[SStato]Yolov7_OBB_KFIOU_112_01_18.ipynb

{
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  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU",
    "gpuClass": "standard"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "#How To Train YOLOv5 Oriented Bounding Boxes Custom Object Detection\n",
        "\n",
        "Oriented bounding boxes are bounding boxes rotated to better fit the objects represented on an angle. In this notebook, we use the YOLOv5-OBB repository in order to train an already annotated dataset to view the Oriented bounding boxes detection at play.\n",
        "\n",
        "\n",
        "## Instructions\n",
        "\n",
        "Run one cell at at time and run:\n",
        "\n",
        "```\n",
        ">>> import torch\n",
        ">>> torch.version.cuda\n",
        ">>> exit()\n",
        "```\n",
        "\n",
        "when prompted to in the first cell. \n",
        "\n",
        "Addidtional Resources:\n",
        "\n",
        "- [Yolov5-OBB installation instructions](https://github.com/hukaixuan19970627/yolov5_obb/blob/master/docs/install.md)\n",
        "- [Getting started](https://github.com/hukaixuan19970627/yolov5_obb/blob/master/docs/GetStart.md)"
      ],
      "metadata": {
        "id": "STfdGh5Ui6Tn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip3 install torch==1.10.1+cu113 torchvision==0.11.2+cu113 torchaudio==0.10.1+cu113 -f https://download.pytorch.org/whl/cu113/torch_stable.html\n",
        "!nvcc -V\n",
        "!nvidia-smi"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1NZxhXTMWvek",
        "outputId": "69470765-d772-45f9-d1a3-ee44ffeb7663"
      },
      "execution_count": null,
      "outputs": [
        {
          "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://download.pytorch.org/whl/cu113/torch_stable.html\n",
            "Collecting torch==1.10.1+cu113\n",
            "  Downloading https://download.pytorch.org/whl/cu113/torch-1.10.1%2Bcu113-cp38-cp38-linux_x86_64.whl (1821.4 MB)\n",
            "\u001b[2K     \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m \u001b[32m1.8/1.8 GB\u001b[0m \u001b[31m100.3 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0mtcmalloc: large alloc 1821442048 bytes == 0x36a2000 @  0x7f1343e271e7 0x4d30a0 0x4d312c 0x5d6f4c 0x51edd1 0x51ef5b 0x4f750a 0x4997a2 0x55cd91 0x5d8941 0x4997a2 0x55cd91 0x5d8941 0x4997a2 0x55cd91 0x5d8941 0x4997a2 0x55cd91 0x5d8941 0x4997a2 0x55cd91 0x5d8941 0x4997a2 0x5d8868 0x4997a2 0x55cd91 0x5d8941 0x49abe4 0x55cd91 0x5d8941 0x4997a2\n",
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            "tcmalloc: large alloc 1821442048 bytes == 0x36a2000 @  0x7f1343e271e7 0x4d30a0 0x5dede2 0x6758aa 0x4f750a 0x4997a2 0x55cd91 0x5d8941 0x4997a2 0x55cd91 0x5d8941 0x4fe318 0x5da092 0x62042c 0x5d8d8c 0x561f80 0x4fd2db 0x4997c7 0x4fd8b5 0x4997c7 0x4fd8b5 0x49abe4 0x4f5fe9 0x55e146 0x4f5fe9 0x55e146 0x4f5fe9 0x55e146 0x5d8868 0x5da092 0x587116\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.8/1.8 GB\u001b[0m \u001b[31m935.5 kB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting torchvision==0.11.2+cu113\n",
            "  Downloading https://download.pytorch.org/whl/cu113/torchvision-0.11.2%2Bcu113-cp38-cp38-linux_x86_64.whl (24.6 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m24.6/24.6 MB\u001b[0m \u001b[31m54.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting torchaudio==0.10.1+cu113\n",
            "  Downloading https://download.pytorch.org/whl/cu113/torchaudio-0.10.1%2Bcu113-cp38-cp38-linux_x86_64.whl (2.9 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.9/2.9 MB\u001b[0m \u001b[31m41.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: typing-extensions in /usr/local/lib/python3.8/dist-packages (from torch==1.10.1+cu113) (4.4.0)\n",
            "Requirement already satisfied: pillow!=8.3.0,>=5.3.0 in /usr/local/lib/python3.8/dist-packages (from torchvision==0.11.2+cu113) (7.1.2)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.8/dist-packages (from torchvision==0.11.2+cu113) (1.21.6)\n",
            "Installing collected packages: torch, torchvision, torchaudio\n",
            "  Attempting uninstall: torch\n",
            "    Found existing installation: torch 1.13.0+cu116\n",
            "    Uninstalling torch-1.13.0+cu116:\n",
            "      Successfully uninstalled torch-1.13.0+cu116\n",
            "  Attempting uninstall: torchvision\n",
            "    Found existing installation: torchvision 0.14.0+cu116\n",
            "    Uninstalling torchvision-0.14.0+cu116:\n",
            "      Successfully uninstalled torchvision-0.14.0+cu116\n",
            "  Attempting uninstall: torchaudio\n",
            "    Found existing installation: torchaudio 0.13.0+cu116\n",
            "    Uninstalling torchaudio-0.13.0+cu116:\n",
            "      Successfully uninstalled torchaudio-0.13.0+cu116\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",
            "torchtext 0.14.0 requires torch==1.13.0, but you have torch 1.10.1+cu113 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0mSuccessfully installed torch-1.10.1+cu113 torchaudio-0.10.1+cu113 torchvision-0.11.2+cu113\n",
            "nvcc: NVIDIA (R) Cuda compiler driver\n",
            "Copyright (c) 2005-2021 NVIDIA Corporation\n",
            "Built on Sun_Feb_14_21:12:58_PST_2021\n",
            "Cuda compilation tools, release 11.2, V11.2.152\n",
            "Build cuda_11.2.r11.2/compiler.29618528_0\n",
            "Tue Jan 17 03:21:20 2023       \n",
            "+-----------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 460.32.03    Driver Version: 460.32.03    CUDA Version: 11.2     |\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  Tesla T4            Off  | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   38C    P8     9W /  70W |      0MiB / 15109MiB |      0%      Default |\n",
            "|                               |                      |                  N/A |\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": [
        "!git clone git@github.com:Suppersine/YOLOv7_obb_KFIOU.git\n",
        "#!git clone https://github.com/SSTato/YOLOv7_obb.git"
      ],
      "metadata": {
        "id": "9cWpOA-iLBdJ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7e77a653-4371-45f5-8848-0a7bc1da8690"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'YOLOv7_obb'...\n",
            "remote: Enumerating objects: 224, done.\u001b[K\n",
            "remote: Counting objects: 100% (224/224), done.\u001b[K\n",
            "remote: Compressing objects: 100% (188/188), done.\u001b[K\n",
            "remote: Total 224 (delta 41), reused 173 (delta 19), pack-reused 0\u001b[K\n",
            "Receiving objects: 100% (224/224), 19.16 MiB | 16.87 MiB/s, done.\n",
            "Resolving deltas: 100% (41/41), done.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#%cd /content/yolov7_obb_expm/\n",
        "%cd /content/YOLOv7_obb_KFIOU/\n",
        "!pip install -r requirements.txt"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XrMDF3vubJlt",
        "outputId": "4b7d6529-86f5-4798-e362-d7826eeba68d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/YOLOv7_obb\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/python3.8/dist-packages (from -r requirements.txt (line 4)) (3.2.2)\n",
            "Requirement already satisfied: numpy>=1.18.5 in /usr/local/lib/python3.8/dist-packages (from -r requirements.txt (line 5)) (1.21.6)\n",
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            "Requirement already satisfied: Pillow>=7.1.2 in /usr/local/lib/python3.8/dist-packages (from -r requirements.txt (line 7)) (7.1.2)\n",
            "Requirement already satisfied: PyYAML>=5.3.1 in /usr/local/lib/python3.8/dist-packages (from -r requirements.txt (line 8)) (6.0)\n",
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            "Requirement already satisfied: scipy>=1.4.1 in /usr/local/lib/python3.8/dist-packages (from -r requirements.txt (line 10)) (1.7.3)\n",
            "Requirement already satisfied: torch>=1.7.0 in /usr/local/lib/python3.8/dist-packages (from -r requirements.txt (line 11)) (1.10.1+cu113)\n",
            "Requirement already satisfied: torchvision>=0.8.1 in /usr/local/lib/python3.8/dist-packages (from -r requirements.txt (line 12)) (0.11.2+cu113)\n",
            "Requirement already satisfied: tqdm>=4.41.0 in /usr/local/lib/python3.8/dist-packages (from -r requirements.txt (line 13)) (4.64.1)\n",
            "Requirement already satisfied: tensorboard>=2.4.1 in /usr/local/lib/python3.8/dist-packages (from -r requirements.txt (line 16)) (2.9.1)\n",
            "Requirement already satisfied: pandas>=1.1.4 in /usr/local/lib/python3.8/dist-packages (from -r requirements.txt (line 20)) (1.3.5)\n",
            "Requirement already satisfied: seaborn>=0.11.0 in /usr/local/lib/python3.8/dist-packages (from -r requirements.txt (line 21)) (0.11.2)\n",
            "Collecting thop\n",
            "  Downloading thop-0.1.1.post2209072238-py3-none-any.whl (15 kB)\n",
            "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.8/dist-packages (from matplotlib>=3.2.2->-r requirements.txt (line 4)) (2.8.2)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.8/dist-packages (from matplotlib>=3.2.2->-r requirements.txt (line 4)) (0.11.0)\n",
            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.8/dist-packages (from matplotlib>=3.2.2->-r requirements.txt (line 4)) (3.0.9)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.8/dist-packages (from matplotlib>=3.2.2->-r requirements.txt (line 4)) (1.4.4)\n",
            "Requirement already satisfied: chardet<5,>=3.0.2 in /usr/local/lib/python3.8/dist-packages (from requests>=2.23.0->-r requirements.txt (line 9)) (4.0.0)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.8/dist-packages (from requests>=2.23.0->-r requirements.txt (line 9)) (2022.12.7)\n",
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            "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.8/dist-packages (from requests>=2.23.0->-r requirements.txt (line 9)) (1.24.3)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.8/dist-packages (from torch>=1.7.0->-r requirements.txt (line 11)) (4.4.0)\n",
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            "Requirement already satisfied: absl-py>=0.4 in /usr/local/lib/python3.8/dist-packages (from tensorboard>=2.4.1->-r requirements.txt (line 16)) (1.3.0)\n",
            "Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.8/dist-packages (from tensorboard>=2.4.1->-r requirements.txt (line 16)) (3.4.1)\n",
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            "Requirement already satisfied: grpcio>=1.24.3 in /usr/local/lib/python3.8/dist-packages (from tensorboard>=2.4.1->-r requirements.txt (line 16)) (1.51.1)\n",
            "Requirement already satisfied: tensorboard-data-server<0.7.0,>=0.6.0 in /usr/local/lib/python3.8/dist-packages (from tensorboard>=2.4.1->-r requirements.txt (line 16)) (0.6.1)\n",
            "Requirement already satisfied: google-auth-oauthlib<0.5,>=0.4.1 in /usr/local/lib/python3.8/dist-packages (from tensorboard>=2.4.1->-r requirements.txt (line 16)) (0.4.6)\n",
            "Requirement already satisfied: setuptools>=41.0.0 in /usr/local/lib/python3.8/dist-packages (from tensorboard>=2.4.1->-r requirements.txt (line 16)) (57.4.0)\n",
            "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.8/dist-packages (from pandas>=1.1.4->-r requirements.txt (line 20)) (2022.7)\n",
            "Requirement already satisfied: six>=1.9.0 in /usr/local/lib/python3.8/dist-packages (from google-auth<3,>=1.6.3->tensorboard>=2.4.1->-r requirements.txt (line 16)) (1.15.0)\n",
            "Requirement already satisfied: cachetools<6.0,>=2.0.0 in /usr/local/lib/python3.8/dist-packages (from google-auth<3,>=1.6.3->tensorboard>=2.4.1->-r requirements.txt (line 16)) (5.2.0)\n",
            "Requirement already satisfied: rsa<5,>=3.1.4 in /usr/local/lib/python3.8/dist-packages (from google-auth<3,>=1.6.3->tensorboard>=2.4.1->-r requirements.txt (line 16)) (4.9)\n",
            "Requirement already satisfied: pyasn1-modules>=0.2.1 in /usr/local/lib/python3.8/dist-packages (from google-auth<3,>=1.6.3->tensorboard>=2.4.1->-r requirements.txt (line 16)) (0.2.8)\n",
            "Requirement already satisfied: requests-oauthlib>=0.7.0 in /usr/local/lib/python3.8/dist-packages (from google-auth-oauthlib<0.5,>=0.4.1->tensorboard>=2.4.1->-r requirements.txt (line 16)) (1.3.1)\n",
            "Requirement already satisfied: importlib-metadata>=4.4 in /usr/local/lib/python3.8/dist-packages (from markdown>=2.6.8->tensorboard>=2.4.1->-r requirements.txt (line 16)) (6.0.0)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.8/dist-packages (from importlib-metadata>=4.4->markdown>=2.6.8->tensorboard>=2.4.1->-r requirements.txt (line 16)) (3.11.0)\n",
            "Requirement already satisfied: pyasn1<0.5.0,>=0.4.6 in /usr/local/lib/python3.8/dist-packages (from pyasn1-modules>=0.2.1->google-auth<3,>=1.6.3->tensorboard>=2.4.1->-r requirements.txt (line 16)) (0.4.8)\n",
            "Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.8/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard>=2.4.1->-r requirements.txt (line 16)) (3.2.2)\n",
            "Installing collected packages: thop\n",
            "Successfully installed thop-0.1.1.post2209072238\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%cd utils/nms_rotated\n",
        "!python setup.py develop  #or \"pip install -v -e .\""
      ],
      "metadata": {
        "id": "11G939jgNaVK",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1516081a-a8d1-411f-e509-e92213e9b121"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/YOLOv7_obb/utils/nms_rotated\n",
            "running develop\n",
            "running egg_info\n",
            "writing nms_rotated.egg-info/PKG-INFO\n",
            "writing dependency_links to nms_rotated.egg-info/dependency_links.txt\n",
            "writing top-level names to nms_rotated.egg-info/top_level.txt\n",
            "/usr/local/lib/python3.8/dist-packages/torch/utils/cpp_extension.py:381: UserWarning: Attempted to use ninja as the BuildExtension backend but we could not find ninja.. Falling back to using the slow distutils backend.\n",
            "  warnings.warn(msg.format('we could not find ninja.'))\n",
            "writing manifest file 'nms_rotated.egg-info/SOURCES.txt'\n",
            "running build_ext\n",
            "/usr/local/lib/python3.8/dist-packages/torch/utils/cpp_extension.py:782: UserWarning: The detected CUDA version (11.2) has a minor version mismatch with the version that was used to compile PyTorch (11.3). Most likely this shouldn't be a problem.\n",
            "  warnings.warn(CUDA_MISMATCH_WARN.format(cuda_str_version, torch.version.cuda))\n",
            "building '.nms_rotated_ext' extension\n",
            "creating build/temp.linux-x86_64-3.8\n",
            "creating build/temp.linux-x86_64-3.8/src\n",
            "x86_64-linux-gnu-gcc -pthread -Wno-unused-result -Wsign-compare -DNDEBUG -g -fwrapv -O2 -Wall -g -fstack-protector-strong -Wformat -Werror=format-security -g -fwrapv -O2 -g -fstack-protector-strong -Wformat -Werror=format-security -Wdate-time -D_FORTIFY_SOURCE=2 -fPIC -DWITH_CUDA -I/usr/local/lib/python3.8/dist-packages/torch/include -I/usr/local/lib/python3.8/dist-packages/torch/include/torch/csrc/api/include -I/usr/local/lib/python3.8/dist-packages/torch/include/TH -I/usr/local/lib/python3.8/dist-packages/torch/include/THC -I/usr/local/cuda/include -I/usr/include/python3.8 -c src/nms_rotated_cpu.cpp -o build/temp.linux-x86_64-3.8/src/nms_rotated_cpu.o -DTORCH_API_INCLUDE_EXTENSION_H -DPYBIND11_COMPILER_TYPE=\"_gcc\" -DPYBIND11_STDLIB=\"_libstdcpp\" -DPYBIND11_BUILD_ABI=\"_cxxabi1011\" -DTORCH_EXTENSION_NAME=nms_rotated_ext -D_GLIBCXX_USE_CXX11_ABI=0 -std=c++14\n",
            "x86_64-linux-gnu-gcc -pthread -Wno-unused-result -Wsign-compare -DNDEBUG -g -fwrapv -O2 -Wall -g -fstack-protector-strong -Wformat -Werror=format-security -g -fwrapv -O2 -g -fstack-protector-strong -Wformat -Werror=format-security -Wdate-time -D_FORTIFY_SOURCE=2 -fPIC -DWITH_CUDA -I/usr/local/lib/python3.8/dist-packages/torch/include -I/usr/local/lib/python3.8/dist-packages/torch/include/torch/csrc/api/include -I/usr/local/lib/python3.8/dist-packages/torch/include/TH -I/usr/local/lib/python3.8/dist-packages/torch/include/THC -I/usr/local/cuda/include -I/usr/include/python3.8 -c src/nms_rotated_ext.cpp -o build/temp.linux-x86_64-3.8/src/nms_rotated_ext.o -DTORCH_API_INCLUDE_EXTENSION_H -DPYBIND11_COMPILER_TYPE=\"_gcc\" -DPYBIND11_STDLIB=\"_libstdcpp\" -DPYBIND11_BUILD_ABI=\"_cxxabi1011\" -DTORCH_EXTENSION_NAME=nms_rotated_ext -D_GLIBCXX_USE_CXX11_ABI=0 -std=c++14\n",
            "/usr/local/cuda/bin/nvcc -DWITH_CUDA -I/usr/local/lib/python3.8/dist-packages/torch/include -I/usr/local/lib/python3.8/dist-packages/torch/include/torch/csrc/api/include -I/usr/local/lib/python3.8/dist-packages/torch/include/TH -I/usr/local/lib/python3.8/dist-packages/torch/include/THC -I/usr/local/cuda/include -I/usr/include/python3.8 -c src/nms_rotated_cuda.cu -o build/temp.linux-x86_64-3.8/src/nms_rotated_cuda.o -D__CUDA_NO_HALF_OPERATORS__ -D__CUDA_NO_HALF_CONVERSIONS__ -D__CUDA_NO_BFLOAT16_CONVERSIONS__ -D__CUDA_NO_HALF2_OPERATORS__ --expt-relaxed-constexpr --compiler-options '-fPIC' -D__CUDA_NO_HALF_OPERATORS__ -D__CUDA_NO_HALF_CONVERSIONS__ -D__CUDA_NO_HALF2_OPERATORS__ -DTORCH_API_INCLUDE_EXTENSION_H -DPYBIND11_COMPILER_TYPE=\"_gcc\" -DPYBIND11_STDLIB=\"_libstdcpp\" -DPYBIND11_BUILD_ABI=\"_cxxabi1011\" -DTORCH_EXTENSION_NAME=nms_rotated_ext -D_GLIBCXX_USE_CXX11_ABI=0 -gencode=arch=compute_75,code=compute_75 -gencode=arch=compute_75,code=sm_75 -std=c++14\n",
            "/usr/local/cuda/bin/nvcc -DWITH_CUDA -I/usr/local/lib/python3.8/dist-packages/torch/include -I/usr/local/lib/python3.8/dist-packages/torch/include/torch/csrc/api/include -I/usr/local/lib/python3.8/dist-packages/torch/include/TH -I/usr/local/lib/python3.8/dist-packages/torch/include/THC -I/usr/local/cuda/include -I/usr/include/python3.8 -c src/poly_nms_cuda.cu -o build/temp.linux-x86_64-3.8/src/poly_nms_cuda.o -D__CUDA_NO_HALF_OPERATORS__ -D__CUDA_NO_HALF_CONVERSIONS__ -D__CUDA_NO_BFLOAT16_CONVERSIONS__ -D__CUDA_NO_HALF2_OPERATORS__ --expt-relaxed-constexpr --compiler-options '-fPIC' -D__CUDA_NO_HALF_OPERATORS__ -D__CUDA_NO_HALF_CONVERSIONS__ -D__CUDA_NO_HALF2_OPERATORS__ -DTORCH_API_INCLUDE_EXTENSION_H -DPYBIND11_COMPILER_TYPE=\"_gcc\" -DPYBIND11_STDLIB=\"_libstdcpp\" -DPYBIND11_BUILD_ABI=\"_cxxabi1011\" -DTORCH_EXTENSION_NAME=nms_rotated_ext -D_GLIBCXX_USE_CXX11_ABI=0 -gencode=arch=compute_75,code=compute_75 -gencode=arch=compute_75,code=sm_75 -std=c++14\n",
            "creating build/lib.linux-x86_64-3.8\n",
            "x86_64-linux-gnu-g++ -pthread -shared -Wl,-O1 -Wl,-Bsymbolic-functions -Wl,-Bsymbolic-functions -g -fwrapv -O2 -Wl,-Bsymbolic-functions -g -fwrapv -O2 -g -fstack-protector-strong -Wformat -Werror=format-security -Wdate-time -D_FORTIFY_SOURCE=2 build/temp.linux-x86_64-3.8/src/nms_rotated_cpu.o build/temp.linux-x86_64-3.8/src/nms_rotated_ext.o build/temp.linux-x86_64-3.8/src/nms_rotated_cuda.o build/temp.linux-x86_64-3.8/src/poly_nms_cuda.o -L/usr/local/lib/python3.8/dist-packages/torch/lib -L/usr/local/cuda/lib64 -lc10 -ltorch -ltorch_cpu -ltorch_python -lcudart -lc10_cuda -ltorch_cuda_cu -ltorch_cuda_cpp -o build/lib.linux-x86_64-3.8/nms_rotated_ext.cpython-38-x86_64-linux-gnu.so\n",
            "copying build/lib.linux-x86_64-3.8/nms_rotated_ext.cpython-38-x86_64-linux-gnu.so -> \n",
            "Creating /usr/local/lib/python3.8/dist-packages/nms-rotated.egg-link (link to .)\n",
            "Adding nms-rotated 0.0.0 to easy-install.pth file\n",
            "\n",
            "Installed /content/YOLOv7_obb/utils/nms_rotated\n",
            "Processing dependencies for nms-rotated==0.0.0\n",
            "Finished processing dependencies for nms-rotated==0.0.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%cd /content\n",
        "!mkdir /content/datasets/"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "49QzmYLIMK4r",
        "outputId": "5186559c-9e6a-4f34-c01a-aaaba8da49dc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Export\n",
        "\n",
        "Remember to export your annotated dataset following the steps shown [in the video tutorial](https://youtu.be/iRkCNo9-slY?t=136)"
      ],
      "metadata": {
        "id": "HGf0HWV2Lgns"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%cd /content/datasets/\n",
        "\n",
        "!pip install roboflow\n",
        "\n",
        "from roboflow import Roboflow\n",
        "rf = Roboflow(api_key=\"fT5oyESQpvY2ntrSyCCW\")\n",
        "project = rf.workspace(\"gdit\").project(\"aerial-airport\")\n",
        "dataset = project.version(1).download(\"yolov5-obb\")"
      ],
      "metadata": {
        "id": "0wdoHhGMX79L",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "9ede6698-bd92-4bf4-9818-2152590ce652"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/datasets\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting roboflow\n",
            "  Downloading roboflow-0.2.25-py3-none-any.whl (46 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m46.5/46.5 KB\u001b[0m \u001b[31m3.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: certifi==2022.12.7 in /usr/local/lib/python3.8/dist-packages (from roboflow) (2022.12.7)\n",
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            "Collecting cycler==0.10.0\n",
            "  Downloading cycler-0.10.0-py2.py3-none-any.whl (6.5 kB)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.8/dist-packages (from roboflow) (1.15.0)\n",
            "Collecting pyparsing==2.4.7\n",
            "  Downloading pyparsing-2.4.7-py2.py3-none-any.whl (67 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m67.8/67.8 KB\u001b[0m \u001b[31m9.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: python-dateutil in /usr/local/lib/python3.8/dist-packages (from roboflow) (2.8.2)\n",
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            "Collecting python-dotenv\n",
            "  Downloading python_dotenv-0.21.0-py3-none-any.whl (18 kB)\n",
            "Requirement already satisfied: kiwisolver>=1.3.1 in /usr/local/lib/python3.8/dist-packages (from roboflow) (1.4.4)\n",
            "Collecting wget\n",
            "  Downloading wget-3.2.zip (10 kB)\n",
            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.8/dist-packages (from roboflow) (3.2.2)\n",
            "Requirement already satisfied: numpy>=1.18.5 in /usr/local/lib/python3.8/dist-packages (from roboflow) (1.21.6)\n",
            "Collecting urllib3==1.26.6\n",
            "  Downloading urllib3-1.26.6-py2.py3-none-any.whl (138 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m138.5/138.5 KB\u001b[0m \u001b[31m15.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: idna==2.10 in /usr/local/lib/python3.8/dist-packages (from roboflow) (2.10)\n",
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            "Requirement already satisfied: opencv-python-headless>=4.5.1.48 in /usr/local/lib/python3.8/dist-packages (from roboflow) (4.7.0.68)\n",
            "Collecting requests-toolbelt\n",
            "  Downloading requests_toolbelt-0.10.1-py2.py3-none-any.whl (54 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m54.5/54.5 KB\u001b[0m \u001b[31m6.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: requests in /usr/local/lib/python3.8/dist-packages (from roboflow) (2.25.1)\n",
            "Building wheels for collected packages: wget\n",
            "  Building wheel for wget (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for wget: filename=wget-3.2-py3-none-any.whl size=9674 sha256=f707e679cc6c8e74bfe7193bb702478ada3fb6eda09ad1688b91f2ba4ad219e4\n",
            "  Stored in directory: /root/.cache/pip/wheels/bd/a8/c3/3cf2c14a1837a4e04bd98631724e81f33f462d86a1d895fae0\n",
            "Successfully built wget\n",
            "Installing collected packages: wget, urllib3, python-dotenv, pyparsing, cycler, requests-toolbelt, roboflow\n",
            "  Attempting uninstall: urllib3\n",
            "    Found existing installation: urllib3 1.24.3\n",
            "    Uninstalling urllib3-1.24.3:\n",
            "      Successfully uninstalled urllib3-1.24.3\n",
            "  Attempting uninstall: pyparsing\n",
            "    Found existing installation: pyparsing 3.0.9\n",
            "    Uninstalling pyparsing-3.0.9:\n",
            "      Successfully uninstalled pyparsing-3.0.9\n",
            "  Attempting uninstall: cycler\n",
            "    Found existing installation: cycler 0.11.0\n",
            "    Uninstalling cycler-0.11.0:\n",
            "      Successfully uninstalled cycler-0.11.0\n",
            "Successfully installed cycler-0.10.0 pyparsing-2.4.7 python-dotenv-0.21.0 requests-toolbelt-0.10.1 roboflow-0.2.25 urllib3-1.26.6 wget-3.2\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "cycler",
                  "pyparsing"
                ]
              }
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "loading Roboflow workspace...\n",
            "loading Roboflow project...\n",
            "Downloading Dataset Version Zip in Aerial-Airport-1 to yolov5-obb: 100% [45214707 / 45214707] bytes\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Extracting Dataset Version Zip to Aerial-Airport-1 in yolov5-obb:: 100%|██████████| 1632/1632 [00:00<00:00, 1663.19it/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Rename dataset name to roboflow\n",
        "\n",
        "After running the `!pip install` package, our python.train will expect the dataset name to be `roboflow` (with or without changing the code path below). Make sure to change your cloned dataset folder name to `roboflow` before running the cell below. \n",
        "\n",
        "In other words, make sure your dataset structure is:\n",
        "\n",
        "```\n",
        "parent\n",
        "├── yolov5\n",
        "└── datasets\n",
        "    └── roboflow\n",
        "        ├── test\n",
        "        ├── train\n",
        "        └── valid\n",
        "            ├── images\n",
        "                 |────1.jpg\n",
        "                 |────...\n",
        "                 └────10000.jpg\n",
        "            ├── labelTxt\n",
        "                 |────1.txt\n",
        "                 |────...\n",
        "                 └────10000.txt\n",
        "        └────README.roboflow.txt\n",
        "        └────data.yaml\n",
        "```"
      ],
      "metadata": {
        "id": "cZJDvs5Bd59B"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Download trained weights\n",
        "# %cd /content/yolov7_obb_expm/\n",
        "%cd /content/YOLOv7_obb_KFIOU/\n",
        "!wget https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7.pt"
      ],
      "metadata": {
        "id": "EyQ-eqwe9Vaw",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "80069ce4-26a5-4d73-8ad4-c4ff80adaa98"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/YOLOv7_obb\n",
            "--2023-01-17 03:23:29--  https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7.pt\n",
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            "Saving to: ‘yolov7.pt’\n",
            "\n",
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            "\n",
            "2023-01-17 03:23:30 (248 MB/s) - ‘yolov7.pt’ saved [75587165/75587165]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Before training the model, always rename the dataset to \"roboflow\", and point its address to:\n",
        "\n",
        "```\n",
        "/content/datasets/roboflow/data.yaml\n",
        "```\n",
        "\n"
      ],
      "metadata": {
        "id": "XZd6BBwHOCME"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# os.rename\n",
        "import os  \n",
        "%cd /content/datasets/\n",
        "os.rename(\"/content/datasets/Aerial-Airport-1\", \"/content/datasets/roboflow\")"
      ],
      "metadata": {
        "id": "uz2QPN9D2OqM",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8a7d9a9e-9ca7-446f-860a-987f36b44ec0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/datasets\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Commence Training the Model\n",
        "# %cd /content/yolov7_obb_expm/\n",
        "%cd /content/YOLOv7_obb_KFIOU/\n",
        "!python train.py --data /content/datasets/roboflow/data.yaml --weights yolov7.pt --epochs 10 --batch-size 5 --img 1024 --device 0 --exist-ok"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jXOF90MZeC11",
        "outputId": "4734f9e0-adb0-4821-edbc-3eabaeb21dbc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/YOLOv7_obb\n",
            "\u001b[34m\u001b[1mtrain: \u001b[0mweights=yolov7.pt, cfg=models/yolov7.yaml, data=/content/datasets/roboflow/data.yaml, hyp=hyp.scratch.obb.yaml, epochs=10, batch_size=5, imgsz=1024, rect=False, resume=None, nosave=False, noval=False, noautoanchor=False, evolve=None, bucket=, cache=None, image_weights=False, device=0, multi_scale=False, single_cls=True, optimizer=SGD, sync_bn=False, workers=8, project=runs/train, name=exp, exist_ok=True, quad=False, cos_lr=False, label_smoothing=0.0, patience=100, freeze=[0], save_period=-1, local_rank=-1, entity=None, upload_dataset=False, bbox_interval=-1, artifact_alias=latest\n",
            "\u001b[34m\u001b[1mgithub: \u001b[0mup to date with https://github.com/SSTato/YOLOv7_obb ✅\n",
            "YOLOv5 🚀 1.0.0-14-g1943d9b torch 1.10.1+cu113 CUDA:0 (Tesla T4, 15110MiB)\n",
            "\n",
            "\u001b[34m\u001b[1mhyperparameters: \u001b[0mlr0=0.01, lrf=0.1, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=0.05, cls=0.3, cls_pw=1.0, theta=0.5, theta_pw=1.0, obj=0.7, 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=180.0, translate=0.2, scale=0.9, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.15, copy_paste=0.0, paste_in=0, cls_theta=180, csl_radius=2.0, loss_ota=0\n",
            "\u001b[34m\u001b[1mWeights & Biases: \u001b[0mrun 'pip install wandb' to automatically track and visualize YOLOv5 🚀 runs (RECOMMENDED)\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       928  models.common.Conv                      [3, 32, 3, 1]                 \n",
            "  1                -1  1     18560  models.common.Conv                      [32, 64, 3, 2]                \n",
            "  2                -1  1     36992  models.common.Conv                      [64, 64, 3, 1]                \n",
            "  3                -1  1     73984  models.common.Conv                      [64, 128, 3, 2]               \n",
            "  4                -1  1      8320  models.common.Conv                      [128, 64, 1, 1]               \n",
            "  5                -2  1      8320  models.common.Conv                      [128, 64, 1, 1]               \n",
            "  6                -1  1     36992  models.common.Conv                      [64, 64, 3, 1]                \n",
            "  7                -1  1     36992  models.common.Conv                      [64, 64, 3, 1]                \n",
            "  8                -1  1     36992  models.common.Conv                      [64, 64, 3, 1]                \n",
            "  9                -1  1     36992  models.common.Conv                      [64, 64, 3, 1]                \n",
            " 10  [-1, -3, -5, -6]  1         0  models.common.Concat                    [1]                           \n",
            " 11                -1  1     66048  models.common.Conv                      [256, 256, 1, 1]              \n",
            " 12                -1  1         0  models.common.MP                        []                            \n",
            " 13                -1  1     33024  models.common.Conv                      [256, 128, 1, 1]              \n",
            " 14                -3  1     33024  models.common.Conv                      [256, 128, 1, 1]              \n",
            " 15                -1  1    147712  models.common.Conv                      [128, 128, 3, 2]              \n",
            " 16          [-1, -3]  1         0  models.common.Concat                    [1]                           \n",
            " 17                -1  1     33024  models.common.Conv                      [256, 128, 1, 1]              \n",
            " 18                -2  1     33024  models.common.Conv                      [256, 128, 1, 1]              \n",
            " 19                -1  1    147712  models.common.Conv                      [128, 128, 3, 1]              \n",
            " 20                -1  1    147712  models.common.Conv                      [128, 128, 3, 1]              \n",
            " 21                -1  1    147712  models.common.Conv                      [128, 128, 3, 1]              \n",
            " 22                -1  1    147712  models.common.Conv                      [128, 128, 3, 1]              \n",
            " 23  [-1, -3, -5, -6]  1         0  models.common.Concat                    [1]                           \n",
            " 24                -1  1    263168  models.common.Conv                      [512, 512, 1, 1]              \n",
            " 25                -1  1         0  models.common.MP                        []                            \n",
            " 26                -1  1    131584  models.common.Conv                      [512, 256, 1, 1]              \n",
            " 27                -3  1    131584  models.common.Conv                      [512, 256, 1, 1]              \n",
            " 28                -1  1    590336  models.common.Conv                      [256, 256, 3, 2]              \n",
            " 29          [-1, -3]  1         0  models.common.Concat                    [1]                           \n",
            " 30                -1  1    131584  models.common.Conv                      [512, 256, 1, 1]              \n",
            " 31                -2  1    131584  models.common.Conv                      [512, 256, 1, 1]              \n",
            " 32                -1  1    590336  models.common.Conv                      [256, 256, 3, 1]              \n",
            " 33                -1  1    590336  models.common.Conv                      [256, 256, 3, 1]              \n",
            " 34                -1  1    590336  models.common.Conv                      [256, 256, 3, 1]              \n",
            " 35                -1  1    590336  models.common.Conv                      [256, 256, 3, 1]              \n",
            " 36  [-1, -3, -5, -6]  1         0  models.common.Concat                    [1]                           \n",
            " 37                -1  1   1050624  models.common.Conv                      [1024, 1024, 1, 1]            \n",
            " 38                -1  1         0  models.common.MP                        []                            \n",
            " 39                -1  1    525312  models.common.Conv                      [1024, 512, 1, 1]             \n",
            " 40                -3  1    525312  models.common.Conv                      [1024, 512, 1, 1]             \n",
            " 41                -1  1   2360320  models.common.Conv                      [512, 512, 3, 2]              \n",
            " 42          [-1, -3]  1         0  models.common.Concat                    [1]                           \n",
            " 43                -1  1    262656  models.common.Conv                      [1024, 256, 1, 1]             \n",
            " 44                -2  1    262656  models.common.Conv                      [1024, 256, 1, 1]             \n",
            " 45                -1  1    590336  models.common.Conv                      [256, 256, 3, 1]              \n",
            " 46                -1  1    590336  models.common.Conv                      [256, 256, 3, 1]              \n",
            " 47                -1  1    590336  models.common.Conv                      [256, 256, 3, 1]              \n",
            " 48                -1  1    590336  models.common.Conv                      [256, 256, 3, 1]              \n",
            " 49  [-1, -3, -5, -6]  1         0  models.common.Concat                    [1]                           \n",
            " 50                -1  1   1050624  models.common.Conv                      [1024, 1024, 1, 1]            \n",
            " 51                -1  1   7609344  models.common.SPPCSPC                   [1024, 512, 1]                \n",
            " 52                -1  1    131584  models.common.Conv                      [512, 256, 1, 1]              \n",
            " 53                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
            " 54                37  1    262656  models.common.Conv                      [1024, 256, 1, 1]             \n",
            " 55          [-1, -2]  1         0  models.common.Concat                    [1]                           \n",
            " 56                -1  1    131584  models.common.Conv                      [512, 256, 1, 1]              \n",
            " 57                -2  1    131584  models.common.Conv                      [512, 256, 1, 1]              \n",
            " 58                -1  1    295168  models.common.Conv                      [256, 128, 3, 1]              \n",
            " 59                -1  1    147712  models.common.Conv                      [128, 128, 3, 1]              \n",
            " 60                -1  1    147712  models.common.Conv                      [128, 128, 3, 1]              \n",
            " 61                -1  1    147712  models.common.Conv                      [128, 128, 3, 1]              \n",
            " 62[-1, -2, -3, -4, -5, -6]  1         0  models.common.Concat                    [1]                           \n",
            " 63                -1  1    262656  models.common.Conv                      [1024, 256, 1, 1]             \n",
            " 64                -1  1     33024  models.common.Conv                      [256, 128, 1, 1]              \n",
            " 65                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
            " 66                24  1     65792  models.common.Conv                      [512, 128, 1, 1]              \n",
            " 67          [-1, -2]  1         0  models.common.Concat                    [1]                           \n",
            " 68                -1  1     33024  models.common.Conv                      [256, 128, 1, 1]              \n",
            " 69                -2  1     33024  models.common.Conv                      [256, 128, 1, 1]              \n",
            " 70                -1  1     73856  models.common.Conv                      [128, 64, 3, 1]               \n",
            " 71                -1  1     36992  models.common.Conv                      [64, 64, 3, 1]                \n",
            " 72                -1  1     36992  models.common.Conv                      [64, 64, 3, 1]                \n",
            " 73                -1  1     36992  models.common.Conv                      [64, 64, 3, 1]                \n",
            " 74[-1, -2, -3, -4, -5, -6]  1         0  models.common.Concat                    [1]                           \n",
            " 75                -1  1     65792  models.common.Conv                      [512, 128, 1, 1]              \n",
            " 76                -1  1         0  models.common.MP                        []                            \n",
            " 77                -1  1     16640  models.common.Conv                      [128, 128, 1, 1]              \n",
            " 78                -3  1     16640  models.common.Conv                      [128, 128, 1, 1]              \n",
            " 79                -1  1    147712  models.common.Conv                      [128, 128, 3, 2]              \n",
            " 80      [-1, -3, 63]  1         0  models.common.Concat                    [1]                           \n",
            " 81                -1  1    131584  models.common.Conv                      [512, 256, 1, 1]              \n",
            " 82                -2  1    131584  models.common.Conv                      [512, 256, 1, 1]              \n",
            " 83                -1  1    295168  models.common.Conv                      [256, 128, 3, 1]              \n",
            " 84                -1  1    147712  models.common.Conv                      [128, 128, 3, 1]              \n",
            " 85                -1  1    147712  models.common.Conv                      [128, 128, 3, 1]              \n",
            " 86                -1  1    147712  models.common.Conv                      [128, 128, 3, 1]              \n",
            " 87[-1, -2, -3, -4, -5, -6]  1         0  models.common.Concat                    [1]                           \n",
            " 88                -1  1    262656  models.common.Conv                      [1024, 256, 1, 1]             \n",
            " 89                -1  1         0  models.common.MP                        []                            \n",
            " 90                -1  1     66048  models.common.Conv                      [256, 256, 1, 1]              \n",
            " 91                -3  1     66048  models.common.Conv                      [256, 256, 1, 1]              \n",
            " 92                -1  1    590336  models.common.Conv                      [256, 256, 3, 2]              \n",
            " 93      [-1, -3, 51]  1         0  models.common.Concat                    [1]                           \n",
            " 94                -1  1    525312  models.common.Conv                      [1024, 512, 1, 1]             \n",
            " 95                -2  1    525312  models.common.Conv                      [1024, 512, 1, 1]             \n",
            " 96                -1  1   1180160  models.common.Conv                      [512, 256, 3, 1]              \n",
            " 97                -1  1    590336  models.common.Conv                      [256, 256, 3, 1]              \n",
            " 98                -1  1    590336  models.common.Conv                      [256, 256, 3, 1]              \n",
            " 99                -1  1    590336  models.common.Conv                      [256, 256, 3, 1]              \n",
            "100[-1, -2, -3, -4, -5, -6]  1         0  models.common.Concat                    [1]                           \n",
            "101                -1  1   1049600  models.common.Conv                      [2048, 512, 1, 1]             \n",
            "102                75  1    328704  models.common.RepConv                   [128, 256, 3, 1]              \n",
            "103                88  1   1312768  models.common.RepConv                   [256, 512, 3, 1]              \n",
            "104               101  1   5246976  models.common.RepConv                   [512, 1024, 3, 1]             \n",
            "105   [102, 103, 104]  1   1005076  models.yolo.IDetect                     [1, [[12, 16, 19, 36, 40, 28], [36, 75, 76, 55, 72, 146], [142, 110, 192, 243, 459, 401]], [256, 512, 1024]]\n",
            "/usr/local/lib/python3.8/dist-packages/torch/functional.py:445: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at  ../aten/src/ATen/native/TensorShape.cpp:2157.)\n",
            "  return _VF.meshgrid(tensors, **kwargs)  # type: ignore[attr-defined]\n",
            "Model Summary: 415 layers, 38167476 parameters, 38167476 gradients, 108.2 GFLOPs\n",
            "\n",
            "Transferred 552/566 items from yolov7.pt\n",
            "Scaled weight_decay = 0.0005078125\n",
            "\u001b[34m\u001b[1moptimizer:\u001b[0m SGD with parameter groups 92 weight, 95 weight (no decay), 95 bias\n",
            "\u001b[34m\u001b[1malbumentations: \u001b[0mBlur(always_apply=False, p=0.01, blur_limit=(3, 7)), MedianBlur(always_apply=False, p=0.01, blur_limit=(3, 7)), ToGray(always_apply=False, p=0.01), CLAHE(always_apply=False, p=0.01, clip_limit=(1, 4.0), tile_grid_size=(8, 8))\n",
            "\u001b[34m\u001b[1mtrain: \u001b[0mScanning '../datasets/roboflow/train/labelTxt.cache' images and labels... 708 found, 0 missing, 9 empty, 0 corrupted: 100% 708/708 [00:00<?, ?it/s]\n",
            "\u001b[34m\u001b[1mval: \u001b[0mScanning '../datasets/roboflow/valid/labelTxt.cache' images and labels... 68 found, 0 missing, 2 empty, 0 corrupted: 100% 68/68 [00:00<?, ?it/s]\n",
            "Plotting labels to runs/train/exp/labels_xyls.jpg... \n",
            "\n",
            "\u001b[34m\u001b[1mAutoAnchor: \u001b[0m5.51 anchors/target, 1.000 Best Possible Recall (BPR). Current anchors are a good fit to dataset ✅\n",
            "Image sizes 1024 train, 1024 val\n",
            "Using 2 dataloader workers\n",
            "Logging results to \u001b[1mruns/train/exp\u001b[0m\n",
            "Starting training for 10 epochs...\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     theta    labels  img_size\n",
            "       0/9     11.1G    0.1105      6.18         0     1.048        19      1024: 100% 142/142 [01:37<00:00,  1.46it/s]\n",
            "               Class     Images     Labels          P          R  HBBmAP@.5  HBBmAP@.5:.95: 100% 7/7 [02:15<00:00, 19.37s/it]\n",
            "                 all         68       1018    0.00332     0.0707    0.00171    0.00038\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls     theta    labels  img_size\n",
            "       1/9     13.6G    0.1103     5.038         0      1.05        56      1024: 100% 142/142 [01:35<00:00,  1.49it/s]\n",
            "               Class     Images     Labels          P          R  HBBmAP@.5  HBBmAP@.5:.95:  14% 1/7 [00:20<02:02, 20.45s/it]"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!python detect.py --weights 'runs/train/exp/weights/best.pt' --source '../datasets/roboflow/valid/images' --conf-thres 0.01 --img 416 --device 0 --agnostic"
      ],
      "metadata": {
        "id": "8yBGxIBJbL1W"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "%cd /content/yolov5_obb/runs/detect/exp/"
      ],
      "metadata": {
        "id": "D782nBcaL5er"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import os, random\n",
        "random_file = random.choice(os.listdir(\"/content/yolov5_obb/runs/detect/exp\"))\n",
        "\n",
        "from IPython.display import Image\n",
        "Image(random_file)"
      ],
      "metadata": {
        "id": "K6RlZpzxb_kE"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}