3        169      0.788      0.751      0.728      0.267\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "      2/49     15.3G   0.03879    0.4303         0       374      1280: 100% 1/1 [00:00<00:00,  4.94it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00, 13.89it/s]\n",
            "                 all          3        169      0.794      0.754      0.731      0.277\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "      3/49     15.3G   0.03977    0.3589         0       222      1280: 100% 1/1 [00:00<00:00,  4.95it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00, 14.64it/s]\n",
            "                 all          3        169      0.807      0.775      0.752       0.29\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "      4/49     15.3G   0.03039    0.3712         0       273      1280: 100% 1/1 [00:00<00:00,  4.92it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00, 14.90it/s]\n",
            "                 all          3        169       0.82      0.787      0.765      0.309\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "      5/49     15.3G   0.03056    0.2705         0       230      1280: 100% 1/1 [00:00<00:00,  4.96it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00, 14.14it/s]\n",
            "                 all          3        169       0.82      0.793      0.784      0.329\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "      6/49     15.3G   0.03071    0.3395         0       201      1280: 100% 1/1 [00:00<00:00,  4.92it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00, 14.99it/s]\n",
            "                 all          3        169      0.818      0.795      0.792       0.35\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "      7/49     15.3G   0.02055    0.2376         0       216      1280: 100% 1/1 [00:00<00:00,  6.07it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00, 14.51it/s]\n",
            "                 all          3        169      0.818      0.795      0.792       0.35\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "      8/49     15.3G   0.03096    0.3174         0       274      1280: 100% 1/1 [00:00<00:00,  4.90it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00, 13.64it/s]\n",
            "                 all          3        169      0.836      0.811      0.818      0.375\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "      9/49     15.3G   0.03183    0.2702         0       248      1280: 100% 1/1 [00:00<00:00,  5.86it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00, 14.39it/s]\n",
            "                 all          3        169      0.836      0.811      0.818      0.375\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     10/49     15.3G   0.03247    0.2872         0       243      1280: 100% 1/1 [00:00<00:00,  4.86it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00, 13.17it/s]\n",
            "                 all          3        169      0.864      0.826      0.834      0.394\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     11/49     15.3G   0.03318     0.336         0       304      1280: 100% 1/1 [00:00<00:00,  5.93it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00, 14.07it/s]\n",
            "                 all          3        169      0.864      0.826      0.834      0.394\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     12/49     15.3G    0.0326    0.4459         0       414      1280: 100% 1/1 [00:00<00:00,  4.88it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00, 14.49it/s]\n",
            "                 all          3        169      0.869       0.84      0.851      0.422\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     13/49     15.3G   0.02564    0.3795         0       453      1280: 100% 1/1 [00:00<00:00,  6.00it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00, 14.07it/s]\n",
            "                 all          3        169      0.869       0.84      0.851      0.422\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     14/49     15.3G   0.03331    0.3296         0       256      1280: 100% 1/1 [00:00<00:00,  4.78it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00, 15.10it/s]\n",
            "                 all          3        169       0.88      0.852      0.868      0.443\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     15/49     15.3G   0.02746    0.2745         0       245      1280: 100% 1/1 [00:00<00:00,  5.93it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00, 13.57it/s]\n",
            "                 all          3        169       0.88      0.852      0.868      0.443\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     16/49     15.3G   0.02705    0.3159         0       254      1280: 100% 1/1 [00:00<00:00,  4.89it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00, 14.82it/s]\n",
            "                 all          3        169      0.887      0.858      0.877      0.456\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     17/49     15.3G   0.03638    0.2118         0       195      1280: 100% 1/1 [00:00<00:00,  5.80it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00, 13.74it/s]\n",
            "                 all          3        169      0.887      0.858      0.877      0.456\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     18/49     15.3G   0.03532    0.2785         0       191      1280: 100% 1/1 [00:00<00:00,  4.96it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00, 14.95it/s]\n",
            "                 all          3        169      0.896       0.87      0.893      0.467\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     19/49     15.3G   0.02679     0.247         0       270      1280: 100% 1/1 [00:00<00:00,  6.08it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00, 14.81it/s]\n",
            "                 all          3        169      0.896       0.87      0.893      0.467\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     20/49     15.3G   0.02801     0.278         0       242      1280: 100% 1/1 [00:00<00:00,  6.01it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00, 15.07it/s]\n",
            "                 all          3        169      0.896       0.87      0.893      0.467\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     21/49     15.3G   0.04064     0.251         0       335      1280: 100% 1/1 [00:00<00:00,  4.89it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00, 14.59it/s]\n",
            "                 all          3        169      0.919       0.87      0.903      0.483\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     22/49     15.3G   0.02947    0.2677         0       247      1280: 100% 1/1 [00:00<00:00,  6.03it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00, 15.08it/s]\n",
            "                 all          3        169      0.919       0.87      0.903      0.483\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     23/49     15.3G   0.02354    0.2673         0       301      1280: 100% 1/1 [00:00<00:00,  5.90it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00, 13.90it/s]\n",
            "                 all          3        169      0.919       0.87      0.903      0.483\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     24/49     15.3G   0.02553    0.2843         0       303      1280: 100% 1/1 [00:00<00:00,  4.79it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00, 14.75it/s]\n",
            "                 all          3        169      0.931      0.888      0.918      0.487\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     25/49     15.3G   0.03433    0.2125         0       224      1280: 100% 1/1 [00:00<00:00,  5.80it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00, 14.63it/s]\n",
            "                 all          3        169      0.931      0.888      0.918      0.487\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     26/49     15.3G   0.03827    0.2212         0       126      1280: 100% 1/1 [00:00<00:00,  5.72it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00, 13.41it/s]\n",
            "                 all          3        169      0.931      0.888      0.918      0.487\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     27/49     15.3G   0.02489    0.3286         0       382      1280: 100% 1/1 [00:00<00:00,  4.80it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00, 14.69it/s]\n",
            "                 all          3        169      0.944      0.876      0.919      0.494\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     28/49     15.3G   0.02613    0.1829         0       166      1280: 100% 1/1 [00:00<00:00,  5.94it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00, 13.44it/s]\n",
            "                 all          3        169      0.944      0.876      0.919      0.494\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     29/49     15.3G   0.02383    0.3158         0       427      1280: 100% 1/1 [00:00<00:00,  6.01it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00, 14.80it/s]\n",
            "                 all          3        169      0.944      0.876      0.919      0.494\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     30/49     15.3G   0.02587    0.2415         0       264      1280: 100% 1/1 [00:00<00:00,  4.71it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00, 14.89it/s]\n",
            "                 all          3        169      0.956      0.893      0.927      0.491\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     31/49     15.3G   0.02398    0.2591         0       342      1280: 100% 1/1 [00:00<00:00,  5.93it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00, 13.96it/s]\n",
            "                 all          3        169      0.956      0.893      0.927      0.491\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     32/49     15.3G    0.0263    0.2088         0       164      1280: 100% 1/1 [00:00<00:00,  5.93it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00, 14.42it/s]\n",
            "                 all          3        169      0.956      0.893      0.927      0.491\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     33/49     15.3G   0.02981    0.2631         0       194      1280: 100% 1/1 [00:00<00:00,  6.62it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00, 14.90it/s]\n",
            "                 all          3        169      0.956      0.893      0.927      0.491\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     34/49     15.3G   0.02341    0.2246         0       249      1280: 100% 1/1 [00:00<00:00,  5.02it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00, 14.46it/s]\n",
            "                 all          3        169      0.954      0.899      0.932      0.501\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     35/49     15.3G   0.02707    0.2293         0       193      1280: 100% 1/1 [00:00<00:00,  6.06it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00, 14.88it/s]\n",
            "                 all          3        169      0.954      0.899      0.932      0.501\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     36/49     15.3G   0.04135    0.2442         0       112      1280: 100% 1/1 [00:00<00:00,  5.78it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00, 13.24it/s]\n",
            "                 all          3        169      0.954      0.899      0.932      0.501\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     37/49     15.3G   0.02607    0.1976         0       152      1280: 100% 1/1 [00:00<00:00,  5.91it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00, 13.94it/s]\n",
            "                 all          3        169      0.954      0.899      0.932      0.501\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     38/49     15.3G   0.03898    0.2064         0       244      1280: 100% 1/1 [00:00<00:00,  4.81it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00, 13.76it/s]\n",
            "                 all          3        169       0.96      0.899      0.932      0.505\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     39/49     15.3G   0.02409    0.2178         0       228      1280: 100% 1/1 [00:00<00:00,  5.88it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00, 14.25it/s]\n",
            "                 all          3        169       0.96      0.899      0.932      0.505\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     40/49     15.3G   0.02378    0.2761         0       348      1280: 100% 1/1 [00:00<00:00,  5.92it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00, 13.94it/s]\n",
            "                 all          3        169       0.96      0.899      0.932      0.505\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     41/49     15.3G   0.02224    0.2734         0       400      1280: 100% 1/1 [00:00<00:00,  5.92it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00, 14.08it/s]\n",
            "                 all          3        169       0.96      0.899      0.932      0.505\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     42/49     15.3G   0.02743    0.1899         0       199      1280: 100% 1/1 [00:00<00:00,  4.86it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00, 13.70it/s]\n",
            "                 all          3        169      0.959      0.899      0.929      0.502\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     43/49     15.3G   0.02589    0.2221         0       173      1280: 100% 1/1 [00:00<00:00,  6.61it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00, 14.45it/s]\n",
            "                 all          3        169      0.959      0.899      0.929      0.502\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     44/49     15.3G   0.03515    0.1649         0       148      1280: 100% 1/1 [00:00<00:00,  5.80it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00, 13.28it/s]\n",
            "                 all          3        169      0.959      0.899      0.929      0.502\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     45/49     15.3G   0.02337    0.2833         0       330      1280: 100% 1/1 [00:00<00:00,  5.90it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00, 13.14it/s]\n",
            "                 all          3        169      0.959      0.899      0.929      0.502\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     46/49     15.3G   0.03688    0.2131         0       189      1280: 100% 1/1 [00:00<00:00,  6.46it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00, 14.76it/s]\n",
            "                 all          3        169      0.959      0.899      0.929      0.502\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     47/49     15.3G   0.02561    0.2444         0       205      1280: 100% 1/1 [00:00<00:00,  4.95it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00, 14.65it/s]\n",
            "                 all          3        169      0.958      0.899      0.928      0.502\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     48/49     15.3G   0.02453    0.2086         0       178      1280: 100% 1/1 [00:00<00:00,  5.94it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00, 13.94it/s]\n",
            "                 all          3        169      0.958      0.899      0.928      0.502\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     49/49     15.3G   0.02369    0.2114         0       216      1280: 100% 1/1 [00:00<00:00,  6.60it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00, 14.03it/s]\n",
            "                 all          3        169      0.958      0.899      0.928      0.502\n",
            "\n",
            "50 epochs completed in 0.069 hours.\n",
            "Optimizer stripped from runs/train/exp/weights/last.pt, 153.7MB\n",
            "Optimizer stripped from runs/train/exp/weights/best.pt, 153.7MB\n",
            "\n",
            "Validating runs/train/exp/weights/best.pt...\n",
            "Fusing layers... \n",
            "Model summary: 476 layers, 76118664 parameters, 0 gradients, 109.9 GFLOPs\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00,  2.75it/s]\n",
            "                 all          3        169      0.963      0.893      0.928      0.502\n",
            "Results saved to \u001b[1mruns/train/exp\u001b[0m\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Finetune with existing feature extracting layers (recommended if distribution varies a lot)\n",
        "%cd /content/DT_SegNet/1_Detection_Model\n",
        "# Tune number of epochs if needed\n",
        "!python train.py --batch -1 --weights {detection_model_path} --data dtsegnet_finetune.yaml --epochs 50 --cache --img 1280 --freeze 10"
      ],
      "metadata": {
        "id": "T8UAPXjjJUgq",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6efe3746-6b08-491c-a31c-898727d77666"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/DT_SegNet/1_Detection_Model\n",
            "2023-05-19 12:39:59.605223: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n",
            "2023-05-19 12:39:59.659222: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
            "To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
            "2023-05-19 12:40:00.727365: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n",
            "\u001b[34m\u001b[1mtrain: \u001b[0mweights=/content/DT_SegNet/Models/detection.pt, cfg=, data=dtsegnet_finetune.yaml, hyp=data/hyps/hyp.scratch-low.yaml, epochs=50, batch_size=-1, imgsz=1280, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, noplots=False, evolve=None, bucket=, cache=ram, image_weights=False, device=, multi_scale=False, single_cls=False, optimizer=SGD, sync_bn=False, workers=8, project=runs/train, name=exp, exist_ok=False, quad=False, cos_lr=False, label_smoothing=0.0, patience=100, freeze=[10], save_period=-1, seed=0, local_rank=-1, entity=None, upload_dataset=False, bbox_interval=-1, artifact_alias=latest\n",
            "\u001b[34m\u001b[1mgithub: \u001b[0mskipping check (not a git repository), for updates see https://github.com/ultralytics/yolov5\n",
            "YOLOv5 🚀 2023-5-19 Python-3.10.11 torch-2.0.1+cu118 CUDA:0 (NVIDIA A100-SXM4-40GB, 40514MiB)\n",
            "\n",
            "\u001b[34m\u001b[1mhyperparameters: \u001b[0mlr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=0.05, cls=0.5, cls_pw=1.0, obj=1.0, obj_pw=1.0, iou_t=0.2, anchor_t=4.0, fl_gamma=0.0, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0, copy_paste=0.0\n",
            "\u001b[34m\u001b[1mWeights & Biases: \u001b[0mrun 'pip install wandb' to automatically track and visualize YOLOv5 🚀 runs in Weights & Biases\n",
            "\u001b[34m\u001b[1mClearML: \u001b[0mrun 'pip install clearml' to automatically track, visualize and remotely train YOLOv5 🚀 in ClearML\n",
            "\u001b[34m\u001b[1mTensorBoard: \u001b[0mStart with 'tensorboard --logdir runs/train', view at http://localhost:6006/\n",
            "\n",
            "                 from  n    params  module                                  arguments                     \n",
            "  0                -1  1      7040  models.common.Conv                      [3, 64, 6, 2, 2]              \n",
            "  1                -1  1     73984  models.common.Conv                      [64, 128, 3, 2]               \n",
            "  2                -1  3    156928  models.common.C3                        [128, 128, 3]                 \n",
            "  3                -1  1    295424  models.common.Conv                      [128, 256, 3, 2]              \n",
            "  4                -1  6   1118208  models.common.C3                        [256, 256, 6]                 \n",
            "  5                -1  1   1180672  models.common.Conv                      [256, 512, 3, 2]              \n",
            "  6                -1  9   6433792  models.common.C3                        [512, 512, 9]                 \n",
            "  7                -1  1   3540480  models.common.Conv                      [512, 768, 3, 2]              \n",
            "  8                -1  3   5611008  models.common.C3                        [768, 768, 3]                 \n",
            "  9                -1  1   7079936  models.common.Conv                      [768, 1024, 3, 2]             \n",
            " 10                -1  3   9971712  models.common.C3                        [1024, 1024, 3]               \n",
            " 11                -1  1   2624512  models.common.SPPF                      [1024, 1024, 5]               \n",
            " 12                -1  1    787968  models.common.Conv                      [1024, 768, 1, 1]             \n",
            " 13                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
            " 14           [-1, 8]  1         0  models.common.Concat                    [1]                           \n",
            " 15                -1  3   6200832  models.common.C3                        [1536, 768, 3, False]         \n",
            " 16                -1  1    394240  models.common.Conv                      [768, 512, 1, 1]              \n",
            " 17                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
            " 18           [-1, 6]  1         0  models.common.Concat                    [1]                           \n",
            " 19                -1  3   2757632  models.common.C3                        [1024, 512, 3, False]         \n",
            " 20                -1  1    131584  models.common.Conv                      [512, 256, 1, 1]              \n",
            " 21                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
            " 22           [-1, 4]  1         0  models.common.Concat                    [1]                           \n",
            " 23                -1  3    690688  models.common.C3                        [512, 256, 3, False]          \n",
            " 24                -1  1    590336  models.common.Conv                      [256, 256, 3, 2]              \n",
            " 25          [-1, 20]  1         0  models.common.Concat                    [1]                           \n",
            " 26                -1  3   2495488  models.common.C3                        [512, 512, 3, False]          \n",
            " 27                -1  1   2360320  models.common.Conv                      [512, 512, 3, 2]              \n",
            " 28          [-1, 16]  1         0  models.common.Concat                    [1]                           \n",
            " 29                -1  3   5807616  models.common.C3                        [1024, 768, 3, False]         \n",
            " 30                -1  1   5309952  models.common.Conv                      [768, 768, 3, 2]              \n",
            " 31          [-1, 12]  1         0  models.common.Concat                    [1]                           \n",
            " 32                -1  3  10496000  models.common.C3                        [1536, 1024, 3, False]        \n",
            " 33  [23, 26, 29, 32]  1     46152  models.yolo.Detect                      [1, [[19, 27, 44, 40, 38, 94], [96, 68, 86, 152, 180, 137], [140, 301, 303, 264, 238, 542], [436, 615, 739, 380, 925, 792]], [256, 512, 768, 1024]]\n",
            "Model summary: 607 layers, 76162504 parameters, 76162504 gradients, 110.5 GFLOPs\n",
            "\n",
            "Transferred 795/795 items from /content/DT_SegNet/Models/detection.pt\n",
            "\u001b[34m\u001b[1mAMP: \u001b[0mchecks failed ❌, disabling Automatic Mixed Precision. See https://github.com/ultralytics/yolov5/issues/7908\n",
            "freezing model.0.conv.weight\n",
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            "freezing model.6.m.7.cv2.conv.weight\n",
            "freezing model.6.m.7.cv2.bn.weight\n",
            "freezing model.6.m.7.cv2.bn.bias\n",
            "freezing model.6.m.8.cv1.conv.weight\n",
            "freezing model.6.m.8.cv1.bn.weight\n",
            "freezing model.6.m.8.cv1.bn.bias\n",
            "freezing model.6.m.8.cv2.conv.weight\n",
            "freezing model.6.m.8.cv2.bn.weight\n",
            "freezing model.6.m.8.cv2.bn.bias\n",
            "freezing model.7.conv.weight\n",
            "freezing model.7.bn.weight\n",
            "freezing model.7.bn.bias\n",
            "freezing model.8.cv1.conv.weight\n",
            "freezing model.8.cv1.bn.weight\n",
            "freezing model.8.cv1.bn.bias\n",
            "freezing model.8.cv2.conv.weight\n",
            "freezing model.8.cv2.bn.weight\n",
            "freezing model.8.cv2.bn.bias\n",
            "freezing model.8.cv3.conv.weight\n",
            "freezing model.8.cv3.bn.weight\n",
            "freezing model.8.cv3.bn.bias\n",
            "freezing model.8.m.0.cv1.conv.weight\n",
            "freezing model.8.m.0.cv1.bn.weight\n",
            "freezing model.8.m.0.cv1.bn.bias\n",
            "freezing model.8.m.0.cv2.conv.weight\n",
            "freezing model.8.m.0.cv2.bn.weight\n",
            "freezing model.8.m.0.cv2.bn.bias\n",
            "freezing model.8.m.1.cv1.conv.weight\n",
            "freezing model.8.m.1.cv1.bn.weight\n",
            "freezing model.8.m.1.cv1.bn.bias\n",
            "freezing model.8.m.1.cv2.conv.weight\n",
            "freezing model.8.m.1.cv2.bn.weight\n",
            "freezing model.8.m.1.cv2.bn.bias\n",
            "freezing model.8.m.2.cv1.conv.weight\n",
            "freezing model.8.m.2.cv1.bn.weight\n",
            "freezing model.8.m.2.cv1.bn.bias\n",
            "freezing model.8.m.2.cv2.conv.weight\n",
            "freezing model.8.m.2.cv2.bn.weight\n",
            "freezing model.8.m.2.cv2.bn.bias\n",
            "freezing model.9.conv.weight\n",
            "freezing model.9.bn.weight\n",
            "freezing model.9.bn.bias\n",
            "\u001b[34m\u001b[1mAutoBatch: \u001b[0mComputing optimal batch size for --imgsz 1280\n",
            "\u001b[34m\u001b[1mAutoBatch: \u001b[0mCUDA:0 (NVIDIA A100-SXM4-40GB) 39.56G total, 0.60G reserved, 0.58G allocated, 38.39G free\n",
            "      Params      GFLOPs  GPU_mem (GB)  forward (ms) backward (ms)                   input                  output\n",
            "    76162504       441.9         5.060         37.72         86.89      (1, 3, 1280, 1280)                    list\n",
            "    76162504       883.8         9.295         35.57         77.19      (2, 3, 1280, 1280)                    list\n",
            "    76162504        1768        17.958          60.3         109.9      (4, 3, 1280, 1280)                    list\n",
            "    76162504        3535        35.727         110.2         184.3      (8, 3, 1280, 1280)                    list\n",
            "CUDA out of memory. Tried to allocate 100.00 MiB (GPU 0; 39.56 GiB total capacity; 36.73 GiB already allocated; 52.56 MiB free; 37.61 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF\n",
            "\u001b[34m\u001b[1mAutoBatch: \u001b[0mUsing batch-size 7 for CUDA:0 31.27G/39.56G (79%) ✅\n",
            "\u001b[34m\u001b[1moptimizer:\u001b[0m SGD(lr=0.01) with parameter groups 131 weight(decay=0.0), 135 weight(decay=0.0004921875), 135 bias\n",
            "\u001b[34m\u001b[1malbumentations: \u001b[0mBlur(p=0.01, blur_limit=(3, 7)), MedianBlur(p=0.01, blur_limit=(3, 7)), ToGray(p=0.01), CLAHE(p=0.01, clip_limit=(1, 4.0), tile_grid_size=(8, 8))\n",
            "\u001b[34m\u001b[1mtrain: \u001b[0mScanning '/content/DT_SegNet/FineTuneDataset/labels.cache' images and labels... 3 found, 0 missing, 0 empty, 0 corrupt: 100% 3/3 [00:00<?, ?it/s]\n",
            "\u001b[34m\u001b[1mtrain: \u001b[0mCaching images (0.0GB ram): 100% 3/3 [00:00<00:00, 105.38it/s]\n",
            "\u001b[34m\u001b[1mval: \u001b[0mScanning '/content/DT_SegNet/FineTuneDataset/labels.cache' images and labels... 3 found, 0 missing, 0 empty, 0 corrupt: 100% 3/3 [00:00<?, ?it/s]\n",
            "\u001b[34m\u001b[1mval: \u001b[0mCaching images (0.0GB ram): 100% 3/3 [00:00<00:00, 103.82it/s]\n",
            "Plotting labels to runs/train/exp2/labels.jpg... \n",
            "\n",
            "\u001b[34m\u001b[1mAutoAnchor: \u001b[0m5.24 anchors/target, 0.994 Best Possible Recall (BPR). Current anchors are a good fit to dataset ✅\n",
            "Image sizes 1280 train, 1280 val\n",
            "Using 3 dataloader workers\n",
            "Logging results to \u001b[1mruns/train/exp2\u001b[0m\n",
            "Starting training for 50 epochs...\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "      0/49     5.69G   0.04331    0.2966         0       225      1280: 100% 1/1 [00:04<00:00,  4.62s/it]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:01<00:00,  1.33s/it]\n",
            "                 all          3        169      0.793      0.763      0.742      0.251\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "      1/49     6.35G   0.02849    0.2098         0       141      1280: 100% 1/1 [00:00<00:00,  7.50it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00, 13.86it/s]\n",
            "                 all          3        169      0.788      0.751      0.728      0.266\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "      2/49     6.35G   0.03879    0.4305         0       374      1280: 100% 1/1 [00:00<00:00,  7.43it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00, 14.41it/s]\n",
            "                 all          3        169      0.795      0.756      0.731      0.274\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "      3/49     6.35G   0.03979    0.3593         0       222      1280: 100% 1/1 [00:00<00:00,  7.14it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00, 15.05it/s]\n",
            "                 all          3        169      0.807      0.767       0.75      0.288\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "      4/49     6.35G   0.03046    0.3729         0       273      1280: 100% 1/1 [00:00<00:00,  7.53it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00, 12.99it/s]\n",
            "                 all          3        169      0.826      0.787      0.764      0.303\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "      5/49     6.35G   0.03072    0.2731         0       230      1280: 100% 1/1 [00:00<00:00,  7.48it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00, 13.99it/s]\n",
            "                 all          3        169      0.827      0.787      0.774      0.322\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "      6/49     6.35G   0.03092    0.3445         0       201      1280: 100% 1/1 [00:00<00:00,  7.21it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00, 14.00it/s]\n",
            "                 all          3        169      0.821      0.788      0.782      0.337\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "      7/49     6.35G   0.02102    0.2459         0       216      1280: 100% 1/1 [00:00<00:00,  9.93it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00, 13.94it/s]\n",
            "                 all          3        169      0.821      0.788      0.782      0.337\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "      8/49     6.35G   0.03129    0.3235         0       274      1280: 100% 1/1 [00:00<00:00,  7.51it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00, 12.18it/s]\n",
            "                 all          3        169       0.83      0.793      0.792      0.356\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "      9/49     6.35G   0.03242    0.2777         0       248      1280: 100% 1/1 [00:00<00:00,  9.90it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00, 13.96it/s]\n",
            "                 all          3        169       0.83      0.793      0.792      0.356\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     10/49     6.35G    0.0329    0.2933         0       243      1280: 100% 1/1 [00:00<00:00,  7.50it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00, 11.75it/s]\n",
            "                 all          3        169      0.841      0.813      0.811      0.372\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     11/49     6.35G   0.03389    0.3461         0       304      1280: 100% 1/1 [00:00<00:00,  9.86it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00, 14.20it/s]\n",
            "                 all          3        169      0.841      0.813      0.811      0.372\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     12/49     6.35G   0.03321    0.4604         0       414      1280: 100% 1/1 [00:00<00:00,  7.54it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00, 14.50it/s]\n",
            "                 all          3        169      0.842      0.821      0.816       0.39\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     13/49     6.35G   0.02648    0.3977         0       453      1280: 100% 1/1 [00:00<00:00,  9.80it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00, 13.93it/s]\n",
            "                 all          3        169      0.842      0.821      0.816       0.39\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     14/49     6.35G   0.03407    0.3439         0       256      1280: 100% 1/1 [00:00<00:00,  7.50it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00, 13.75it/s]\n",
            "                 all          3        169      0.866       0.84      0.844      0.399\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     15/49     6.35G   0.02848    0.2883         0       245      1280: 100% 1/1 [00:00<00:00,  9.84it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00, 13.73it/s]\n",
            "                 all          3        169      0.866       0.84      0.844      0.399\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     16/49     6.35G   0.02814    0.3335         0       254      1280: 100% 1/1 [00:00<00:00,  7.32it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00, 14.44it/s]\n",
            "                 all          3        169      0.872      0.846      0.853      0.414\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     17/49     6.35G   0.03698     0.226         0       195      1280: 100% 1/1 [00:00<00:00,  9.47it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00, 13.74it/s]\n",
            "                 all          3        169      0.872      0.846      0.853      0.414\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     18/49     6.35G    0.0365    0.2864         0       191      1280: 100% 1/1 [00:00<00:00,  7.38it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00, 14.01it/s]\n",
            "                 all          3        169      0.878      0.858      0.857      0.423\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     19/49     6.35G    0.0279    0.2646         0       270      1280: 100% 1/1 [00:00<00:00,  9.89it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00, 13.91it/s]\n",
            "                 all          3        169      0.878      0.858      0.857      0.423\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     20/49     6.35G   0.02926    0.2997         0       242      1280: 100% 1/1 [00:00<00:00,  9.83it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00, 13.79it/s]\n",
            "                 all          3        169      0.878      0.858      0.857      0.423\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     21/49     6.35G   0.04286    0.2683         0       335      1280: 100% 1/1 [00:00<00:00,  7.25it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00, 13.91it/s]\n",
            "                 all          3        169      0.877      0.858      0.857      0.427\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     22/49     6.35G   0.03116    0.2899         0       247      1280: 100% 1/1 [00:00<00:00,  9.90it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00, 14.47it/s]\n",
            "                 all          3        169      0.877      0.858      0.857      0.427\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     23/49     6.35G   0.02422    0.2911         0       301      1280: 100% 1/1 [00:00<00:00,  9.89it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00, 13.79it/s]\n",
            "                 all          3        169      0.877      0.858      0.857      0.427\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     24/49     6.35G   0.02706    0.3065         0       303      1280: 100% 1/1 [00:00<00:00,  7.55it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00, 13.77it/s]\n",
            "                 all          3        169      0.881      0.864      0.864      0.433\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     25/49     6.35G    0.0367    0.2281         0       224      1280: 100% 1/1 [00:00<00:00,  9.50it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00, 13.88it/s]\n",
            "                 all          3        169      0.881      0.864      0.864      0.433\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     26/49     6.35G    0.0396    0.2348         0       126      1280: 100% 1/1 [00:00<00:00,  9.50it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00, 13.54it/s]\n",
            "                 all          3        169      0.881      0.864      0.864      0.433\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     27/49     6.35G   0.02679    0.3624         0       382      1280: 100% 1/1 [00:00<00:00,  7.23it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00, 14.53it/s]\n",
            "                 all          3        169      0.889       0.87       0.87      0.441\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     28/49     6.35G    0.0271    0.1917         0       166      1280: 100% 1/1 [00:00<00:00,  9.92it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00, 14.16it/s]\n",
            "                 all          3        169      0.889       0.87       0.87      0.441\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     29/49     6.35G   0.02465    0.3437         0       427      1280: 100% 1/1 [00:00<00:00,  9.91it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00, 14.15it/s]\n",
            "                 all          3        169      0.889       0.87       0.87      0.441\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     30/49     6.35G   0.02732    0.2594         0       264      1280: 100% 1/1 [00:00<00:00,  6.37it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00, 13.97it/s]\n",
            "                 all          3        169      0.895      0.876      0.878      0.448\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     31/49     6.35G   0.02424    0.2781         0       342      1280: 100% 1/1 [00:00<00:00,  9.58it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00, 14.38it/s]\n",
            "                 all          3        169      0.895      0.876      0.878      0.448\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     32/49     6.35G    0.0284    0.2245         0       164      1280: 100% 1/1 [00:00<00:00,  9.84it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00, 14.04it/s]\n",
            "                 all          3        169      0.895      0.876      0.878      0.448\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     33/49     6.35G   0.03248    0.2821         0       194      1280: 100% 1/1 [00:00<00:00,  9.47it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00, 13.81it/s]\n",
            "                 all          3        169      0.895      0.876      0.878      0.448\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     34/49     6.35G   0.02394    0.2436         0       249      1280: 100% 1/1 [00:00<00:00,  7.58it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00, 14.28it/s]\n",
            "                 all          3        169      0.901      0.882      0.886      0.457\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     35/49     6.35G   0.02889    0.2476         0       193      1280: 100% 1/1 [00:00<00:00,  9.91it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00, 13.97it/s]\n",
            "                 all          3        169      0.901      0.882      0.886      0.457\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     36/49     6.35G   0.04249    0.2575         0       112      1280: 100% 1/1 [00:00<00:00,  9.38it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00, 14.16it/s]\n",
            "                 all          3        169      0.901      0.882      0.886      0.457\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     37/49     6.35G   0.02707    0.2098         0       152      1280: 100% 1/1 [00:00<00:00,  9.85it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00, 12.78it/s]\n",
            "                 all          3        169      0.901      0.882      0.886      0.457\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     38/49     6.35G   0.04089    0.2161         0       244      1280: 100% 1/1 [00:00<00:00,  7.25it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00, 13.57it/s]\n",
            "                 all          3        169      0.923      0.888      0.909      0.462\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     39/49     6.35G    0.0243    0.2375         0       228      1280: 100% 1/1 [00:00<00:00, 10.00it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00, 13.81it/s]\n",
            "                 all          3        169      0.923      0.888      0.909      0.462\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     40/49     6.35G   0.02562    0.3013         0       348      1280: 100% 1/1 [00:00<00:00,  9.16it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00, 13.86it/s]\n",
            "                 all          3        169      0.923      0.888      0.909      0.462\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     41/49     6.35G   0.02333    0.2977         0       400      1280: 100% 1/1 [00:00<00:00,  9.82it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00, 13.61it/s]\n",
            "                 all          3        169      0.923      0.888      0.909      0.462\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     42/49     6.35G   0.02742     0.208         0       199      1280: 100% 1/1 [00:00<00:00,  7.48it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00, 12.99it/s]\n",
            "                 all          3        169      0.926      0.894      0.909      0.462\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     43/49     6.35G   0.02779    0.2402         0       173      1280: 100% 1/1 [00:00<00:00,  9.22it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00, 13.06it/s]\n",
            "                 all          3        169      0.926      0.894      0.909      0.462\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     44/49     6.35G   0.03729    0.1717         0       148      1280: 100% 1/1 [00:00<00:00,  9.55it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00, 13.26it/s]\n",
            "                 all          3        169      0.926      0.894      0.909      0.462\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     45/49     6.35G   0.02408    0.3071         0       330      1280: 100% 1/1 [00:00<00:00,  9.36it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00, 13.85it/s]\n",
            "                 all          3        169      0.926      0.894      0.909      0.462\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     46/49     6.35G   0.04027    0.2284         0       189      1280: 100% 1/1 [00:00<00:00,  9.53it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00, 13.83it/s]\n",
            "                 all          3        169      0.926      0.894      0.909      0.462\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     47/49     6.35G   0.02757    0.2675         0       205      1280: 100% 1/1 [00:00<00:00,  7.38it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00, 13.80it/s]\n",
            "                 all          3        169      0.926      0.895      0.909      0.463\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     48/49     6.35G   0.02496    0.2272         0       178      1280: 100% 1/1 [00:00<00:00,  9.45it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00, 13.73it/s]\n",
            "                 all          3        169      0.926      0.895      0.909      0.463\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     49/49     6.35G   0.02586     0.235         0       216      1280: 100% 1/1 [00:00<00:00,  9.59it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00, 13.99it/s]\n",
            "                 all          3        169      0.926      0.895      0.909      0.463\n",
            "\n",
            "50 epochs completed in 0.068 hours.\n",
            "Optimizer stripped from runs/train/exp2/weights/last.pt, 153.7MB\n",
            "Optimizer stripped from runs/train/exp2/weights/best.pt, 153.7MB\n",
            "\n",
            "Validating runs/train/exp2/weights/best.pt...\n",
            "Fusing layers... \n",
            "Model summary: 476 layers, 76118664 parameters, 0 gradients, 109.9 GFLOPs\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00,  3.05it/s]\n",
            "                 all          3        169       0.92      0.889      0.907      0.463\n",
            "Results saved to \u001b[1mruns/train/exp2\u001b[0m\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Finetune freezing all layers expect header (recommended)\n",
        "%cd /content/DT_SegNet/1_Detection_Model\n",
        "# Tune number of epochs if needed\n",
        "!python train.py --batch -1 --weights {detection_model_path} --data dtsegnet_finetune.yaml --epochs 50 --cache --img 1280 --freeze 33"
      ],
      "metadata": {
        "id": "z-noxyeZJUs9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0e00b7cf-ea57-48d9-e9c1-f0f952d19b08"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/DT_SegNet/1_Detection_Model\n",
            "2023-05-19 12:44:38.141830: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n",
            "2023-05-19 12:44:38.196368: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
            "To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
            "2023-05-19 12:44:39.259613: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n",
            "\u001b[34m\u001b[1mtrain: \u001b[0mweights=/content/DT_SegNet/Models/detection.pt, cfg=, data=dtsegnet_finetune.yaml, hyp=data/hyps/hyp.scratch-low.yaml, epochs=50, batch_size=-1, imgsz=1280, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, noplots=False, evolve=None, bucket=, cache=ram, image_weights=False, device=, multi_scale=False, single_cls=False, optimizer=SGD, sync_bn=False, workers=8, project=runs/train, name=exp, exist_ok=False, quad=False, cos_lr=False, label_smoothing=0.0, patience=100, freeze=[33], save_period=-1, seed=0, local_rank=-1, entity=None, upload_dataset=False, bbox_interval=-1, artifact_alias=latest\n",
            "\u001b[34m\u001b[1mgithub: \u001b[0mskipping check (not a git repository), for updates see https://github.com/ultralytics/yolov5\n",
            "YOLOv5 🚀 2023-5-19 Python-3.10.11 torch-2.0.1+cu118 CUDA:0 (NVIDIA A100-SXM4-40GB, 40514MiB)\n",
            "\n",
            "\u001b[34m\u001b[1mhyperparameters: \u001b[0mlr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=0.05, cls=0.5, cls_pw=1.0, obj=1.0, obj_pw=1.0, iou_t=0.2, anchor_t=4.0, fl_gamma=0.0, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0, copy_paste=0.0\n",
            "\u001b[34m\u001b[1mWeights & Biases: \u001b[0mrun 'pip install wandb' to automatically track and visualize YOLOv5 🚀 runs in Weights & Biases\n",
            "\u001b[34m\u001b[1mClearML: \u001b[0mrun 'pip install clearml' to automatically track, visualize and remotely train YOLOv5 🚀 in ClearML\n",
            "\u001b[34m\u001b[1mTensorBoard: \u001b[0mStart with 'tensorboard --logdir runs/train', view at http://localhost:6006/\n",
            "\n",
            "                 from  n    params  module                                  arguments                     \n",
            "  0                -1  1      7040  models.common.Conv                      [3, 64, 6, 2, 2]              \n",
            "  1                -1  1     73984  models.common.Conv                      [64, 128, 3, 2]               \n",
            "  2                -1  3    156928  models.common.C3                        [128, 128, 3]                 \n",
            "  3                -1  1    295424  models.common.Conv                      [128, 256, 3, 2]              \n",
            "  4                -1  6   1118208  models.common.C3                        [256, 256, 6]                 \n",
            "  5                -1  1   1180672  models.common.Conv                      [256, 512, 3, 2]              \n",
            "  6                -1  9   6433792  models.common.C3                        [512, 512, 9]                 \n",
            "  7                -1  1   3540480  models.common.Conv                      [512, 768, 3, 2]              \n",
            "  8                -1  3   5611008  models.common.C3                        [768, 768, 3]                 \n",
            "  9                -1  1   7079936  models.common.Conv                      [768, 1024, 3, 2]             \n",
            " 10                -1  3   9971712  models.common.C3                        [1024, 1024, 3]               \n",
            " 11                -1  1   2624512  models.common.SPPF                      [1024, 1024, 5]               \n",
            " 12                -1  1    787968  models.common.Conv                      [1024, 768, 1, 1]             \n",
            " 13                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
            " 14           [-1, 8]  1         0  models.common.Concat                    [1]                           \n",
            " 15                -1  3   6200832  models.common.C3                        [1536, 768, 3, False]         \n",
            " 16                -1  1    394240  models.common.Conv                      [768, 512, 1, 1]              \n",
            " 17                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
            " 18           [-1, 6]  1         0  models.common.Concat                    [1]                           \n",
            " 19                -1  3   2757632  models.common.C3                        [1024, 512, 3, False]         \n",
            " 20                -1  1    131584  models.common.Conv                      [512, 256, 1, 1]              \n",
            " 21                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
            " 22           [-1, 4]  1         0  models.common.Concat                    [1]                           \n",
            " 23                -1  3    690688  models.common.C3                        [512, 256, 3, False]          \n",
            " 24                -1  1    590336  models.common.Conv                      [256, 256, 3, 2]              \n",
            " 25          [-1, 20]  1         0  models.common.Concat                    [1]                           \n",
            " 26                -1  3   2495488  models.common.C3                        [512, 512, 3, False]          \n",
            " 27                -1  1   2360320  models.common.Conv                      [512, 512, 3, 2]              \n",
            " 28          [-1, 16]  1         0  models.common.Concat                    [1]                           \n",
            " 29                -1  3   5807616  models.common.C3                        [1024, 768, 3, False]         \n",
            " 30                -1  1   5309952  models.common.Conv                      [768, 768, 3, 2]              \n",
            " 31          [-1, 12]  1         0  models.common.Concat                    [1]                           \n",
            " 32                -1  3  10496000  models.common.C3                        [1536, 1024, 3, False]        \n",
            " 33  [23, 26, 29, 32]  1     46152  models.yolo.Detect                      [1, [[19, 27, 44, 40, 38, 94], [96, 68, 86, 152, 180, 137], [140, 301, 303, 264, 238, 542], [436, 615, 739, 380, 925, 792]], [256, 512, 768, 1024]]\n",
            "Model summary: 607 layers, 76162504 parameters, 76162504 gradients, 110.5 GFLOPs\n",
            "\n",
            "Transferred 795/795 items from /content/DT_SegNet/Models/detection.pt\n",
            "\u001b[34m\u001b[1mAMP: \u001b[0mchecks failed ❌, disabling Automatic Mixed Precision. See https://github.com/ultralytics/yolov5/issues/7908\n",
            "freezing model.0.conv.weight\n",
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            "freezing model.32.m.2.cv2.bn.bias\n",
            "\u001b[34m\u001b[1mAutoBatch: \u001b[0mComputing optimal batch size for --imgsz 1280\n",
            "\u001b[34m\u001b[1mAutoBatch: \u001b[0mCUDA:0 (NVIDIA A100-SXM4-40GB) 39.56G total, 0.60G reserved, 0.58G allocated, 38.39G free\n",
            "      Params      GFLOPs  GPU_mem (GB)  forward (ms) backward (ms)                   input                  output\n",
            "    76162504       441.9         4.899         38.35         69.56      (1, 3, 1280, 1280)                    list\n",
            "    76162504       883.8         8.936         37.41         59.86      (2, 3, 1280, 1280)                    list\n",
            "    76162504        1768        17.566          61.2         89.37      (4, 3, 1280, 1280)                    list\n",
            "    76162504        3535        34.374         110.3         151.8      (8, 3, 1280, 1280)                    list\n",
            "CUDA out of memory. Tried to allocate 200.00 MiB (GPU 0; 39.56 GiB total capacity; 37.12 GiB already allocated; 118.56 MiB free; 37.54 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF\n",
            "\u001b[34m\u001b[1mAutoBatch: \u001b[0mUsing batch-size 8 for CUDA:0 34.39G/39.56G (87%) ✅\n",
            "\u001b[34m\u001b[1moptimizer:\u001b[0m SGD(lr=0.01) with parameter groups 131 weight(decay=0.0), 135 weight(decay=0.0005), 135 bias\n",
            "\u001b[34m\u001b[1malbumentations: \u001b[0mBlur(p=0.01, blur_limit=(3, 7)), MedianBlur(p=0.01, blur_limit=(3, 7)), ToGray(p=0.01), CLAHE(p=0.01, clip_limit=(1, 4.0), tile_grid_size=(8, 8))\n",
            "\u001b[34m\u001b[1mtrain: \u001b[0mScanning '/content/DT_SegNet/FineTuneDataset/labels.cache' images and labels... 3 found, 0 missing, 0 empty, 0 corrupt: 100% 3/3 [00:00<?, ?it/s]\n",
            "\u001b[34m\u001b[1mtrain: \u001b[0mCaching images (0.0GB ram): 100% 3/3 [00:00<00:00, 107.16it/s]\n",
            "\u001b[34m\u001b[1mval: \u001b[0mScanning '/content/DT_SegNet/FineTuneDataset/labels.cache' images and labels... 3 found, 0 missing, 0 empty, 0 corrupt: 100% 3/3 [00:00<?, ?it/s]\n",
            "\u001b[34m\u001b[1mval: \u001b[0mCaching images (0.0GB ram): 100% 3/3 [00:00<00:00, 103.91it/s]\n",
            "Plotting labels to runs/train/exp3/labels.jpg... \n",
            "\n",
            "\u001b[34m\u001b[1mAutoAnchor: \u001b[0m5.24 anchors/target, 0.994 Best Possible Recall (BPR). Current anchors are a good fit to dataset ✅\n",
            "Image sizes 1280 train, 1280 val\n",
            "Using 3 dataloader workers\n",
            "Logging results to \u001b[1mruns/train/exp3\u001b[0m\n",
            "Starting training for 50 epochs...\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "      0/49     2.14G   0.04331    0.2966         0       225      1280: 100% 1/1 [00:04<00:00,  4.53s/it]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:01<00:00,  1.37s/it]\n",
            "                 all          3        169      0.793      0.763      0.742      0.251\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "      1/49     2.82G   0.02849      0.21         0       141      1280: 100% 1/1 [00:00<00:00, 10.19it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00,  4.25it/s]\n",
            "                 all          3        169      0.789      0.751      0.728      0.266\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "      2/49     2.82G   0.03881    0.4314         0       374      1280: 100% 1/1 [00:00<00:00, 10.38it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00, 14.00it/s]\n",
            "                 all          3        169      0.795      0.755      0.731      0.274\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "      3/49     2.82G   0.03985    0.3613         0       222      1280: 100% 1/1 [00:00<00:00, 10.35it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00, 14.15it/s]\n",
            "                 all          3        169      0.803      0.763       0.75      0.285\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "      4/49     2.82G   0.03066    0.3778         0       273      1280: 100% 1/1 [00:00<00:00, 10.11it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00, 13.68it/s]\n",
            "                 all          3        169      0.821      0.781      0.764      0.294\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "      5/49     2.82G   0.03102    0.2789         0       230      1280: 100% 1/1 [00:00<00:00,  9.85it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00, 13.66it/s]\n",
            "                 all          3        169      0.821      0.781      0.765      0.312\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "      6/49     2.82G   0.03143    0.3567         0       201      1280: 100% 1/1 [00:00<00:00, 10.07it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00, 13.68it/s]\n",
            "                 all          3        169      0.818      0.781      0.776      0.323\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "      7/49     2.82G   0.02178    0.2619         0       216      1280: 100% 1/1 [00:00<00:00, 10.07it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00, 13.58it/s]\n",
            "                 all          3        169      0.805      0.805      0.787      0.331\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "      8/49     2.82G    0.0315    0.3372         0       274      1280: 100% 1/1 [00:00<00:00, 15.35it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00, 14.00it/s]\n",
            "                 all          3        169      0.805      0.805      0.787      0.331\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "      9/49     2.82G   0.03334    0.2956         0       248      1280: 100% 1/1 [00:00<00:00, 10.29it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00, 14.47it/s]\n",
            "                 all          3        169      0.839      0.803      0.797      0.344\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     10/49     2.82G    0.0331    0.3069         0       243      1280: 100% 1/1 [00:00<00:00, 15.57it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00, 14.02it/s]\n",
            "                 all          3        169      0.839      0.803      0.797      0.344\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     11/49     2.82G   0.03503    0.3718         0       304      1280: 100% 1/1 [00:00<00:00, 10.10it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00, 13.99it/s]\n",
            "                 all          3        169       0.84      0.805      0.808      0.354\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     12/49     2.82G   0.03375    0.4937         0       414      1280: 100% 1/1 [00:00<00:00, 15.36it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00, 13.79it/s]\n",
            "                 all          3        169       0.84      0.805      0.808      0.354\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     13/49     2.82G   0.02808    0.4375         0       453      1280: 100% 1/1 [00:00<00:00, 10.41it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00, 13.98it/s]\n",
            "                 all          3        169      0.846      0.814      0.809      0.363\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     14/49     2.82G    0.0351    0.3764         0       256      1280: 100% 1/1 [00:00<00:00, 15.29it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00, 13.18it/s]\n",
            "                 all          3        169      0.846      0.814      0.809      0.363\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     15/49     2.82G   0.03006     0.326         0       245      1280: 100% 1/1 [00:00<00:00, 10.33it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00, 13.17it/s]\n",
            "                 all          3        169      0.834      0.822      0.807       0.37\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     16/49     2.82G   0.02989    0.3766         0       254      1280: 100% 1/1 [00:00<00:00, 14.69it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00, 13.95it/s]\n",
            "                 all          3        169      0.834      0.822      0.807       0.37\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     17/49     2.82G   0.03834    0.2608         0       195      1280: 100% 1/1 [00:00<00:00,  9.96it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00, 13.77it/s]\n",
            "                 all          3        169      0.841      0.828      0.815      0.375\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     18/49     2.82G    0.0382     0.318         0       191      1280: 100% 1/1 [00:00<00:00, 15.48it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00, 13.86it/s]\n",
            "                 all          3        169      0.841      0.828      0.815      0.375\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     19/49     2.82G   0.02939    0.3113         0       270      1280: 100% 1/1 [00:00<00:00, 10.45it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00, 13.92it/s]\n",
            "                 all          3        169      0.862      0.846      0.833      0.382\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     20/49     2.82G   0.03068    0.3527         0       242      1280: 100% 1/1 [00:00<00:00, 15.22it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00, 13.79it/s]\n",
            "                 all          3        169      0.862      0.846      0.833      0.382\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     21/49     2.82G    0.0463    0.3071         0       335      1280: 100% 1/1 [00:00<00:00,  9.82it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00, 13.86it/s]\n",
            "                 all          3        169      0.864      0.846      0.832      0.382\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     22/49     2.82G   0.03465    0.3489         0       247      1280: 100% 1/1 [00:00<00:00, 15.36it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00, 13.49it/s]\n",
            "                 all          3        169      0.864      0.846      0.832      0.382\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     23/49     2.82G   0.02535    0.3515         0       301      1280: 100% 1/1 [00:00<00:00, 15.00it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00, 13.54it/s]\n",
            "                 all          3        169      0.864      0.846      0.832      0.382\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     24/49     2.82G   0.03003    0.3612         0       303      1280: 100% 1/1 [00:00<00:00, 10.56it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00, 14.27it/s]\n",
            "                 all          3        169      0.867      0.846      0.831      0.382\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     25/49     2.82G   0.04008    0.2711         0       224      1280: 100% 1/1 [00:00<00:00, 14.37it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00, 13.87it/s]\n",
            "                 all          3        169      0.867      0.846      0.831      0.382\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     26/49     2.82G   0.04162    0.2804         0       126      1280: 100% 1/1 [00:00<00:00, 14.48it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00, 13.72it/s]\n",
            "                 all          3        169      0.867      0.846      0.831      0.382\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     27/49     2.82G   0.03054    0.4452         0       382      1280: 100% 1/1 [00:00<00:00, 10.97it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00, 14.87it/s]\n",
            "                 all          3        169      0.877      0.852      0.838      0.385\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     28/49     2.82G   0.02822    0.2328         0       166      1280: 100% 1/1 [00:00<00:00, 15.44it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00, 13.90it/s]\n",
            "                 all          3        169      0.877      0.852      0.838      0.385\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     29/49     2.82G   0.02606    0.4155         0       427      1280: 100% 1/1 [00:00<00:00, 15.47it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00, 13.73it/s]\n",
            "                 all          3        169      0.877      0.852      0.838      0.385\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     30/49     2.82G   0.02946    0.3131         0       264      1280: 100% 1/1 [00:00<00:00, 10.38it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00, 13.04it/s]\n",
            "                 all          3        169      0.877      0.842      0.832      0.387\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     31/49     2.82G   0.02459    0.3458         0       342      1280: 100% 1/1 [00:00<00:00, 16.09it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00, 13.90it/s]\n",
            "                 all          3        169      0.877      0.842      0.832      0.387\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     32/49     2.82G   0.03118     0.273         0       164      1280: 100% 1/1 [00:00<00:00, 15.41it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00, 13.98it/s]\n",
            "                 all          3        169      0.877      0.842      0.832      0.387\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     33/49     2.82G   0.03623    0.3475         0       194      1280: 100% 1/1 [00:00<00:00, 10.24it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00, 13.81it/s]\n",
            "                 all          3        169      0.882      0.852      0.841      0.388\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     34/49     2.82G   0.02443    0.3079         0       249      1280: 100% 1/1 [00:00<00:00, 15.21it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00, 14.15it/s]\n",
            "                 all          3        169      0.882      0.852      0.841      0.388\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     35/49     2.82G   0.03154    0.3068         0       193      1280: 100% 1/1 [00:00<00:00, 15.43it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00, 13.92it/s]\n",
            "                 all          3        169      0.882      0.852      0.841      0.388\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     36/49     2.82G   0.04563    0.3234         0       112      1280: 100% 1/1 [00:00<00:00, 15.20it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00, 14.46it/s]\n",
            "                 all          3        169      0.882      0.852      0.841      0.388\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     37/49     2.82G   0.02897    0.2641         0       152      1280: 100% 1/1 [00:00<00:00, 10.72it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00, 14.98it/s]\n",
            "                 all          3        169      0.875      0.852      0.836      0.389\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     38/49     2.82G   0.04426    0.2509         0       244      1280: 100% 1/1 [00:00<00:00, 14.75it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00, 15.00it/s]\n",
            "                 all          3        169      0.875      0.852      0.836      0.389\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     39/49     2.82G   0.02519    0.3099         0       228      1280: 100% 1/1 [00:00<00:00, 15.46it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00, 13.80it/s]\n",
            "                 all          3        169      0.875      0.852      0.836      0.389\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     40/49     2.82G   0.02844    0.3764         0       348      1280: 100% 1/1 [00:00<00:00, 15.36it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00, 13.82it/s]\n",
            "                 all          3        169      0.875      0.852      0.836      0.389\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     41/49     2.82G   0.02444    0.3741         0       400      1280: 100% 1/1 [00:00<00:00, 10.79it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00, 14.81it/s]\n",
            "                 all          3        169      0.875      0.852      0.842      0.389\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     42/49     2.82G   0.02712    0.2772         0       199      1280: 100% 1/1 [00:00<00:00, 15.43it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00, 13.85it/s]\n",
            "                 all          3        169      0.875      0.852      0.842      0.389\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     43/49     2.82G   0.03176    0.3125         0       173      1280: 100% 1/1 [00:00<00:00, 14.89it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00, 13.74it/s]\n",
            "                 all          3        169      0.875      0.852      0.842      0.389\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     44/49     2.82G   0.04145    0.2097         0       148      1280: 100% 1/1 [00:00<00:00, 14.28it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00, 13.87it/s]\n",
            "                 all          3        169      0.875      0.852      0.842      0.389\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     45/49     2.82G   0.02609     0.398         0       330      1280: 100% 1/1 [00:00<00:00,  9.70it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00, 13.95it/s]\n",
            "                 all          3        169      0.882      0.858       0.85      0.392\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     46/49     2.82G   0.04386    0.2811         0       189      1280: 100% 1/1 [00:00<00:00, 14.28it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00, 13.64it/s]\n",
            "                 all          3        169      0.882      0.858       0.85      0.392\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     47/49     2.82G   0.03158    0.3457         0       205      1280: 100% 1/1 [00:00<00:00, 16.46it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00, 14.58it/s]\n",
            "                 all          3        169      0.882      0.858       0.85      0.392\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     48/49     2.82G     0.027    0.3058         0       178      1280: 100% 1/1 [00:00<00:00, 15.44it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00, 13.99it/s]\n",
            "                 all          3        169      0.882      0.858       0.85      0.392\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     49/49     2.82G   0.02934    0.3032         0       216      1280: 100% 1/1 [00:00<00:00, 10.90it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00, 13.97it/s]\n",
            "                 all          3        169      0.868      0.846      0.828      0.389\n",
            "\n",
            "50 epochs completed in 0.041 hours.\n",
            "Optimizer stripped from runs/train/exp3/weights/last.pt, 153.7MB\n",
            "Optimizer stripped from runs/train/exp3/weights/best.pt, 153.7MB\n",
            "\n",
            "Validating runs/train/exp3/weights/best.pt...\n",
            "Fusing layers... \n",
            "Model summary: 476 layers, 76118664 parameters, 0 gradients, 109.9 GFLOPs\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00,  3.03it/s]\n",
            "                 all          3        169      0.869      0.846      0.829      0.395\n",
            "Results saved to \u001b[1mruns/train/exp3\u001b[0m\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Finetune segmentation model"
      ],
      "metadata": {
        "id": "r6OVvoSdzKwA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Evaluation before finetune\n",
        "%cd /content/DT_SegNet/3_Segmentation_Model\n",
        "!python val.py --config configs/dtsegnet/finetune.yml --model_path {segmentation_model_path}"
      ],
      "metadata": {
        "id": "Q-5nCIxEQ6Yf",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4d264a3a-97aa-4212-8631-fcec4f3027b8"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/DT_SegNet/3_Segmentation_Model\n",
            "/usr/local/lib/python3.10/dist-packages/pkg_resources/__init__.py:121: DeprecationWarning: pkg_resources is deprecated as an API\n",
            "  warnings.warn(\"pkg_resources is deprecated as an API\", DeprecationWarning)\n",
            "/usr/local/lib/python3.10/dist-packages/pkg_resources/__init__.py:2870: DeprecationWarning: Deprecated call to `pkg_resources.declare_namespace('google')`.\n",
            "Implementing implicit namespace packages (as specified in PEP 420) is preferred to `pkg_resources.declare_namespace`. See https://setuptools.pypa.io/en/latest/references/keywords.html#keyword-namespace-packages\n",
            "  declare_namespace(pkg)\n",
            "/usr/local/lib/python3.10/dist-packages/pkg_resources/__init__.py:2870: DeprecationWarning: Deprecated call to `pkg_resources.declare_namespace('google.cloud')`.\n",
            "Implementing implicit namespace packages (as specified in PEP 420) is preferred to `pkg_resources.declare_namespace`. See https://setuptools.pypa.io/en/latest/references/keywords.html#keyword-namespace-packages\n",
            "  declare_namespace(pkg)\n",
            "/usr/local/lib/python3.10/dist-packages/pkg_resources/__init__.py:2349: DeprecationWarning: Deprecated call to `pkg_resources.declare_namespace('google')`.\n",
            "Implementing implicit namespace packages (as specified in PEP 420) is preferred to `pkg_resources.declare_namespace`. See https://setuptools.pypa.io/en/latest/references/keywords.html#keyword-namespace-packages\n",
            "  declare_namespace(parent)\n",
            "/usr/local/lib/python3.10/dist-packages/pkg_resources/__init__.py:2870: DeprecationWarning: Deprecated call to `pkg_resources.declare_namespace('google.logging')`.\n",
            "Implementing implicit namespace packages (as specified in PEP 420) is preferred to `pkg_resources.declare_namespace`. See https://setuptools.pypa.io/en/latest/references/keywords.html#keyword-namespace-packages\n",
            "  declare_namespace(pkg)\n",
            "/usr/local/lib/python3.10/dist-packages/pkg_resources/__init__.py:2870: DeprecationWarning: Deprecated call to `pkg_resources.declare_namespace('mpl_toolkits')`.\n",
            "Implementing implicit namespace packages (as specified in PEP 420) is preferred to `pkg_resources.declare_namespace`. See https://setuptools.pypa.io/en/latest/references/keywords.html#keyword-namespace-packages\n",
            "  declare_namespace(pkg)\n",
            "/usr/local/lib/python3.10/dist-packages/pkg_resources/__init__.py:2870: DeprecationWarning: Deprecated call to `pkg_resources.declare_namespace('sphinxcontrib')`.\n",
            "Implementing implicit namespace packages (as specified in PEP 420) is preferred to `pkg_resources.declare_namespace`. See https://setuptools.pypa.io/en/latest/references/keywords.html#keyword-namespace-packages\n",
            "  declare_namespace(pkg)\n",
            "/usr/local/lib/python3.10/dist-packages/pkg_resources/__init__.py:2870: DeprecationWarning: Deprecated call to `pkg_resources.declare_namespace('zope')`.\n",
            "Implementing implicit namespace packages (as specified in PEP 420) is preferred to `pkg_resources.declare_namespace`. See https://setuptools.pypa.io/en/latest/references/keywords.html#keyword-namespace-packages\n",
            "  declare_namespace(pkg)\n",
            "WARNING: OMP_NUM_THREADS set to 8, not 1. The computation speed will not be optimized if you use data parallel. It will fail if this PaddlePaddle binary is compiled with OpenBlas since OpenBlas does not support multi-threads.\n",
            "PLEASE USE OMP_NUM_THREADS WISELY.\n",
            "/content/DT_SegNet/3_Segmentation_Model/paddleseg/models/losses/rmi_loss.py:73: DeprecationWarning: invalid escape sequence '\\i'\n",
            "  \"\"\"\n",
            "2023-05-19 12:47:38 [INFO]\t\n",
            "---------------Config Information---------------\n",
            "batch_size: 1\n",
            "iters: 2000\n",
            "loss:\n",
            "  coef:\n",
            "  - 1\n",
            "  types:\n",
            "  - type: CrossEntropyLoss\n",
            "lr_scheduler:\n",
            "  end_lr: 0\n",
            "  learning_rate: 6.0e-05\n",
            "  power: 1\n",
            "  type: PolynomialDecay\n",
            "model:\n",
            "  backbone:\n",
            "    in_channels: 3\n",
            "    type: MixVisionTransformer_B1\n",
            "  embedding_dim: 256\n",
            "  num_classes: 2\n",
            "  pretrained: /content/DT_SegNet/Models/segmentation.pdparams\n",
            "  type: SegFormer\n",
            "optimizer:\n",
            "  beta1: 0.9\n",
            "  beta2: 0.999\n",
            "  type: AdamW\n",
            "  weight_decay: 0.01\n",
            "test_config:\n",
            "  crop_size:\n",
            "  - 1024\n",
            "  - 1024\n",
            "  is_slide: true\n",
            "  stride:\n",
            "  - 768\n",
            "  - 768\n",
            "test_dataset:\n",
            "  dataset_root: /content/DT_SegNet/FineTuneDataset/Segmentation_Dataset\n",
            "  mode: val\n",
            "  num_classes: 2\n",
            "  transforms:\n",
            "  - target_size:\n",
            "    - 512\n",
            "    - 512\n",
            "    type: Resize\n",
            "  - type: Normalize\n",
            "  type: Dataset\n",
            "  val_path: /content/DT_SegNet/FineTuneDataset/Segmentation_Dataset/train.txt\n",
            "train_dataset:\n",
            "  dataset_root: /content/DT_SegNet/FineTuneDataset/Segmentation_Dataset\n",
            "  img_channels: 3\n",
            "  mode: train\n",
            "  num_classes: 2\n",
            "  train_path: /content/DT_SegNet/FineTuneDataset/Segmentation_Dataset/train.txt\n",
            "  transforms:\n",
            "  - target_size:\n",
            "    - 512\n",
            "    - 512\n",
            "    type: Resize\n",
            "  - type: RandomHorizontalFlip\n",
            "  - type: RandomVerticalFlip\n",
            "  - type: Normalize\n",
            "  type: Dataset\n",
            "val_dataset:\n",
            "  dataset_root: /content/DT_SegNet/FineTuneDataset/Segmentation_Dataset\n",
            "  img_channels: 3\n",
            "  mode: val\n",
            "  num_classes: 2\n",
            "  transforms:\n",
            "  - target_size:\n",
            "    - 512\n",
            "    - 512\n",
            "    type: Resize\n",
            "  - type: Normalize\n",
            "  type: Dataset\n",
            "  val_path: /content/DT_SegNet/FineTuneDataset/Segmentation_Dataset/train.txt\n",
            "------------------------------------------------\n",
            "W0519 12:47:38.763284  7766 gpu_resources.cc:61] Please NOTE: device: 0, GPU Compute Capability: 8.0, Driver API Version: 12.0, Runtime API Version: 11.8\n",
            "W0519 12:47:38.763319  7766 gpu_resources.cc:91] device: 0, cuDNN Version: 8.7.\n",
            "2023-05-19 12:47:38 [INFO]\tLoading pretrained model from /content/DT_SegNet/Models/segmentation.pdparams\n",
            "2023-05-19 12:47:38 [INFO]\tThere are 191/191 variables loaded into SegFormer.\n",
            "2023-05-19 12:47:38 [INFO]\tLoading pretrained model from /content/DT_SegNet/Models/segmentation.pdparams\n",
            "2023-05-19 12:47:39 [INFO]\tThere are 191/191 variables loaded into SegFormer.\n",
            "2023-05-19 12:47:39 [INFO]\tLoaded trained params of model successfully\n",
            "2023-05-19 12:47:39 [INFO]\tStart evaluating (total_samples: 169, total_iters: 169)...\n",
            "/content/DT_SegNet/3_Segmentation_Model/paddleseg/core/infer.py:98: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
            "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
            "  rows = np.int(np.ceil(1.0 * (h_im - h_crop) / h_stride)) + 1\n",
            "/content/DT_SegNet/3_Segmentation_Model/paddleseg/core/infer.py:99: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
            "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
            "  cols = np.int(np.ceil(1.0 * (w_im - w_crop) / w_stride)) + 1\n",
            "169/169 [==============================] - 5s 28ms/step - batch_cost: 0.0278 - reader cost: 7.6605e-04\n",
            "2023-05-19 12:47:43 [INFO]\t[EVAL] #Images: 169 mIoU: 0.8705 Acc: 0.9383 Kappa: 0.8605 Dice: 0.9303\n",
            "2023-05-19 12:47:43 [INFO]\t[EVAL] Class IoU: \n",
            "[0.9119 0.8291]\n",
            "2023-05-19 12:47:43 [INFO]\t[EVAL] Class Precision: \n",
            "[0.9477 0.9187]\n",
            "2023-05-19 12:47:43 [INFO]\t[EVAL] Class Recall: \n",
            "[0.9602 0.8948]\n",
            "2023-05-19 12:47:43 [WARNING]\tThis `val.py`  will be removed in version 2.8, please use `tools/val.py`.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Finetune freezing all layers expect header (recommended)\n",
        "%cd /content/DT_SegNet/3_Segmentation_Model\n",
        "Path('/content/DT_SegNet/Segmentation_Model_Output/Finetune').mkdir(parents=True, exist_ok=True)\n",
        "# Tune iters in /content/DT_SegNet/3_Segmentation_Model/configs/dtsegnet/finetune.yml if needed\n",
        "!python train.py --config configs/dtsegnet/finetune.yml --do_eval --use_vdl --save_interval 200 --keep_checkpoint_max 1 --save_dir \"/content/DT_SegNet/Segmentation_Model_Output/Finetune\""
      ],
      "metadata": {
        "id": "H0sgE6aJQ6WF",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "08679cb8-3a45-4409-9a63-17a59a5b4fd0"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/DT_SegNet/3_Segmentation_Model\n",
            "/usr/local/lib/python3.10/dist-packages/pkg_resources/__init__.py:121: DeprecationWarning: pkg_resources is deprecated as an API\n",
            "  warnings.warn(\"pkg_resources is deprecated as an API\", DeprecationWarning)\n",
            "/usr/local/lib/python3.10/dist-packages/pkg_resources/__init__.py:2870: DeprecationWarning: Deprecated call to `pkg_resources.declare_namespace('google')`.\n",
            "Implementing implicit namespace packages (as specified in PEP 420) is preferred to `pkg_resources.declare_namespace`. See https://setuptools.pypa.io/en/latest/references/keywords.html#keyword-namespace-packages\n",
            "  declare_namespace(pkg)\n",
            "/usr/local/lib/python3.10/dist-packages/pkg_resources/__init__.py:2870: DeprecationWarning: Deprecated call to `pkg_resources.declare_namespace('google.cloud')`.\n",
            "Implementing implicit namespace packages (as specified in PEP 420) is preferred to `pkg_resources.declare_namespace`. See https://setuptools.pypa.io/en/latest/references/keywords.html#keyword-namespace-packages\n",
            "  declare_namespace(pkg)\n",
            "/usr/local/lib/python3.10/dist-packages/pkg_resources/__init__.py:2349: DeprecationWarning: Deprecated call to `pkg_resources.declare_namespace('google')`.\n",
            "Implementing implicit namespace packages (as specified in PEP 420) is preferred to `pkg_resources.declare_namespace`. See https://setuptools.pypa.io/en/latest/references/keywords.html#keyword-namespace-packages\n",
            "  declare_namespace(parent)\n",
            "/usr/local/lib/python3.10/dist-packages/pkg_resources/__init__.py:2870: DeprecationWarning: Deprecated call to `pkg_resources.declare_namespace('google.logging')`.\n",
            "Implementing implicit namespace packages (as specified in PEP 420) is preferred to `pkg_resources.declare_namespace`. See https://setuptools.pypa.io/en/latest/references/keywords.html#keyword-namespace-packages\n",
            "  declare_namespace(pkg)\n",
            "/usr/local/lib/python3.10/dist-packages/pkg_resources/__init__.py:2870: DeprecationWarning: Deprecated call to `pkg_resources.declare_namespace('mpl_toolkits')`.\n",
            "Implementing implicit namespace packages (as specified in PEP 420) is preferred to `pkg_resources.declare_namespace`. See https://setuptools.pypa.io/en/latest/references/keywords.html#keyword-namespace-packages\n",
            "  declare_namespace(pkg)\n",
            "/usr/local/lib/python3.10/dist-packages/pkg_resources/__init__.py:2870: DeprecationWarning: Deprecated call to `pkg_resources.declare_namespace('sphinxcontrib')`.\n",
            "Implementing implicit namespace packages (as specified in PEP 420) is preferred to `pkg_resources.declare_namespace`. See https://setuptools.pypa.io/en/latest/references/keywords.html#keyword-namespace-packages\n",
            "  declare_namespace(pkg)\n",
            "/usr/local/lib/python3.10/dist-packages/pkg_resources/__init__.py:2870: DeprecationWarning: Deprecated call to `pkg_resources.declare_namespace('zope')`.\n",
            "Implementing implicit namespace packages (as specified in PEP 420) is preferred to `pkg_resources.declare_namespace`. See https://setuptools.pypa.io/en/latest/references/keywords.html#keyword-namespace-packages\n",
            "  declare_namespace(pkg)\n",
            "WARNING: OMP_NUM_THREADS set to 8, not 1. The computation speed will not be optimized if you use data parallel. It will fail if this PaddlePaddle binary is compiled with OpenBlas since OpenBlas does not support multi-threads.\n",
            "PLEASE USE OMP_NUM_THREADS WISELY.\n",
            "2023-05-19 12:47:49 [INFO]\t\n",
            "------------Environment Information-------------\n",
            "platform: Linux-5.15.107+-x86_64-with-glibc2.31\n",
            "Python: 3.10.11 (main, Apr  5 2023, 14:15:10) [GCC 9.4.0]\n",
            "Paddle compiled with cuda: True\n",
            "NVCC: Build cuda_11.8.r11.8/compiler.31833905_0\n",
            "cudnn: 8.7\n",
            "GPUs used: 1\n",
            "CUDA_VISIBLE_DEVICES: None\n",
            "GPU: ['GPU 0: NVIDIA A100-SXM4-40GB']\n",
            "GCC: gcc (Ubuntu 9.4.0-1ubuntu1~20.04.1) 9.4.0\n",
            "PaddleSeg: develop\n",
            "PaddlePaddle: 2.4.1\n",
            "OpenCV: 4.7.0\n",
            "------------------------------------------------\n",
            "2023-05-19 12:47:49 [INFO]\t\n",
            "---------------Config Information---------------\n",
            "batch_size: 1\n",
            "iters: 2000\n",
            "loss:\n",
            "  coef:\n",
            "  - 1\n",
            "  types:\n",
            "  - ignore_index: 255\n",
            "    type: CrossEntropyLoss\n",
            "lr_scheduler:\n",
            "  end_lr: 0\n",
            "  learning_rate: 6.0e-05\n",
            "  power: 1\n",
            "  type: PolynomialDecay\n",
            "model:\n",
            "  backbone:\n",
            "    in_channels: 3\n",
            "    type: MixVisionTransformer_B1\n",
            "  embedding_dim: 256\n",
            "  num_classes: 2\n",
            "  pretrained: /content/DT_SegNet/Models/segmentation.pdparams\n",
            "  type: SegFormer\n",
            "optimizer:\n",
            "  beta1: 0.9\n",
            "  beta2: 0.999\n",
            "  type: AdamW\n",
            "  weight_decay: 0.01\n",
            "test_config:\n",
            "  crop_size:\n",
            "  - 1024\n",
            "  - 1024\n",
            "  is_slide: true\n",
            "  stride:\n",
            "  - 768\n",
            "  - 768\n",
            "test_dataset:\n",
            "  dataset_root: /content/DT_SegNet/FineTuneDataset/Segmentation_Dataset\n",
            "  mode: val\n",
            "  num_classes: 2\n",
            "  transforms:\n",
            "  - target_size:\n",
            "    - 512\n",
            "    - 512\n",
            "    type: Resize\n",
            "  - type: Normalize\n",
            "  type: Dataset\n",
            "  val_path: /content/DT_SegNet/FineTuneDataset/Segmentation_Dataset/train.txt\n",
            "train_dataset:\n",
            "  dataset_root: /content/DT_SegNet/FineTuneDataset/Segmentation_Dataset\n",
            "  img_channels: 3\n",
            "  mode: train\n",
            "  num_classes: 2\n",
            "  train_path: /content/DT_SegNet/FineTuneDataset/Segmentation_Dataset/train.txt\n",
            "  transforms:\n",
            "  - target_size:\n",
            "    - 512\n",
            "    - 512\n",
            "    type: Resize\n",
            "  - type: RandomHorizontalFlip\n",
            "  - type: RandomVerticalFlip\n",
            "  - type: Normalize\n",
            "  type: Dataset\n",
            "val_dataset:\n",
            "  dataset_root: /content/DT_SegNet/FineTuneDataset/Segmentation_Dataset\n",
            "  img_channels: 3\n",
            "  mode: val\n",
            "  num_classes: 2\n",
            "  transforms:\n",
            "  - target_size:\n",
            "    - 512\n",
            "    - 512\n",
            "    type: Resize\n",
            "  - type: Normalize\n",
            "  type: Dataset\n",
            "  val_path: /content/DT_SegNet/FineTuneDataset/Segmentation_Dataset/train.txt\n",
            "------------------------------------------------\n",
            "W0519 12:47:49.886979  7872 gpu_resources.cc:61] Please NOTE: device: 0, GPU Compute Capability: 8.0, Driver API Version: 12.0, Runtime API Version: 11.8\n",
            "W0519 12:47:49.887009  7872 gpu_resources.cc:91] device: 0, cuDNN Version: 8.7.\n",
            "2023-05-19 12:47:50 [INFO]\tLoading pretrained model from /content/DT_SegNet/Models/segmentation.pdparams\n",
            "2023-05-19 12:47:50 [INFO]\tThere are 191/191 variables loaded into SegFormer.\n",
            "/usr/local/lib/python3.10/dist-packages/paddle/nn/layer/norm.py:711: UserWarning: When training, we now always track global mean and variance.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/paddle/fluid/dygraph/math_op_patch.py:275: UserWarning: The dtype of left and right variables are not the same, left dtype is paddle.float32, but right dtype is paddle.int64, the right dtype will convert to paddle.float32\n",
            "  warnings.warn(\n",
            "2023-05-19 12:47:51 [INFO]\t[TRAIN] epoch: 1, iter: 10/2000, loss: 0.1649, lr: 0.000060, batch_cost: 0.1265, reader_cost: 0.00923, ips: 7.9072 samples/sec | ETA 00:04:11\n",
            "2023-05-19 12:47:51 [INFO]\t[TRAIN] epoch: 1, iter: 20/2000, loss: 0.1323, lr: 0.000059, batch_cost: 0.0442, reader_cost: 0.00013, ips: 22.6493 samples/sec | ETA 00:01:27\n",
            "2023-05-19 12:47:52 [INFO]\t[TRAIN] epoch: 1, iter: 30/2000, loss: 0.1908, lr: 0.000059, batch_cost: 0.0436, reader_cost: 0.00013, ips: 22.9540 samples/sec | ETA 00:01:25\n",
            "2023-05-19 12:47:52 [INFO]\t[TRAIN] epoch: 1, iter: 40/2000, loss: 0.1440, lr: 0.000059, batch_cost: 0.0442, reader_cost: 0.00014, ips: 22.6032 samples/sec | ETA 00:01:26\n",
            "2023-05-19 12:47:53 [INFO]\t[TRAIN] epoch: 1, iter: 50/2000, loss: 0.1429, lr: 0.000059, batch_cost: 0.0434, reader_cost: 0.00013, ips: 23.0564 samples/sec | ETA 00:01:24\n",
            "2023-05-19 12:47:53 [INFO]\t[TRAIN] epoch: 1, iter: 60/2000, loss: 0.1729, lr: 0.000058, batch_cost: 0.0448, reader_cost: 0.00014, ips: 22.2986 samples/sec | ETA 00:01:27\n",
            "2023-05-19 12:47:54 [INFO]\t[TRAIN] epoch: 1, iter: 70/2000, loss: 0.1212, lr: 0.000058, batch_cost: 0.0452, reader_cost: 0.00015, ips: 22.1053 samples/sec | ETA 00:01:27\n",
            "2023-05-19 12:47:54 [INFO]\t[TRAIN] epoch: 1, iter: 80/2000, loss: 0.1400, lr: 0.000058, batch_cost: 0.0444, reader_cost: 0.00014, ips: 22.5314 samples/sec | ETA 00:01:25\n",
            "2023-05-19 12:47:55 [INFO]\t[TRAIN] epoch: 1, iter: 90/2000, loss: 0.1928, lr: 0.000057, batch_cost: 0.0437, reader_cost: 0.00014, ips: 22.8654 samples/sec | ETA 00:01:23\n",
            "2023-05-19 12:47:55 [INFO]\t[TRAIN] epoch: 1, iter: 100/2000, loss: 0.1245, lr: 0.000057, batch_cost: 0.0436, reader_cost: 0.00014, ips: 22.9284 samples/sec | ETA 00:01:22\n",
            "2023-05-19 12:47:55 [INFO]\t[TRAIN] epoch: 1, iter: 110/2000, loss: 0.1301, lr: 0.000057, batch_cost: 0.0435, reader_cost: 0.00016, ips: 23.0097 samples/sec | ETA 00:01:22\n",
            "2023-05-19 12:47:56 [INFO]\t[TRAIN] epoch: 1, iter: 120/2000, loss: 0.1233, lr: 0.000056, batch_cost: 0.0426, reader_cost: 0.00016, ips: 23.4662 samples/sec | ETA 00:01:20\n",
            "2023-05-19 12:47:56 [INFO]\t[TRAIN] epoch: 1, iter: 130/2000, loss: 0.1577, lr: 0.000056, batch_cost: 0.0437, reader_cost: 0.00016, ips: 22.8790 samples/sec | ETA 00:01:21\n",
            "2023-05-19 12:47:57 [INFO]\t[TRAIN] epoch: 1, iter: 140/2000, loss: 0.1495, lr: 0.000056, batch_cost: 0.0447, reader_cost: 0.00014, ips: 22.3854 samples/sec | ETA 00:01:23\n",
            "2023-05-19 12:47:57 [INFO]\t[TRAIN] epoch: 1, iter: 150/2000, loss: 0.1292, lr: 0.000056, batch_cost: 0.0442, reader_cost: 0.00014, ips: 22.6170 samples/sec | ETA 00:01:21\n",
            "2023-05-19 12:47:58 [INFO]\t[TRAIN] epoch: 1, iter: 160/2000, loss: 0.1538, lr: 0.000055, batch_cost: 0.0432, reader_cost: 0.00013, ips: 23.1390 samples/sec | ETA 00:01:19\n",
            "2023-05-19 12:47:58 [INFO]\t[TRAIN] epoch: 2, iter: 170/2000, loss: 0.1263, lr: 0.000055, batch_cost: 0.0446, reader_cost: 0.00146, ips: 22.4269 samples/sec | ETA 00:01:21\n",
            "2023-05-19 12:47:58 [INFO]\t[TRAIN] epoch: 2, iter: 180/2000, loss: 0.1491, lr: 0.000055, batch_cost: 0.0467, reader_cost: 0.00015, ips: 21.3939 samples/sec | ETA 00:01:25\n",
            "2023-05-19 12:47:59 [INFO]\t[TRAIN] epoch: 2, iter: 190/2000, loss: 0.1317, lr: 0.000054, batch_cost: 0.0449, reader_cost: 0.00015, ips: 22.2523 samples/sec | ETA 00:01:21\n",
            "2023-05-19 12:47:59 [INFO]\t[TRAIN] epoch: 2, iter: 200/2000, loss: 0.1107, lr: 0.000054, batch_cost: 0.0459, reader_cost: 0.00015, ips: 21.7917 samples/sec | ETA 00:01:22\n",
            "2023-05-19 12:47:59 [INFO]\tStart evaluating (total_samples: 169, total_iters: 169)...\n",
            "/content/DT_SegNet/3_Segmentation_Model/paddleseg/core/infer.py:98: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
            "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
            "  rows = np.int(np.ceil(1.0 * (h_im - h_crop) / h_stride)) + 1\n",
            "/content/DT_SegNet/3_Segmentation_Model/paddleseg/core/infer.py:99: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
            "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
            "  cols = np.int(np.ceil(1.0 * (w_im - w_crop) / w_stride)) + 1\n",
            "169/169 [==============================] - 4s 22ms/step - batch_cost: 0.0223 - reader cost: 1.9317e-04\n",
            "2023-05-19 12:48:03 [INFO]\t[EVAL] #Images: 169 mIoU: 0.8986 Acc: 0.9527 Kappa: 0.8926 Dice: 0.9463\n",
            "2023-05-19 12:48:03 [INFO]\t[EVAL] Class IoU: \n",
            "[0.932  0.8652]\n",
            "2023-05-19 12:48:03 [INFO]\t[EVAL] Class Precision: \n",
            "[0.9545 0.9489]\n",
            "2023-05-19 12:48:03 [INFO]\t[EVAL] Class Recall: \n",
            "[0.9754 0.9075]\n",
            "2023-05-19 12:48:04 [INFO]\t[EVAL] The model with the best validation mIoU (0.8986) was saved at iter 200.\n",
            "2023-05-19 12:48:04 [INFO]\t[TRAIN] epoch: 2, iter: 210/2000, loss: 0.1220, lr: 0.000054, batch_cost: 0.0444, reader_cost: 0.00015, ips: 22.5278 samples/sec | ETA 00:01:19\n",
            "2023-05-19 12:48:04 [INFO]\t[TRAIN] epoch: 2, iter: 220/2000, loss: 0.1199, lr: 0.000053, batch_cost: 0.0446, reader_cost: 0.00014, ips: 22.4098 samples/sec | ETA 00:01:19\n",
            "2023-05-19 12:48:05 [INFO]\t[TRAIN] epoch: 2, iter: 230/2000, loss: 0.1089, lr: 0.000053, batch_cost: 0.0444, reader_cost: 0.00014, ips: 22.5080 samples/sec | ETA 00:01:18\n",
            "2023-05-19 12:48:05 [INFO]\t[TRAIN] epoch: 2, iter: 240/2000, loss: 0.1625, lr: 0.000053, batch_cost: 0.0453, reader_cost: 0.00014, ips: 22.0954 samples/sec | ETA 00:01:19\n",
            "2023-05-19 12:48:06 [INFO]\t[TRAIN] epoch: 2, iter: 250/2000, loss: 0.1230, lr: 0.000053, batch_cost: 0.0460, reader_cost: 0.00016, ips: 21.7492 samples/sec | ETA 00:01:20\n",
            "2023-05-19 12:48:06 [INFO]\t[TRAIN] epoch: 2, iter: 260/2000, loss: 0.1162, lr: 0.000052, batch_cost: 0.0476, reader_cost: 0.00018, ips: 21.0009 samples/sec | ETA 00:01:22\n",
            "2023-05-19 12:48:07 [INFO]\t[TRAIN] epoch: 2, iter: 270/2000, loss: 0.1279, lr: 0.000052, batch_cost: 0.0498, reader_cost: 0.00019, ips: 20.0657 samples/sec | ETA 00:01:26\n",
            "2023-05-19 12:48:07 [INFO]\t[TRAIN] epoch: 2, iter: 280/2000, loss: 0.1028, lr: 0.000052, batch_cost: 0.0487, reader_cost: 0.00018, ips: 20.5212 samples/sec | ETA 00:01:23\n",
            "2023-05-19 12:48:08 [INFO]\t[TRAIN] epoch: 2, iter: 290/2000, loss: 0.1298, lr: 0.000051, batch_cost: 0.0483, reader_cost: 0.00021, ips: 20.7030 samples/sec | ETA 00:01:22\n",
            "2023-05-19 12:48:08 [INFO]\t[TRAIN] epoch: 2, iter: 300/2000, loss: 0.1405, lr: 0.000051, batch_cost: 0.0480, reader_cost: 0.00020, ips: 20.8487 samples/sec | ETA 00:01:21\n",
            "2023-05-19 12:48:09 [INFO]\t[TRAIN] epoch: 2, iter: 310/2000, loss: 0.1217, lr: 0.000051, batch_cost: 0.0487, reader_cost: 0.00021, ips: 20.5428 samples/sec | ETA 00:01:22\n",
            "2023-05-19 12:48:09 [INFO]\t[TRAIN] epoch: 2, iter: 320/2000, loss: 0.1105, lr: 0.000050, batch_cost: 0.0483, reader_cost: 0.00021, ips: 20.7225 samples/sec | ETA 00:01:21\n",
            "2023-05-19 12:48:10 [INFO]\t[TRAIN] epoch: 2, iter: 330/2000, loss: 0.1535, lr: 0.000050, batch_cost: 0.0480, reader_cost: 0.00020, ips: 20.8327 samples/sec | ETA 00:01:20\n",
            "2023-05-19 12:48:10 [INFO]\t[TRAIN] epoch: 3, iter: 340/2000, loss: 0.1215, lr: 0.000050, batch_cost: 0.0504, reader_cost: 0.00149, ips: 19.8589 samples/sec | ETA 00:01:23\n",
            "2023-05-19 12:48:11 [INFO]\t[TRAIN] epoch: 3, iter: 350/2000, loss: 0.1510, lr: 0.000050, batch_cost: 0.0464, reader_cost: 0.00016, ips: 21.5289 samples/sec | ETA 00:01:16\n",
            "2023-05-19 12:48:11 [INFO]\t[TRAIN] epoch: 3, iter: 360/2000, loss: 0.1209, lr: 0.000049, batch_cost: 0.0477, reader_cost: 0.00014, ips: 20.9745 samples/sec | ETA 00:01:18\n",
            "2023-05-19 12:48:12 [INFO]\t[TRAIN] epoch: 3, iter: 370/2000, loss: 0.0968, lr: 0.000049, batch_cost: 0.0452, reader_cost: 0.00015, ips: 22.1454 samples/sec | ETA 00:01:13\n",
            "2023-05-19 12:48:12 [INFO]\t[TRAIN] epoch: 3, iter: 380/2000, loss: 0.0917, lr: 0.000049, batch_cost: 0.0444, reader_cost: 0.00014, ips: 22.5423 samples/sec | ETA 00:01:11\n",
            "2023-05-19 12:48:13 [INFO]\t[TRAIN] epoch: 3, iter: 390/2000, loss: 0.1145, lr: 0.000048, batch_cost: 0.0469, reader_cost: 0.00016, ips: 21.3443 samples/sec | ETA 00:01:15\n",
            "2023-05-19 12:48:13 [INFO]\t[TRAIN] epoch: 3, iter: 400/2000, loss: 0.1438, lr: 0.000048, batch_cost: 0.0457, reader_cost: 0.00014, ips: 21.8646 samples/sec | ETA 00:01:13\n",
            "2023-05-19 12:48:13 [INFO]\tStart evaluating (total_samples: 169, total_iters: 169)...\n",
            "169/169 [==============================] - 4s 22ms/step - batch_cost: 0.0221 - reader cost: 1.7939e-04\n",
            "2023-05-19 12:48:17 [INFO]\t[EVAL] #Images: 169 mIoU: 0.9217 Acc: 0.9637 Kappa: 0.9181 Dice: 0.9591\n",
            "2023-05-19 12:48:17 [INFO]\t[EVAL] Class IoU: \n",
            "[0.9471 0.8963]\n",
            "2023-05-19 12:48:17 [INFO]\t[EVAL] Class Precision: \n",
            "[0.9691 0.9527]\n",
            "2023-05-19 12:48:17 [INFO]\t[EVAL] Class Recall: \n",
            "[0.9766 0.938 ]\n",
            "2023-05-19 12:48:17 [INFO]\t[EVAL] The model with the best validation mIoU (0.9217) was saved at iter 400.\n",
            "2023-05-19 12:48:18 [INFO]\t[TRAIN] epoch: 3, iter: 410/2000, loss: 0.1221, lr: 0.000048, batch_cost: 0.0445, reader_cost: 0.00016, ips: 22.4682 samples/sec | ETA 00:01:10\n",
            "2023-05-19 12:48:18 [INFO]\t[TRAIN] epoch: 3, iter: 420/2000, loss: 0.1052, lr: 0.000047, batch_cost: 0.0446, reader_cost: 0.00014, ips: 22.4300 samples/sec | ETA 00:01:10\n",
            "2023-05-19 12:48:18 [INFO]\t[TRAIN] epoch: 3, iter: 430/2000, loss: 0.0795, lr: 0.000047, batch_cost: 0.0440, reader_cost: 0.00015, ips: 22.7081 samples/sec | ETA 00:01:09\n",
            "2023-05-19 12:48:19 [INFO]\t[TRAIN] epoch: 3, iter: 440/2000, loss: 0.1083, lr: 0.000047, batch_cost: 0.0430, reader_cost: 0.00013, ips: 23.2384 samples/sec | ETA 00:01:07\n",
            "2023-05-19 12:48:19 [INFO]\t[TRAIN] epoch: 3, iter: 450/2000, loss: 0.0938, lr: 0.000047, batch_cost: 0.0459, reader_cost: 0.00016, ips: 21.7859 samples/sec | ETA 00:01:11\n",
            "2023-05-19 12:48:20 [INFO]\t[TRAIN] epoch: 3, iter: 460/2000, loss: 0.0949, lr: 0.000046, batch_cost: 0.0450, reader_cost: 0.00015, ips: 22.2461 samples/sec | ETA 00:01:09\n",
            "2023-05-19 12:48:20 [INFO]\t[TRAIN] epoch: 3, iter: 470/2000, loss: 0.1311, lr: 0.000046, batch_cost: 0.0451, reader_cost: 0.00015, ips: 22.1800 samples/sec | ETA 00:01:08\n",
            "2023-05-19 12:48:21 [INFO]\t[TRAIN] epoch: 3, iter: 480/2000, loss: 0.1437, lr: 0.000046, batch_cost: 0.0448, reader_cost: 0.00014, ips: 22.3272 samples/sec | ETA 00:01:08\n",
            "2023-05-19 12:48:21 [INFO]\t[TRAIN] epoch: 3, iter: 490/2000, loss: 0.1090, lr: 0.000045, batch_cost: 0.0447, reader_cost: 0.00014, ips: 22.3478 samples/sec | ETA 00:01:07\n",
            "2023-05-19 12:48:22 [INFO]\t[TRAIN] epoch: 3, iter: 500/2000, loss: 0.1087, lr: 0.000045, batch_cost: 0.0453, reader_cost: 0.00014, ips: 22.0746 samples/sec | ETA 00:01:07\n",
            "2023-05-19 12:48:22 [INFO]\t[TRAIN] epoch: 4, iter: 510/2000, loss: 0.0797, lr: 0.000045, batch_cost: 0.0457, reader_cost: 0.00141, ips: 21.8587 samples/sec | ETA 00:01:08\n",
            "2023-05-19 12:48:22 [INFO]\t[TRAIN] epoch: 4, iter: 520/2000, loss: 0.1134, lr: 0.000044, batch_cost: 0.0457, reader_cost: 0.00016, ips: 21.8786 samples/sec | ETA 00:01:07\n",
            "2023-05-19 12:48:23 [INFO]\t[TRAIN] epoch: 4, iter: 530/2000, loss: 0.1417, lr: 0.000044, batch_cost: 0.0458, reader_cost: 0.00018, ips: 21.8225 samples/sec | ETA 00:01:07\n",
            "2023-05-19 12:48:23 [INFO]\t[TRAIN] epoch: 4, iter: 540/2000, loss: 0.1116, lr: 0.000044, batch_cost: 0.0459, reader_cost: 0.00018, ips: 21.7793 samples/sec | ETA 00:01:07\n",
            "2023-05-19 12:48:24 [INFO]\t[TRAIN] epoch: 4, iter: 550/2000, loss: 0.0907, lr: 0.000044, batch_cost: 0.0456, reader_cost: 0.00017, ips: 21.9209 samples/sec | ETA 00:01:06\n",
            "2023-05-19 12:48:24 [INFO]\t[TRAIN] epoch: 4, iter: 560/2000, loss: 0.1318, lr: 0.000043, batch_cost: 0.0456, reader_cost: 0.00016, ips: 21.9351 samples/sec | ETA 00:01:05\n",
            "2023-05-19 12:48:25 [INFO]\t[TRAIN] epoch: 4, iter: 570/2000, loss: 0.0941, lr: 0.000043, batch_cost: 0.0477, reader_cost: 0.00015, ips: 20.9783 samples/sec | ETA 00:01:08\n",
            "2023-05-19 12:48:25 [INFO]\t[TRAIN] epoch: 4, iter: 580/2000, loss: 0.0886, lr: 0.000043, batch_cost: 0.0467, reader_cost: 0.00015, ips: 21.3971 samples/sec | ETA 00:01:06\n",
            "2023-05-19 12:48:26 [INFO]\t[TRAIN] epoch: 4, iter: 590/2000, loss: 0.1103, lr: 0.000042, batch_cost: 0.0453, reader_cost: 0.00015, ips: 22.0964 samples/sec | ETA 00:01:03\n",
            "2023-05-19 12:48:26 [INFO]\t[TRAIN] epoch: 4, iter: 600/2000, loss: 0.1078, lr: 0.000042, batch_cost: 0.0488, reader_cost: 0.00015, ips: 20.4833 samples/sec | ETA 00:01:08\n",
            "2023-05-19 12:48:26 [INFO]\tStart evaluating (total_samples: 169, total_iters: 169)...\n",
            "169/169 [==============================] - 4s 22ms/step - batch_cost: 0.0223 - reader cost: 1.6873e-04\n",
            "2023-05-19 12:48:30 [INFO]\t[EVAL] #Images: 169 mIoU: 0.9227 Acc: 0.9638 Kappa: 0.9193 Dice: 0.9596\n",
            "2023-05-19 12:48:30 [INFO]\t[EVAL] Class IoU: \n",
            "[0.9465 0.8988]\n",
            "2023-05-19 12:48:30 [INFO]\t[EVAL] Class Precision: \n",
            "[0.9806 0.9319]\n",
            "2023-05-19 12:48:30 [INFO]\t[EVAL] Class Recall: \n",
            "[0.9646 0.962 ]\n",
            "2023-05-19 12:48:30 [INFO]\t[EVAL] The model with the best validation mIoU (0.9227) was saved at iter 600.\n",
            "2023-05-19 12:48:31 [INFO]\t[TRAIN] epoch: 4, iter: 610/2000, loss: 0.1077, lr: 0.000042, batch_cost: 0.0459, reader_cost: 0.00016, ips: 21.7827 samples/sec | ETA 00:01:03\n",
            "2023-05-19 12:48:31 [INFO]\t[TRAIN] epoch: 4, iter: 620/2000, loss: 0.0938, lr: 0.000041, batch_cost: 0.0457, reader_cost: 0.00014, ips: 21.8762 samples/sec | ETA 00:01:03\n",
            "2023-05-19 12:48:32 [INFO]\t[TRAIN] epoch: 4, iter: 630/2000, loss: 0.1085, lr: 0.000041, batch_cost: 0.0453, reader_cost: 0.00015, ips: 22.0700 samples/sec | ETA 00:01:02\n",
            "2023-05-19 12:48:32 [INFO]\t[TRAIN] epoch: 4, iter: 640/2000, loss: 0.0992, lr: 0.000041, batch_cost: 0.0439, reader_cost: 0.00013, ips: 22.7838 samples/sec | ETA 00:00:59\n",
            "2023-05-19 12:48:33 [INFO]\t[TRAIN] epoch: 4, iter: 650/2000, loss: 0.1129, lr: 0.000041, batch_cost: 0.0445, reader_cost: 0.00014, ips: 22.4960 samples/sec | ETA 00:01:00\n",
            "2023-05-19 12:48:33 [INFO]\t[TRAIN] epoch: 4, iter: 660/2000, loss: 0.1368, lr: 0.000040, batch_cost: 0.0435, reader_cost: 0.00014, ips: 22.9706 samples/sec | ETA 00:00:58\n",
            "2023-05-19 12:48:33 [INFO]\t[TRAIN] epoch: 4, iter: 670/2000, loss: 0.1161, lr: 0.000040, batch_cost: 0.0442, reader_cost: 0.00013, ips: 22.6365 samples/sec | ETA 00:00:58\n",
            "2023-05-19 12:48:34 [INFO]\t[TRAIN] epoch: 5, iter: 680/2000, loss: 0.1082, lr: 0.000040, batch_cost: 0.0434, reader_cost: 0.00109, ips: 23.0616 samples/sec | ETA 00:00:57\n",
            "2023-05-19 12:48:34 [INFO]\t[TRAIN] epoch: 5, iter: 690/2000, loss: 0.1256, lr: 0.000039, batch_cost: 0.0448, reader_cost: 0.00013, ips: 22.3380 samples/sec | ETA 00:00:58\n",
            "2023-05-19 12:48:35 [INFO]\t[TRAIN] epoch: 5, iter: 700/2000, loss: 0.0845, lr: 0.000039, batch_cost: 0.0442, reader_cost: 0.00013, ips: 22.6235 samples/sec | ETA 00:00:57\n",
            "2023-05-19 12:48:35 [INFO]\t[TRAIN] epoch: 5, iter: 710/2000, loss: 0.0985, lr: 0.000039, batch_cost: 0.0444, reader_cost: 0.00013, ips: 22.5302 samples/sec | ETA 00:00:57\n",
            "2023-05-19 12:48:36 [INFO]\t[TRAIN] epoch: 5, iter: 720/2000, loss: 0.1173, lr: 0.000038, batch_cost: 0.0432, reader_cost: 0.00012, ips: 23.1403 samples/sec | ETA 00:00:55\n",
            "2023-05-19 12:48:36 [INFO]\t[TRAIN] epoch: 5, iter: 730/2000, loss: 0.1085, lr: 0.000038, batch_cost: 0.0436, reader_cost: 0.00013, ips: 22.9367 samples/sec | ETA 00:00:55\n",
            "2023-05-19 12:48:37 [INFO]\t[TRAIN] epoch: 5, iter: 740/2000, loss: 0.1135, lr: 0.000038, batch_cost: 0.0434, reader_cost: 0.00013, ips: 23.0425 samples/sec | ETA 00:00:54\n",
            "2023-05-19 12:48:37 [INFO]\t[TRAIN] epoch: 5, iter: 750/2000, loss: 0.1191, lr: 0.000038, batch_cost: 0.0443, reader_cost: 0.00013, ips: 22.5698 samples/sec | ETA 00:00:55\n",
            "2023-05-19 12:48:37 [INFO]\t[TRAIN] epoch: 5, iter: 760/2000, loss: 0.0870, lr: 0.000037, batch_cost: 0.0446, reader_cost: 0.00014, ips: 22.4258 samples/sec | ETA 00:00:55\n",
            "2023-05-19 12:48:38 [INFO]\t[TRAIN] epoch: 5, iter: 770/2000, loss: 0.0980, lr: 0.000037, batch_cost: 0.0444, reader_cost: 0.00016, ips: 22.5196 samples/sec | ETA 00:00:54\n",
            "2023-05-19 12:48:38 [INFO]\t[TRAIN] epoch: 5, iter: 780/2000, loss: 0.1036, lr: 0.000037, batch_cost: 0.0448, reader_cost: 0.00015, ips: 22.3019 samples/sec | ETA 00:00:54\n",
            "2023-05-19 12:48:39 [INFO]\t[TRAIN] epoch: 5, iter: 790/2000, loss: 0.0879, lr: 0.000036, batch_cost: 0.0450, reader_cost: 0.00015, ips: 22.2355 samples/sec | ETA 00:00:54\n",
            "2023-05-19 12:48:39 [INFO]\t[TRAIN] epoch: 5, iter: 800/2000, loss: 0.0970, lr: 0.000036, batch_cost: 0.0459, reader_cost: 0.00014, ips: 21.7697 samples/sec | ETA 00:00:55\n",
            "2023-05-19 12:48:39 [INFO]\tStart evaluating (total_samples: 169, total_iters: 169)...\n",
            "169/169 [==============================] - 4s 23ms/step - batch_cost: 0.0226 - reader cost: 1.7555e-04\n",
            "2023-05-19 12:48:43 [INFO]\t[EVAL] #Images: 169 mIoU: 0.9115 Acc: 0.9578 Kappa: 0.9071 Dice: 0.9535\n",
            "2023-05-19 12:48:43 [INFO]\t[EVAL] Class IoU: \n",
            "[0.9372 0.8858]\n",
            "2023-05-19 12:48:43 [INFO]\t[EVAL] Class Precision: \n",
            "[0.9888 0.9033]\n",
            "2023-05-19 12:48:43 [INFO]\t[EVAL] Class Recall: \n",
            "[0.9473 0.9786]\n",
            "2023-05-19 12:48:43 [INFO]\t[EVAL] The model with the best validation mIoU (0.9227) was saved at iter 600.\n",
            "2023-05-19 12:48:44 [INFO]\t[TRAIN] epoch: 5, iter: 810/2000, loss: 0.0937, lr: 0.000036, batch_cost: 0.0460, reader_cost: 0.00015, ips: 21.7327 samples/sec | ETA 00:00:54\n",
            "2023-05-19 12:48:44 [INFO]\t[TRAIN] epoch: 5, iter: 820/2000, loss: 0.1044, lr: 0.000035, batch_cost: 0.0446, reader_cost: 0.00014, ips: 22.4424 samples/sec | ETA 00:00:52\n",
            "2023-05-19 12:48:45 [INFO]\t[TRAIN] epoch: 5, iter: 830/2000, loss: 0.1063, lr: 0.000035, batch_cost: 0.0446, reader_cost: 0.00015, ips: 22.4412 samples/sec | ETA 00:00:52\n",
            "2023-05-19 12:48:45 [INFO]\t[TRAIN] epoch: 5, iter: 840/2000, loss: 0.0816, lr: 0.000035, batch_cost: 0.0441, reader_cost: 0.00013, ips: 22.6716 samples/sec | ETA 00:00:51\n",
            "2023-05-19 12:48:46 [INFO]\t[TRAIN] epoch: 6, iter: 850/2000, loss: 0.0983, lr: 0.000035, batch_cost: 0.0447, reader_cost: 0.00133, ips: 22.3942 samples/sec | ETA 00:00:51\n",
            "2023-05-19 12:48:46 [INFO]\t[TRAIN] epoch: 6, iter: 860/2000, loss: 0.1127, lr: 0.000034, batch_cost: 0.0433, reader_cost: 0.00014, ips: 23.0962 samples/sec | ETA 00:00:49\n",
            "2023-05-19 12:48:46 [INFO]\t[TRAIN] epoch: 6, iter: 870/2000, loss: 0.0864, lr: 0.000034, batch_cost: 0.0431, reader_cost: 0.00014, ips: 23.1986 samples/sec | ETA 00:00:48\n",
            "2023-05-19 12:48:47 [INFO]\t[TRAIN] epoch: 6, iter: 880/2000, loss: 0.1025, lr: 0.000034, batch_cost: 0.0441, reader_cost: 0.00015, ips: 22.6624 samples/sec | ETA 00:00:49\n",
            "2023-05-19 12:48:47 [INFO]\t[TRAIN] epoch: 6, iter: 890/2000, loss: 0.0886, lr: 0.000033, batch_cost: 0.0453, reader_cost: 0.00016, ips: 22.0887 samples/sec | ETA 00:00:50\n",
            "2023-05-19 12:48:48 [INFO]\t[TRAIN] epoch: 6, iter: 900/2000, loss: 0.0840, lr: 0.000033, batch_cost: 0.0448, reader_cost: 0.00015, ips: 22.3201 samples/sec | ETA 00:00:49\n",
            "2023-05-19 12:48:48 [INFO]\t[TRAIN] epoch: 6, iter: 910/2000, loss: 0.1242, lr: 0.000033, batch_cost: 0.0456, reader_cost: 0.00016, ips: 21.9202 samples/sec | ETA 00:00:49\n",
            "2023-05-19 12:48:49 [INFO]\t[TRAIN] epoch: 6, iter: 920/2000, loss: 0.1034, lr: 0.000032, batch_cost: 0.0448, reader_cost: 0.00014, ips: 22.3348 samples/sec | ETA 00:00:48\n",
            "2023-05-19 12:48:49 [INFO]\t[TRAIN] epoch: 6, iter: 930/2000, loss: 0.0874, lr: 0.000032, batch_cost: 0.0449, reader_cost: 0.00014, ips: 22.2729 samples/sec | ETA 00:00:48\n",
            "2023-05-19 12:48:50 [INFO]\t[TRAIN] epoch: 6, iter: 940/2000, loss: 0.0910, lr: 0.000032, batch_cost: 0.0446, reader_cost: 0.00014, ips: 22.4459 samples/sec | ETA 00:00:47\n",
            "2023-05-19 12:48:50 [INFO]\t[TRAIN] epoch: 6, iter: 950/2000, loss: 0.1346, lr: 0.000032, batch_cost: 0.0462, reader_cost: 0.00014, ips: 21.6678 samples/sec | ETA 00:00:48\n",
            "2023-05-19 12:48:51 [INFO]\t[TRAIN] epoch: 6, iter: 960/2000, loss: 0.1109, lr: 0.000031, batch_cost: 0.0455, reader_cost: 0.00013, ips: 21.9842 samples/sec | ETA 00:00:47\n",
            "2023-05-19 12:48:51 [INFO]\t[TRAIN] epoch: 6, iter: 970/2000, loss: 0.1252, lr: 0.000031, batch_cost: 0.0439, reader_cost: 0.00013, ips: 22.7763 samples/sec | ETA 00:00:45\n",
            "2023-05-19 12:48:51 [INFO]\t[TRAIN] epoch: 6, iter: 980/2000, loss: 0.0793, lr: 0.000031, batch_cost: 0.0443, reader_cost: 0.00013, ips: 22.5841 samples/sec | ETA 00:00:45\n",
            "2023-05-19 12:48:52 [INFO]\t[TRAIN] epoch: 6, iter: 990/2000, loss: 0.1074, lr: 0.000030, batch_cost: 0.0476, reader_cost: 0.00014, ips: 20.9890 samples/sec | ETA 00:00:48\n",
            "2023-05-19 12:48:52 [INFO]\t[TRAIN] epoch: 6, iter: 1000/2000, loss: 0.0998, lr: 0.000030, batch_cost: 0.0446, reader_cost: 0.00014, ips: 22.4156 samples/sec | ETA 00:00:44\n",
            "2023-05-19 12:48:52 [INFO]\tStart evaluating (total_samples: 169, total_iters: 169)...\n",
            "169/169 [==============================] - 4s 23ms/step - batch_cost: 0.0225 - reader cost: 1.8139e-04\n",
            "2023-05-19 12:48:56 [INFO]\t[EVAL] #Images: 169 mIoU: 0.9209 Acc: 0.9628 Kappa: 0.9173 Dice: 0.9587\n",
            "2023-05-19 12:48:56 [INFO]\t[EVAL] Class IoU: \n",
            "[0.9449 0.8969]\n",
            "2023-05-19 12:48:56 [INFO]\t[EVAL] Class Precision: \n",
            "[0.9833 0.9247]\n",
            "2023-05-19 12:48:56 [INFO]\t[EVAL] Class Recall: \n",
            "[0.9603 0.9676]\n",
            "2023-05-19 12:48:56 [INFO]\t[EVAL] The model with the best validation mIoU (0.9227) was saved at iter 600.\n",
            "2023-05-19 12:48:57 [INFO]\t[TRAIN] epoch: 6, iter: 1010/2000, loss: 0.1082, lr: 0.000030, batch_cost: 0.0437, reader_cost: 0.00016, ips: 22.8983 samples/sec | ETA 00:00:43\n",
            "2023-05-19 12:48:57 [INFO]\t[TRAIN] epoch: 7, iter: 1020/2000, loss: 0.1058, lr: 0.000029, batch_cost: 0.0461, reader_cost: 0.00103, ips: 21.6736 samples/sec | ETA 00:00:45\n",
            "2023-05-19 12:48:58 [INFO]\t[TRAIN] epoch: 7, iter: 1030/2000, loss: 0.0877, lr: 0.000029, batch_cost: 0.0446, reader_cost: 0.00015, ips: 22.3977 samples/sec | ETA 00:00:43\n",
            "2023-05-19 12:48:58 [INFO]\t[TRAIN] epoch: 7, iter: 1040/2000, loss: 0.1190, lr: 0.000029, batch_cost: 0.0446, reader_cost: 0.00014, ips: 22.4017 samples/sec | ETA 00:00:42\n",
            "2023-05-19 12:48:59 [INFO]\t[TRAIN] epoch: 7, iter: 1050/2000, loss: 0.0878, lr: 0.000029, batch_cost: 0.0439, reader_cost: 0.00014, ips: 22.7570 samples/sec | ETA 00:00:41\n",
            "2023-05-19 12:48:59 [INFO]\t[TRAIN] epoch: 7, iter: 1060/2000, loss: 0.0974, lr: 0.000028, batch_cost: 0.0441, reader_cost: 0.00013, ips: 22.6569 samples/sec | ETA 00:00:41\n",
            "2023-05-19 12:49:00 [INFO]\t[TRAIN] epoch: 7, iter: 1070/2000, loss: 0.1000, lr: 0.000028, batch_cost: 0.0445, reader_cost: 0.00013, ips: 22.4475 samples/sec | ETA 00:00:41\n",
            "2023-05-19 12:49:00 [INFO]\t[TRAIN] epoch: 7, iter: 1080/2000, loss: 0.0845, lr: 0.000028, batch_cost: 0.0443, reader_cost: 0.00013, ips: 22.5684 samples/sec | ETA 00:00:40\n",
            "2023-05-19 12:49:00 [INFO]\t[TRAIN] epoch: 7, iter: 1090/2000, loss: 0.0853, lr: 0.000027, batch_cost: 0.0445, reader_cost: 0.00013, ips: 22.4507 samples/sec | ETA 00:00:40\n",
            "2023-05-19 12:49:01 [INFO]\t[TRAIN] epoch: 7, iter: 1100/2000, loss: 0.0923, lr: 0.000027, batch_cost: 0.0439, reader_cost: 0.00013, ips: 22.7897 samples/sec | ETA 00:00:39\n",
            "2023-05-19 12:49:01 [INFO]\t[TRAIN] epoch: 7, iter: 1110/2000, loss: 0.0764, lr: 0.000027, batch_cost: 0.0443, reader_cost: 0.00013, ips: 22.5783 samples/sec | ETA 00:00:39\n",
            "2023-05-19 12:49:02 [INFO]\t[TRAIN] epoch: 7, iter: 1120/2000, loss: 0.0903, lr: 0.000026, batch_cost: 0.0437, reader_cost: 0.00014, ips: 22.9009 samples/sec | ETA 00:00:38\n",
            "2023-05-19 12:49:02 [INFO]\t[TRAIN] epoch: 7, iter: 1130/2000, loss: 0.0802, lr: 0.000026, batch_cost: 0.0443, reader_cost: 0.00013, ips: 22.5818 samples/sec | ETA 00:00:38\n",
            "2023-05-19 12:49:03 [INFO]\t[TRAIN] epoch: 7, iter: 1140/2000, loss: 0.1045, lr: 0.000026, batch_cost: 0.0448, reader_cost: 0.00014, ips: 22.2978 samples/sec | ETA 00:00:38\n",
            "2023-05-19 12:49:03 [INFO]\t[TRAIN] epoch: 7, iter: 1150/2000, loss: 0.1084, lr: 0.000026, batch_cost: 0.0454, reader_cost: 0.00015, ips: 22.0185 samples/sec | ETA 00:00:38\n",
            "2023-05-19 12:49:04 [INFO]\t[TRAIN] epoch: 7, iter: 1160/2000, loss: 0.0955, lr: 0.000025, batch_cost: 0.0452, reader_cost: 0.00014, ips: 22.1424 samples/sec | ETA 00:00:37\n",
            "2023-05-19 12:49:04 [INFO]\t[TRAIN] epoch: 7, iter: 1170/2000, loss: 0.0813, lr: 0.000025, batch_cost: 0.0438, reader_cost: 0.00014, ips: 22.8126 samples/sec | ETA 00:00:36\n",
            "2023-05-19 12:49:04 [INFO]\t[TRAIN] epoch: 7, iter: 1180/2000, loss: 0.1086, lr: 0.000025, batch_cost: 0.0433, reader_cost: 0.00013, ips: 23.0912 samples/sec | ETA 00:00:35\n",
            "2023-05-19 12:49:05 [INFO]\t[TRAIN] epoch: 8, iter: 1190/2000, loss: 0.0976, lr: 0.000024, batch_cost: 0.0449, reader_cost: 0.00143, ips: 22.2900 samples/sec | ETA 00:00:36\n",
            "2023-05-19 12:49:05 [INFO]\t[TRAIN] epoch: 8, iter: 1200/2000, loss: 0.0814, lr: 0.000024, batch_cost: 0.0442, reader_cost: 0.00013, ips: 22.6239 samples/sec | ETA 00:00:35\n",
            "2023-05-19 12:49:05 [INFO]\tStart evaluating (total_samples: 169, total_iters: 169)...\n",
            "169/169 [==============================] - 4s 23ms/step - batch_cost: 0.0231 - reader cost: 1.8914e-04\n",
            "2023-05-19 12:49:09 [INFO]\t[EVAL] #Images: 169 mIoU: 0.9339 Acc: 0.9696 Kappa: 0.9314 Dice: 0.9657\n",
            "2023-05-19 12:49:09 [INFO]\t[EVAL] Class IoU: \n",
            "[0.9556 0.9122]\n",
            "2023-05-19 12:49:09 [INFO]\t[EVAL] Class Precision: \n",
            "[0.9719 0.9649]\n",
            "2023-05-19 12:49:09 [INFO]\t[EVAL] Class Recall: \n",
            "[0.9827 0.9435]\n",
            "2023-05-19 12:49:10 [INFO]\t[EVAL] The model with the best validation mIoU (0.9339) was saved at iter 1200.\n",
            "2023-05-19 12:49:10 [INFO]\t[TRAIN] epoch: 8, iter: 1210/2000, loss: 0.0784, lr: 0.000024, batch_cost: 0.0487, reader_cost: 0.00021, ips: 20.5434 samples/sec | ETA 00:00:38\n",
            "2023-05-19 12:49:11 [INFO]\t[TRAIN] epoch: 8, iter: 1220/2000, loss: 0.0852, lr: 0.000023, batch_cost: 0.0505, reader_cost: 0.00018, ips: 19.8061 samples/sec | ETA 00:00:39\n",
            "2023-05-19 12:49:11 [INFO]\t[TRAIN] epoch: 8, iter: 1230/2000, loss: 0.1146, lr: 0.000023, batch_cost: 0.0490, reader_cost: 0.00018, ips: 20.4099 samples/sec | ETA 00:00:37\n",
            "2023-05-19 12:49:12 [INFO]\t[TRAIN] epoch: 8, iter: 1240/2000, loss: 0.0943, lr: 0.000023, batch_cost: 0.0458, reader_cost: 0.00015, ips: 21.8225 samples/sec | ETA 00:00:34\n",
            "2023-05-19 12:49:12 [INFO]\t[TRAIN] epoch: 8, iter: 1250/2000, loss: 0.0888, lr: 0.000023, batch_cost: 0.0457, reader_cost: 0.00015, ips: 21.8881 samples/sec | ETA 00:00:34\n",
            "2023-05-19 12:49:12 [INFO]\t[TRAIN] epoch: 8, iter: 1260/2000, loss: 0.1103, lr: 0.000022, batch_cost: 0.0450, reader_cost: 0.00017, ips: 22.2276 samples/sec | ETA 00:00:33\n",
            "2023-05-19 12:49:13 [INFO]\t[TRAIN] epoch: 8, iter: 1270/2000, loss: 0.1038, lr: 0.000022, batch_cost: 0.0441, reader_cost: 0.00017, ips: 22.6704 samples/sec | ETA 00:00:32\n",
            "2023-05-19 12:49:13 [INFO]\t[TRAIN] epoch: 8, iter: 1280/2000, loss: 0.0907, lr: 0.000022, batch_cost: 0.0458, reader_cost: 0.00015, ips: 21.8398 samples/sec | ETA 00:00:32\n",
            "2023-05-19 12:49:14 [INFO]\t[TRAIN] epoch: 8, iter: 1290/2000, loss: 0.1134, lr: 0.000021, batch_cost: 0.0443, reader_cost: 0.00015, ips: 22.5546 samples/sec | ETA 00:00:31\n",
            "2023-05-19 12:49:14 [INFO]\t[TRAIN] epoch: 8, iter: 1300/2000, loss: 0.0912, lr: 0.000021, batch_cost: 0.0463, reader_cost: 0.00014, ips: 21.5793 samples/sec | ETA 00:00:32\n",
            "2023-05-19 12:49:15 [INFO]\t[TRAIN] epoch: 8, iter: 1310/2000, loss: 0.0750, lr: 0.000021, batch_cost: 0.0446, reader_cost: 0.00013, ips: 22.4307 samples/sec | ETA 00:00:30\n",
            "2023-05-19 12:49:15 [INFO]\t[TRAIN] epoch: 8, iter: 1320/2000, loss: 0.0792, lr: 0.000020, batch_cost: 0.0448, reader_cost: 0.00014, ips: 22.3163 samples/sec | ETA 00:00:30\n",
            "2023-05-19 12:49:16 [INFO]\t[TRAIN] epoch: 8, iter: 1330/2000, loss: 0.1074, lr: 0.000020, batch_cost: 0.0452, reader_cost: 0.00013, ips: 22.1158 samples/sec | ETA 00:00:30\n",
            "2023-05-19 12:49:16 [INFO]\t[TRAIN] epoch: 8, iter: 1340/2000, loss: 0.0874, lr: 0.000020, batch_cost: 0.0441, reader_cost: 0.00014, ips: 22.6587 samples/sec | ETA 00:00:29\n",
            "2023-05-19 12:49:17 [INFO]\t[TRAIN] epoch: 8, iter: 1350/2000, loss: 0.0771, lr: 0.000020, batch_cost: 0.0434, reader_cost: 0.00014, ips: 23.0481 samples/sec | ETA 00:00:28\n",
            "2023-05-19 12:49:17 [INFO]\t[TRAIN] epoch: 9, iter: 1360/2000, loss: 0.0993, lr: 0.000019, batch_cost: 0.0458, reader_cost: 0.00107, ips: 21.8222 samples/sec | ETA 00:00:29\n",
            "2023-05-19 12:49:17 [INFO]\t[TRAIN] epoch: 9, iter: 1370/2000, loss: 0.0889, lr: 0.000019, batch_cost: 0.0454, reader_cost: 0.00014, ips: 22.0120 samples/sec | ETA 00:00:28\n",
            "2023-05-19 12:49:18 [INFO]\t[TRAIN] epoch: 9, iter: 1380/2000, loss: 0.0983, lr: 0.000019, batch_cost: 0.0439, reader_cost: 0.00013, ips: 22.7608 samples/sec | ETA 00:00:27\n",
            "2023-05-19 12:49:18 [INFO]\t[TRAIN] epoch: 9, iter: 1390/2000, loss: 0.0923, lr: 0.000018, batch_cost: 0.0444, reader_cost: 0.00013, ips: 22.5350 samples/sec | ETA 00:00:27\n",
            "2023-05-19 12:49:19 [INFO]\t[TRAIN] epoch: 9, iter: 1400/2000, loss: 0.0936, lr: 0.000018, batch_cost: 0.0435, reader_cost: 0.00014, ips: 22.9797 samples/sec | ETA 00:00:26\n",
            "2023-05-19 12:49:19 [INFO]\tStart evaluating (total_samples: 169, total_iters: 169)...\n",
            "169/169 [==============================] - 4s 23ms/step - batch_cost: 0.0225 - reader cost: 1.6670e-04\n",
            "2023-05-19 12:49:23 [INFO]\t[EVAL] #Images: 169 mIoU: 0.9416 Acc: 0.9730 Kappa: 0.9397 Dice: 0.9699\n",
            "2023-05-19 12:49:23 [INFO]\t[EVAL] Class IoU: \n",
            "[0.9599 0.9234]\n",
            "2023-05-19 12:49:23 [INFO]\t[EVAL] Class Precision: \n",
            "[0.9868 0.9465]\n",
            "2023-05-19 12:49:23 [INFO]\t[EVAL] Class Recall: \n",
            "[0.9723 0.9742]\n",
            "2023-05-19 12:49:23 [INFO]\t[EVAL] The model with the best validation mIoU (0.9416) was saved at iter 1400.\n",
            "2023-05-19 12:49:23 [INFO]\t[TRAIN] epoch: 9, iter: 1410/2000, loss: 0.0955, lr: 0.000018, batch_cost: 0.0468, reader_cost: 0.00016, ips: 21.3714 samples/sec | ETA 00:00:27\n",
            "2023-05-19 12:49:24 [INFO]\t[TRAIN] epoch: 9, iter: 1420/2000, loss: 0.0739, lr: 0.000017, batch_cost: 0.0481, reader_cost: 0.00016, ips: 20.7856 samples/sec | ETA 00:00:27\n",
            "2023-05-19 12:49:24 [INFO]\t[TRAIN] epoch: 9, iter: 1430/2000, loss: 0.1082, lr: 0.000017, batch_cost: 0.0465, reader_cost: 0.00014, ips: 21.4952 samples/sec | ETA 00:00:26\n",
            "2023-05-19 12:49:25 [INFO]\t[TRAIN] epoch: 9, iter: 1440/2000, loss: 0.0873, lr: 0.000017, batch_cost: 0.0443, reader_cost: 0.00014, ips: 22.5512 samples/sec | ETA 00:00:24\n",
            "2023-05-19 12:49:25 [INFO]\t[TRAIN] epoch: 9, iter: 1450/2000, loss: 0.0753, lr: 0.000017, batch_cost: 0.0440, reader_cost: 0.00013, ips: 22.7247 samples/sec | ETA 00:00:24\n",
            "2023-05-19 12:49:26 [INFO]\t[TRAIN] epoch: 9, iter: 1460/2000, loss: 0.1013, lr: 0.000016, batch_cost: 0.0444, reader_cost: 0.00015, ips: 22.5209 samples/sec | ETA 00:00:23\n",
            "2023-05-19 12:49:26 [INFO]\t[TRAIN] epoch: 9, iter: 1470/2000, loss: 0.0995, lr: 0.000016, batch_cost: 0.0440, reader_cost: 0.00013, ips: 22.7147 samples/sec | ETA 00:00:23\n",
            "2023-05-19 12:49:27 [INFO]\t[TRAIN] epoch: 9, iter: 1480/2000, loss: 0.0781, lr: 0.000016, batch_cost: 0.0436, reader_cost: 0.00013, ips: 22.9552 samples/sec | ETA 00:00:22\n",
            "2023-05-19 12:49:27 [INFO]\t[TRAIN] epoch: 9, iter: 1490/2000, loss: 0.0879, lr: 0.000015, batch_cost: 0.0444, reader_cost: 0.00014, ips: 22.5396 samples/sec | ETA 00:00:22\n",
            "2023-05-19 12:49:27 [INFO]\t[TRAIN] epoch: 9, iter: 1500/2000, loss: 0.0907, lr: 0.000015, batch_cost: 0.0453, reader_cost: 0.00015, ips: 22.0743 samples/sec | ETA 00:00:22\n",
            "2023-05-19 12:49:28 [INFO]\t[TRAIN] epoch: 9, iter: 1510/2000, loss: 0.1141, lr: 0.000015, batch_cost: 0.0443, reader_cost: 0.00014, ips: 22.5876 samples/sec | ETA 00:00:21\n",
            "2023-05-19 12:49:28 [INFO]\t[TRAIN] epoch: 9, iter: 1520/2000, loss: 0.0868, lr: 0.000014, batch_cost: 0.0437, reader_cost: 0.00013, ips: 22.8780 samples/sec | ETA 00:00:20\n",
            "2023-05-19 12:49:29 [INFO]\t[TRAIN] epoch: 10, iter: 1530/2000, loss: 0.1322, lr: 0.000014, batch_cost: 0.0450, reader_cost: 0.00100, ips: 22.2039 samples/sec | ETA 00:00:21\n",
            "2023-05-19 12:49:29 [INFO]\t[TRAIN] epoch: 10, iter: 1540/2000, loss: 0.0793, lr: 0.000014, batch_cost: 0.0437, reader_cost: 0.00014, ips: 22.8830 samples/sec | ETA 00:00:20\n",
            "2023-05-19 12:49:30 [INFO]\t[TRAIN] epoch: 10, iter: 1550/2000, loss: 0.0956, lr: 0.000014, batch_cost: 0.0448, reader_cost: 0.00013, ips: 22.3207 samples/sec | ETA 00:00:20\n",
            "2023-05-19 12:49:30 [INFO]\t[TRAIN] epoch: 10, iter: 1560/2000, loss: 0.1004, lr: 0.000013, batch_cost: 0.0454, reader_cost: 0.00013, ips: 22.0247 samples/sec | ETA 00:00:19\n",
            "2023-05-19 12:49:31 [INFO]\t[TRAIN] epoch: 10, iter: 1570/2000, loss: 0.0908, lr: 0.000013, batch_cost: 0.0440, reader_cost: 0.00014, ips: 22.7044 samples/sec | ETA 00:00:18\n",
            "2023-05-19 12:49:31 [INFO]\t[TRAIN] epoch: 10, iter: 1580/2000, loss: 0.0639, lr: 0.000013, batch_cost: 0.0432, reader_cost: 0.00013, ips: 23.1567 samples/sec | ETA 00:00:18\n",
            "2023-05-19 12:49:31 [INFO]\t[TRAIN] epoch: 10, iter: 1590/2000, loss: 0.0897, lr: 0.000012, batch_cost: 0.0431, reader_cost: 0.00013, ips: 23.2131 samples/sec | ETA 00:00:17\n",
            "2023-05-19 12:49:32 [INFO]\t[TRAIN] epoch: 10, iter: 1600/2000, loss: 0.0748, lr: 0.000012, batch_cost: 0.0423, reader_cost: 0.00013, ips: 23.6492 samples/sec | ETA 00:00:16\n",
            "2023-05-19 12:49:32 [INFO]\tStart evaluating (total_samples: 169, total_iters: 169)...\n",
            "169/169 [==============================] - 4s 22ms/step - batch_cost: 0.0216 - reader cost: 1.6297e-04\n",
            "2023-05-19 12:49:36 [INFO]\t[EVAL] #Images: 169 mIoU: 0.9453 Acc: 0.9748 Kappa: 0.9436 Dice: 0.9718\n",
            "2023-05-19 12:49:36 [INFO]\t[EVAL] Class IoU: \n",
            "[0.9626 0.9279]\n",
            "2023-05-19 12:49:36 [INFO]\t[EVAL] Class Precision: \n",
            "[0.9848 0.9552]\n",
            "2023-05-19 12:49:36 [INFO]\t[EVAL] Class Recall: \n",
            "[0.9771 0.9701]\n",
            "2023-05-19 12:49:36 [INFO]\t[EVAL] The model with the best validation mIoU (0.9453) was saved at iter 1600.\n",
            "2023-05-19 12:49:36 [INFO]\t[TRAIN] epoch: 10, iter: 1610/2000, loss: 0.1142, lr: 0.000012, batch_cost: 0.0460, reader_cost: 0.00014, ips: 21.7208 samples/sec | ETA 00:00:17\n",
            "2023-05-19 12:49:37 [INFO]\t[TRAIN] epoch: 10, iter: 1620/2000, loss: 0.0860, lr: 0.000011, batch_cost: 0.0440, reader_cost: 0.00014, ips: 22.7031 samples/sec | ETA 00:00:16\n",
            "2023-05-19 12:49:37 [INFO]\t[TRAIN] epoch: 10, iter: 1630/2000, loss: 0.0753, lr: 0.000011, batch_cost: 0.0451, reader_cost: 0.00014, ips: 22.1651 samples/sec | ETA 00:00:16\n",
            "2023-05-19 12:49:38 [INFO]\t[TRAIN] epoch: 10, iter: 1640/2000, loss: 0.0934, lr: 0.000011, batch_cost: 0.0469, reader_cost: 0.00014, ips: 21.3443 samples/sec | ETA 00:00:16\n",
            "2023-05-19 12:49:38 [INFO]\t[TRAIN] epoch: 10, iter: 1650/2000, loss: 0.0786, lr: 0.000011, batch_cost: 0.0444, reader_cost: 0.00013, ips: 22.5097 samples/sec | ETA 00:00:15\n",
            "2023-05-19 12:49:39 [INFO]\t[TRAIN] epoch: 10, iter: 1660/2000, loss: 0.0966, lr: 0.000010, batch_cost: 0.0483, reader_cost: 0.00018, ips: 20.7000 samples/sec | ETA 00:00:16\n",
            "2023-05-19 12:49:39 [INFO]\t[TRAIN] epoch: 10, iter: 1670/2000, loss: 0.0859, lr: 0.000010, batch_cost: 0.0473, reader_cost: 0.00013, ips: 21.1442 samples/sec | ETA 00:00:15\n",
            "2023-05-19 12:49:40 [INFO]\t[TRAIN] epoch: 10, iter: 1680/2000, loss: 0.0834, lr: 0.000010, batch_cost: 0.0473, reader_cost: 0.00013, ips: 21.1487 samples/sec | ETA 00:00:15\n",
            "2023-05-19 12:49:40 [INFO]\t[TRAIN] epoch: 10, iter: 1690/2000, loss: 0.0822, lr: 0.000009, batch_cost: 0.0457, reader_cost: 0.00015, ips: 21.8774 samples/sec | ETA 00:00:14\n",
            "2023-05-19 12:49:41 [INFO]\t[TRAIN] epoch: 11, iter: 1700/2000, loss: 0.0709, lr: 0.000009, batch_cost: 0.0471, reader_cost: 0.00137, ips: 21.2253 samples/sec | ETA 00:00:14\n",
            "2023-05-19 12:49:41 [INFO]\t[TRAIN] epoch: 11, iter: 1710/2000, loss: 0.0766, lr: 0.000009, batch_cost: 0.0442, reader_cost: 0.00013, ips: 22.6086 samples/sec | ETA 00:00:12\n",
            "2023-05-19 12:49:41 [INFO]\t[TRAIN] epoch: 11, iter: 1720/2000, loss: 0.0789, lr: 0.000008, batch_cost: 0.0463, reader_cost: 0.00014, ips: 21.5926 samples/sec | ETA 00:00:12\n",
            "2023-05-19 12:49:42 [INFO]\t[TRAIN] epoch: 11, iter: 1730/2000, loss: 0.0845, lr: 0.000008, batch_cost: 0.0439, reader_cost: 0.00013, ips: 22.7994 samples/sec | ETA 00:00:11\n",
            "2023-05-19 12:49:42 [INFO]\t[TRAIN] epoch: 11, iter: 1740/2000, loss: 0.0804, lr: 0.000008, batch_cost: 0.0441, reader_cost: 0.00015, ips: 22.6872 samples/sec | ETA 00:00:11\n",
            "2023-05-19 12:49:43 [INFO]\t[TRAIN] epoch: 11, iter: 1750/2000, loss: 0.0780, lr: 0.000008, batch_cost: 0.0443, reader_cost: 0.00015, ips: 22.5855 samples/sec | ETA 00:00:11\n",
            "2023-05-19 12:49:43 [INFO]\t[TRAIN] epoch: 11, iter: 1760/2000, loss: 0.0757, lr: 0.000007, batch_cost: 0.0435, reader_cost: 0.00015, ips: 22.9981 samples/sec | ETA 00:00:10\n",
            "2023-05-19 12:49:44 [INFO]\t[TRAIN] epoch: 11, iter: 1770/2000, loss: 0.1139, lr: 0.000007, batch_cost: 0.0436, reader_cost: 0.00015, ips: 22.9265 samples/sec | ETA 00:00:10\n",
            "2023-05-19 12:49:44 [INFO]\t[TRAIN] epoch: 11, iter: 1780/2000, loss: 0.0669, lr: 0.000007, batch_cost: 0.0431, reader_cost: 0.00014, ips: 23.2042 samples/sec | ETA 00:00:09\n",
            "2023-05-19 12:49:44 [INFO]\t[TRAIN] epoch: 11, iter: 1790/2000, loss: 0.0992, lr: 0.000006, batch_cost: 0.0432, reader_cost: 0.00015, ips: 23.1602 samples/sec | ETA 00:00:09\n",
            "2023-05-19 12:49:45 [INFO]\t[TRAIN] epoch: 11, iter: 1800/2000, loss: 0.0893, lr: 0.000006, batch_cost: 0.0444, reader_cost: 0.00016, ips: 22.5320 samples/sec | ETA 00:00:08\n",
            "2023-05-19 12:49:45 [INFO]\tStart evaluating (total_samples: 169, total_iters: 169)...\n",
            "169/169 [==============================] - 4s 22ms/step - batch_cost: 0.0218 - reader cost: 1.6314e-04\n",
            "2023-05-19 12:49:49 [INFO]\t[EVAL] #Images: 169 mIoU: 0.9466 Acc: 0.9755 Kappa: 0.9450 Dice: 0.9725\n",
            "2023-05-19 12:49:49 [INFO]\t[EVAL] Class IoU: \n",
            "[0.9637 0.9295]\n",
            "2023-05-19 12:49:49 [INFO]\t[EVAL] Class Precision: \n",
            "[0.9832 0.9602]\n",
            "2023-05-19 12:49:49 [INFO]\t[EVAL] Class Recall: \n",
            "[0.9799 0.9667]\n",
            "2023-05-19 12:49:49 [INFO]\t[EVAL] The model with the best validation mIoU (0.9466) was saved at iter 1800.\n",
            "2023-05-19 12:49:49 [INFO]\t[TRAIN] epoch: 11, iter: 1810/2000, loss: 0.0986, lr: 0.000006, batch_cost: 0.0436, reader_cost: 0.00014, ips: 22.9495 samples/sec | ETA 00:00:08\n",
            "2023-05-19 12:49:50 [INFO]\t[TRAIN] epoch: 11, iter: 1820/2000, loss: 0.0766, lr: 0.000005, batch_cost: 0.0432, reader_cost: 0.00013, ips: 23.1579 samples/sec | ETA 00:00:07\n",
            "2023-05-19 12:49:50 [INFO]\t[TRAIN] epoch: 11, iter: 1830/2000, loss: 0.0679, lr: 0.000005, batch_cost: 0.0440, reader_cost: 0.00013, ips: 22.7269 samples/sec | ETA 00:00:07\n",
            "2023-05-19 12:49:51 [INFO]\t[TRAIN] epoch: 11, iter: 1840/2000, loss: 0.1010, lr: 0.000005, batch_cost: 0.0463, reader_cost: 0.00013, ips: 21.6121 samples/sec | ETA 00:00:07\n",
            "2023-05-19 12:49:51 [INFO]\t[TRAIN] epoch: 11, iter: 1850/2000, loss: 0.0708, lr: 0.000005, batch_cost: 0.0466, reader_cost: 0.00014, ips: 21.4449 samples/sec | ETA 00:00:06\n",
            "2023-05-19 12:49:52 [INFO]\t[TRAIN] epoch: 12, iter: 1860/2000, loss: 0.0762, lr: 0.000004, batch_cost: 0.0475, reader_cost: 0.00142, ips: 21.0349 samples/sec | ETA 00:00:06\n",
            "2023-05-19 12:49:52 [INFO]\t[TRAIN] epoch: 12, iter: 1870/2000, loss: 0.0849, lr: 0.000004, batch_cost: 0.0446, reader_cost: 0.00013, ips: 22.4221 samples/sec | ETA 00:00:05\n",
            "2023-05-19 12:49:53 [INFO]\t[TRAIN] epoch: 12, iter: 1880/2000, loss: 0.0816, lr: 0.000004, batch_cost: 0.0441, reader_cost: 0.00014, ips: 22.6646 samples/sec | ETA 00:00:05\n",
            "2023-05-19 12:49:53 [INFO]\t[TRAIN] epoch: 12, iter: 1890/2000, loss: 0.0782, lr: 0.000003, batch_cost: 0.0434, reader_cost: 0.00013, ips: 23.0234 samples/sec | ETA 00:00:04\n",
            "2023-05-19 12:49:53 [INFO]\t[TRAIN] epoch: 12, iter: 1900/2000, loss: 0.0666, lr: 0.000003, batch_cost: 0.0429, reader_cost: 0.00013, ips: 23.3042 samples/sec | ETA 00:00:04\n",
            "2023-05-19 12:49:54 [INFO]\t[TRAIN] epoch: 12, iter: 1910/2000, loss: 0.0783, lr: 0.000003, batch_cost: 0.0430, reader_cost: 0.00012, ips: 23.2778 samples/sec | ETA 00:00:03\n",
            "2023-05-19 12:49:54 [INFO]\t[TRAIN] epoch: 12, iter: 1920/2000, loss: 0.0753, lr: 0.000002, batch_cost: 0.0433, reader_cost: 0.00013, ips: 23.0708 samples/sec | ETA 00:00:03\n",
            "2023-05-19 12:49:55 [INFO]\t[TRAIN] epoch: 12, iter: 1930/2000, loss: 0.0988, lr: 0.000002, batch_cost: 0.0428, reader_cost: 0.00012, ips: 23.3470 samples/sec | ETA 00:00:02\n",
            "2023-05-19 12:49:55 [INFO]\t[TRAIN] epoch: 12, iter: 1940/2000, loss: 0.0849, lr: 0.000002, batch_cost: 0.0437, reader_cost: 0.00014, ips: 22.8974 samples/sec | ETA 00:00:02\n",
            "2023-05-19 12:49:56 [INFO]\t[TRAIN] epoch: 12, iter: 1950/2000, loss: 0.0964, lr: 0.000002, batch_cost: 0.0430, reader_cost: 0.00012, ips: 23.2702 samples/sec | ETA 00:00:02\n",
            "2023-05-19 12:49:56 [INFO]\t[TRAIN] epoch: 12, iter: 1960/2000, loss: 0.0783, lr: 0.000001, batch_cost: 0.0440, reader_cost: 0.00013, ips: 22.7444 samples/sec | ETA 00:00:01\n",
            "2023-05-19 12:49:57 [INFO]\t[TRAIN] epoch: 12, iter: 1970/2000, loss: 0.0729, lr: 0.000001, batch_cost: 0.0440, reader_cost: 0.00013, ips: 22.7198 samples/sec | ETA 00:00:01\n",
            "2023-05-19 12:49:57 [INFO]\t[TRAIN] epoch: 12, iter: 1980/2000, loss: 0.0911, lr: 0.000001, batch_cost: 0.0441, reader_cost: 0.00014, ips: 22.6742 samples/sec | ETA 00:00:00\n",
            "2023-05-19 12:49:57 [INFO]\t[TRAIN] epoch: 12, iter: 1990/2000, loss: 0.0754, lr: 0.000000, batch_cost: 0.0443, reader_cost: 0.00014, ips: 22.5819 samples/sec | ETA 00:00:00\n",
            "2023-05-19 12:49:58 [INFO]\t[TRAIN] epoch: 12, iter: 2000/2000, loss: 0.0784, lr: 0.000000, batch_cost: 0.0438, reader_cost: 0.00014, ips: 22.8472 samples/sec | ETA 00:00:00\n",
            "2023-05-19 12:49:58 [INFO]\tStart evaluating (total_samples: 169, total_iters: 169)...\n",
            "169/169 [==============================] - 4s 22ms/step - batch_cost: 0.0220 - reader cost: 1.7018e-04\n",
            "2023-05-19 12:50:02 [INFO]\t[EVAL] #Images: 169 mIoU: 0.9476 Acc: 0.9760 Kappa: 0.9461 Dice: 0.9730\n",
            "2023-05-19 12:50:02 [INFO]\t[EVAL] Class IoU: \n",
            "[0.9645 0.9308]\n",
            "2023-05-19 12:50:02 [INFO]\t[EVAL] Class Precision: \n",
            "[0.9828 0.9624]\n",
            "2023-05-19 12:50:02 [INFO]\t[EVAL] Class Recall: \n",
            "[0.981  0.9659]\n",
            "2023-05-19 12:50:02 [INFO]\t[EVAL] The model with the best validation mIoU (0.9476) was saved at iter 2000.\n",
            "<class 'paddle.nn.layer.conv.Conv2D'>'s flops has been counted\n",
            "Cannot find suitable count function for <class 'paddle.nn.layer.norm.LayerNorm'>. Treat it as zero FLOPs.\n",
            "<class 'paddle.nn.layer.common.Linear'>'s flops has been counted\n",
            "<class 'paddle.nn.layer.common.Dropout'>'s flops has been counted\n",
            "Cannot find suitable count function for <class 'paddleseg.models.backbones.transformer_utils.Identity'>. Treat it as zero FLOPs.\n",
            "Cannot find suitable count function for <class 'paddle.nn.layer.activation.GELU'>. Treat it as zero FLOPs.\n",
            "Cannot find suitable count function for <class 'paddleseg.models.backbones.transformer_utils.DropPath'>. Treat it as zero FLOPs.\n",
            "Cannot find suitable count function for <class 'paddle.nn.layer.common.Dropout2D'>. Treat it as zero FLOPs.\n",
            "<class 'paddle.nn.layer.norm.BatchNorm2D'>'s flops has been counted\n",
            "<class 'paddle.nn.layer.activation.ReLU'>'s flops has been counted\n",
            "Total Flops: 13203898368     Total Params: 13678274\n",
            "2023-05-19 12:50:03 [WARNING]\tThis `train.py` will be removed in version 2.8, please use `tools/train.py`.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Download trained models"
      ],
      "metadata": {
        "id": "ODN1kGs934JS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Set destination\n",
        "dest = \"google_drive\" #@param [\"google_drive\", \"local\"]\n"
      ],
      "metadata": {
        "id": "BsQvrI5p3_HE"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Get detection model exp names\n",
        "%cd /content/DT_SegNet/1_Detection_Model/runs/train\n",
        "%ls"
      ],
      "metadata": {
        "id": "j5Vzf7yb2osA",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "808a6717-8aeb-4493-8a46-0a736ef4f219"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/DT_SegNet/1_Detection_Model/runs/train\n",
            "\u001b[0m\u001b[01;34mexp\u001b[0m/  \u001b[01;34mexp2\u001b[0m/  \u001b[01;34mexp3\u001b[0m/\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "if dest == 'google_drive':\n",
        "  drive.mount('/content/drive')\n",
        "  Path('/content/drive/My Drive/DT_SegNet_Finetune').mkdir(exist_ok=True, parents=True)"
      ],
      "metadata": {
        "id": "OOB1-Bcm4ZRl",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "95b3cdaa-0182-42fd-feaf-883c25fb8967"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/pexpect/popen_spawn.py:60: DeprecationWarning: setDaemon() is deprecated, set the daemon attribute instead\n",
            "  self._read_thread.setDaemon(True)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Download finetuned detection model\n",
        "%cd /content/DT_SegNet\n",
        "\n",
        "exp_name = 'exp' #@param {type:\"string\"}\n",
        "\n",
        "location = f'/content/DT_SegNet/1_Detection_Model/runs/train/{exp_name}/weights/best.pt'\n",
        "\n",
        "if dest == 'local':\n",
        "  files.download(location)\n",
        "else:\n",
        "  !cp {location} \"/content/drive/My Drive/DT_SegNet_Finetune/detection.pt\""
      ],
      "metadata": {
        "id": "Ws3yJ9ngZll1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bc3758b0-2ca4-4f71-90b1-5d9dcb05a1c2"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/DT_SegNet\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Download finetuned detection model (with training log)\n",
        "%cd /content/DT_SegNet/\n",
        "\n",
        "exp_name = 'exp' #@param {type:\"string\"}\n",
        "\n",
        "!zip -qr '/content/DT_SegNet/Detection.zip' /content/DT_SegNet/1_Detection_Model/runs/train/{exp_name}\n",
        "\n",
        "location = '/content/DT_SegNet/Detection.zip'\n",
        "\n",
        "if dest == 'local':\n",
        "  files.download(location)\n",
        "else:\n",
        "  !cp {location} \"/content/drive/My Drive/DT_SegNet_Finetune/detection.zip\""
      ],
      "metadata": {
        "id": "FrQ_oy3E2ypZ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "151317e8-dad0-4f6b-d39e-c26b87a36ac6"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/DT_SegNet\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Download finetuned segmentation model\n",
        "\n",
        "%cd /content/DT_SegNet\n",
        "\n",
        "location = '/content/DT_SegNet/Segmentation_Model_Output/Finetune/best_model/model.pdparams'\n",
        "\n",
        "if dest == 'local':\n",
        "  files.download(location)\n",
        "else:\n",
        "  !cp {location} \"/content/drive/My Drive/DT_SegNet_Finetune/segmentation.pdparams\""
      ],
      "metadata": {
        "id": "d7jkoOUL11tX",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b44a85fe-8827-43ea-e464-2f9d7e2a34db"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/DT_SegNet\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Download finetuned segmentation model (with training log)\n",
        "%cd /content/DT_SegNet\n",
        "\n",
        "!zip -qr '/content/DT_SegNet/Segmentation.zip' Segmentation_Model_Output/\n",
        "\n",
        "location = '/content/DT_SegNet/Segmentation.zip'\n",
        "\n",
        "if dest == 'local':\n",
        "  files.download(location)\n",
        "else:\n",
        "  !cp {location} \"/content/drive/My Drive/DT_SegNet_Finetune/segmentation.zip\""
      ],
      "metadata": {
        "id": "FosvB5mi2Q4H",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e2d32633-eb39-45fd-8133-4f2a06a414ae"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/DT_SegNet\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "if dest == 'google_drive':\n",
        "  drive.flush_and_unmount()"
      ],
      "metadata": {
        "id": "YTEhuXhu5fCW"
      },
      "execution_count": 26,
      "outputs": []
    }
  ]
}