{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8IoA9kz36w3N",
        "outputId": "0f278eb1-9149-4209-87ab-21420c5bd78c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fri Sep 30 06:40:35 2022       \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   39C    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",
            "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/torch_stable.html\n",
            "Requirement already satisfied: torch==1.8.1+cu111 in /usr/local/lib/python3.7/dist-packages (1.8.1+cu111)\n",
            "Requirement already satisfied: torchvision==0.9.1+cu111 in /usr/local/lib/python3.7/dist-packages (0.9.1+cu111)\n",
            "Requirement already satisfied: torchaudio==0.8.1 in /usr/local/lib/python3.7/dist-packages (0.8.1)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from torch==1.8.1+cu111) (4.1.1)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from torch==1.8.1+cu111) (1.21.6)\n",
            "Requirement already satisfied: pillow>=4.1.1 in /usr/local/lib/python3.7/dist-packages (from torchvision==0.9.1+cu111) (7.1.2)\n"
          ]
        }
      ],
      "source": [
        "!nvidia-smi\n",
        "!pip install torch==1.8.1+cu111 torchvision==0.9.1+cu111 torchaudio==0.8.1 -f https://download.pytorch.org/whl/torch_stable.html"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "os.chdir('/content')\n",
        "!git clone https://github.com/xaiki/spoof-detect\n",
        "!git clone https://github.com/meituan/YOLOv6\n",
        "!wget -c https://github.com/meituan/YOLOv6/releases/download/0.1.0/yolov6s.pt\n",
        "!mkdir /content/YOLOv6/weights/\n",
        "!mv yolov6s.pt /content/YOLOv6/weights/"
      ],
      "metadata": {
        "id": "bQd8r4hv68cd",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8d7b1ba2-b10a-4a42-9e9f-c77bb4be4fdb"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "fatal: destination path 'spoof-detect' already exists and is not an empty directory.\n",
            "fatal: destination path 'YOLOv6' already exists and is not an empty directory.\n",
            "--2022-09-30 07:11:35--  https://github.com/meituan/YOLOv6/releases/download/0.1.0/yolov6s.pt\n",
            "Resolving github.com (github.com)... 20.205.243.166\n",
            "Connecting to github.com (github.com)|20.205.243.166|:443... connected.\n",
            "HTTP request sent, awaiting response... 302 Found\n",
            "Location: https://objects.githubusercontent.com/github-production-release-asset-2e65be/501076075/7af0d5c3-a8e5-456e-ae8c-29b7fc22753e?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAIWNJYAX4CSVEH53A%2F20220930%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20220930T071135Z&X-Amz-Expires=300&X-Amz-Signature=9b25436af1c1ca00c733f52dcb3dacb3606658be832729e4639f709023d1e2a5&X-Amz-SignedHeaders=host&actor_id=0&key_id=0&repo_id=501076075&response-content-disposition=attachment%3B%20filename%3Dyolov6s.pt&response-content-type=application%2Foctet-stream [following]\n",
            "--2022-09-30 07:11:36--  https://objects.githubusercontent.com/github-production-release-asset-2e65be/501076075/7af0d5c3-a8e5-456e-ae8c-29b7fc22753e?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAIWNJYAX4CSVEH53A%2F20220930%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20220930T071135Z&X-Amz-Expires=300&X-Amz-Signature=9b25436af1c1ca00c733f52dcb3dacb3606658be832729e4639f709023d1e2a5&X-Amz-SignedHeaders=host&actor_id=0&key_id=0&repo_id=501076075&response-content-disposition=attachment%3B%20filename%3Dyolov6s.pt&response-content-type=application%2Foctet-stream\n",
            "Resolving objects.githubusercontent.com (objects.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n",
            "Connecting to objects.githubusercontent.com (objects.githubusercontent.com)|185.199.108.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 416 Range Not Satisfiable\n",
            "\n",
            "    The file is already fully retrieved; nothing to do.\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import files\n",
        "uploaded = files.upload()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 56
        },
        "id": "dy-9m16jBF6t",
        "outputId": "3a8b6f9d-f885-4bbe-f112-2f155536086a"
      },
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-25404031-1291-4d7a-9d8e-f71c2c60467c\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-25404031-1291-4d7a-9d8e-f71c2c60467c\">\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> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "for d in ['/content/spoof-detect/python', '/content/YOLOv6']:\n",
        "  os.chdir(d) \n",
        "  print(os.system('pip install -r requirements.txt'))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "okjQpF7i7Q6f",
        "outputId": "e57e2b5e-a116-4c41-dafd-cd24e5c0fb9a"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "256\n",
            "0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pwd"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eC9UMx6C8N2h",
        "outputId": "fa1fdac9-8d5f-4505-de5f-820740ab7511"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/YOLOv6\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount(\"/content/gdrive\")"
      ],
      "metadata": {
        "id": "9Dw-WaObDRSK",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3aa91913-9f21-41d3-a17b-1dec73c26af2"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/gdrive; to attempt to forcibly remount, call drive.mount(\"/content/gdrive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!ls /content/gdrive/MyDrive/data.tar.gz\n",
        "!cd /content && tar xvzf /content/gdrive/MyDrive/data.tar.gz"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_NZCIqEPAMYO",
        "outputId": "f296adfa-8aab-4b5f-eed0-098843eec2f7"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n",
            "data/split/labels/train/00431.12.69-x0y6.txt\n",
            "data/split/labels/train/00431.12.69-x1y1.txt\n",
            "data/split/labels/train/00431.12.69-x1y2.txt\n",
            "data/split/labels/train/00431.12.69-x2y1.txt\n",
            "data/split/labels/train/00431.12.69-x2y3.txt\n",
            "data/split/labels/train/00431.12.69-x2y4.txt\n",
            "data/split/labels/train/00319.12.70-x0y1.txt\n",
            "data/split/labels/train/00319.12.70-x0y3.txt\n",
            "data/split/labels/train/00319.12.70-x0y7.txt\n",
            "data/split/labels/train/00321.13.3-x0y6.txt\n",
            "data/split/labels/train/00321.13.3-x0y7.txt\n",
            "data/split/labels/train/00321.13.3-x0y8.txt\n",
            "data/split/labels/train/00321.13.3-x1y6.txt\n",
            "data/split/labels/train/00321.13.3-x1y7.txt\n",
            "data/split/labels/train/00321.13.3-x1y8.txt\n",
            "data/split/labels/train/00321.13.3-x3y3.txt\n",
            "data/split/labels/train/00321.13.3-x3y7.txt\n",
            "data/split/labels/train/00321.13.3-x5y7.txt\n",
            "data/split/labels/train/00007.13.4-x0y3.txt\n",
            "data/split/labels/train/00007.13.4-x0y8.txt\n",
            "data/split/labels/train/00007.13.4-x0y9.txt\n",
            "data/split/labels/train/00007.13.4-x0y11.txt\n",
            "data/split/labels/train/00007.13.4-x0y12.txt\n",
            "data/split/labels/train/00007.13.4-x1y3.txt\n",
            "data/split/labels/train/00007.13.4-x1y7.txt\n",
            "data/split/labels/train/00007.13.4-x1y8.txt\n",
            "data/split/labels/train/00007.13.4-x1y9.txt\n",
            "data/split/labels/train/00007.13.4-x2y6.txt\n",
            "data/split/labels/train/00007.13.4-x2y7.txt\n",
            "data/split/labels/train/00007.13.4-x2y8.txt\n",
            "data/split/labels/train/00007.13.4-x2y9.txt\n",
            "data/split/labels/train/00330.14.56-x1y12.txt\n",
            "data/split/labels/train/00332.13.5-x2y7.txt\n",
            "data/split/labels/train/00014.13.6-x0y9.txt\n",
            "data/split/labels/train/00014.13.6-x0y10.txt\n",
            "data/split/labels/train/00014.13.6-x0y12.txt\n",
            "data/split/labels/train/00014.13.6-x1y9.txt\n",
            "data/split/labels/train/00339.12.53-x0y0.txt\n",
            "data/split/labels/train/00339.12.53-x0y1.txt\n",
            "data/split/labels/train/00339.12.53-x0y2.txt\n",
            "data/split/labels/train/00339.12.53-x0y3.txt\n",
            "data/split/labels/train/00339.12.53-x1y0.txt\n",
            "data/split/labels/train/00339.12.53-x1y1.txt\n",
            "data/split/labels/train/00339.12.53-x1y4.txt\n",
            "data/split/labels/train/00339.12.53-x2y0.txt\n",
            "data/split/labels/train/00339.12.53-x2y1.txt\n",
            "data/split/labels/train/00339.12.53-x2y4.txt\n",
            "data/split/labels/train/00339.12.53-x2y7.txt\n",
            "data/split/labels/train/44098.12.54-x0y4.txt\n",
            "data/split/labels/train/44098.12.54-x0y7.txt\n",
            "data/split/labels/train/44098.12.54-x0y9.txt\n",
            "data/split/labels/train/44098.12.54-x0y10.txt\n",
            "data/split/labels/train/44098.12.54-x1y0.txt\n",
            "data/split/labels/train/44098.12.54-x1y4.txt\n",
            "data/split/labels/train/44098.12.54-x1y7.txt\n",
            "data/split/labels/train/44098.12.54-x1y8.txt\n",
            "data/split/labels/train/44098.12.54-x1y9.txt\n",
            "data/split/labels/train/44098.12.54-x1y10.txt\n",
            "data/split/labels/train/44098.12.54-x2y0.txt\n",
            "data/split/labels/train/44098.12.54-x2y8.txt\n",
            "data/split/labels/train/44099.12.55-x0y2.txt\n",
            "data/split/labels/train/44099.12.55-x1y0.txt\n",
            "data/split/labels/train/44099.12.55-x1y4.txt\n",
            "data/split/labels/train/44099.12.55-x2y0.txt\n",
            "data/split/labels/train/44099.12.55-x2y2.txt\n",
            "data/split/labels/train/44099.12.55-x2y4.txt\n",
            "data/split/labels/train/00330.12.56-x0y1.txt\n",
            "data/split/labels/train/00330.12.56-x0y3.txt\n",
            "data/split/labels/train/00330.12.56-x1y1.txt\n",
            "data/split/labels/train/00330.12.56-x1y3.txt\n",
            "data/split/labels/train/00330.12.56-x1y4.txt\n",
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          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%%writefile /content/data/data.yaml\n",
        "# this file is autogenerated by write_data.py for YOLO version 6\n",
        "\n",
        "\n",
        "train: /content/data/split/images/train\n",
        "val:   /content/data/split/images/val\n",
        "test:  /content/data/split/images/test\n",
        "\n",
        "is_coco: False\n",
        "    \n",
        "\n",
        "nc: 55\n",
        "num_classes: 55\n",
        "names: ['BACS BANCO DE CREDITO Y SECURITIZAC', 'BANCO BBVA ARGENTINA S.A.', 'BANCO BICA S.A.', 'BANCO CETELEM ARGENTINA S.A.', 'BANCO CMF S.A.', 'BANCO COINAG S.A.', 'BANCO COLUMBIA S.A.', 'BANCO COMAFI SOCIEDAD ANONIMA', 'BANCO CREDICOOP COOPERATIVO LIMITAD', 'BANCO DE COMERCIO S.A.', 'BANCO DE CORRIENTES S.A.', 'BANCO DE FORMOSA S.A.', 'BANCO DE GALICIA Y BUENOS AIRES S.A', 'BANCO DE INVERSION Y COMERCIO EXTER', 'BANCO DE LA CIUDAD DE BUENOS AIRES', 'BANCO DE LA NACION ARGENTINA', 'BANCO DE LA PAMPA SOCIEDAD DE ECONO', 'BANCO DE LA PROVINCIA DE BUENOS AIR', 'BANCO DE LA PROVINCIA DE CORDOBA S.', 'BANCO DE SAN JUAN S.A.', 'BANCO DE SANTA CRUZ S.A.', 'BANCO DE SANTIAGO DEL ESTERO S.A.', 'BANCO DE SERVICIOS FINANCIEROS S.A.', 'BANCO DE SERVICIOS Y TRANSACCIONES ', 'BANCO DE VALORES S.A.', 'BANCO DEL CHUBUT S.A.', 'BANCO DEL SOL S.A.', 'BANCO DINO S.A.', 'BANCO HIPOTECARIO S.A.', 'BANCO INDUSTRIAL S.A.', 'BANCO INTERFINANZAS S.A.', 'BANCO ITAU ARGENTINA S.A.', 'BANCO JULIO SOCIEDAD ANONIMA', 'BANCO MACRO S.A.', 'BANCO MARIVA S.A.', 'BANCO MASVENTAS S.A.', 'BANCO MERIDIAN S.A.', 'BANCO MUNICIPAL DE ROSARIO', 'BANCO PATAGONIA S.A.', 'BANCO PIANO S.A.', 'BANCO PROVINCIA DE TIERRA DEL FUEGO', 'BANCO PROVINCIA DEL NEUQUÉN SOCIEDA', 'BANCO RIOJA SOCIEDAD ANONIMA UNIPER', 'BANCO ROELA S.A.', 'BANCO SAENZ S.A.', 'BANCO SANTANDER RIO S.A.', 'BANCO SUCREDITO REGIONAL S.A.U.', 'BANCO SUPERVIELLE S.A.', 'BANCO VOII S.A.', 'BNP PARIBAS', 'BRUBANK S.A.U.', 'CITIBANK N.A.', 'COMPAÑIA FINANCIERA ARGENTINA S.A.', 'CREDITO REGIONAL COMPAÑIA FINANCIER', 'FCA COMPAÑIA FINANCIERA S.A.']\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0KARBCTAB-oj",
        "outputId": "d5ef877a-d671-4219-90ca-9191ad683c7c"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Overwriting /content/data/data.yaml\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# run this cell to begin training\n",
        "!python /content/YOLOv6/tools/train.py --batch 32 --conf configs/yolov6s_finetune.py --epochs 100 --img-size 416 --data /content/data/data.yaml --device 0 "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ie9WgRG4D3q0",
        "outputId": "aad591cb-36ad-4809-a0ad-8d66caa793bc"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Using 1 GPU for training... \n",
            "training args are: Namespace(batch_size=32, calib=False, check_images=False, check_labels=False, conf_file='configs/yolov6s_finetune.py', data_path='/content/data/data.yaml', device='0', dist_url='env://', distill=False, distill_feat=False, epochs=100, eval_final_only=False, eval_interval=20, gpu_count=0, heavy_eval_range=50, img_size=416, local_rank=-1, name='exp', output_dir='./runs/train', quant=False, rank=-1, resume=False, save_ckpt_on_last_n_epoch=-1, save_dir='runs/train/exp6', stop_aug_last_n_epoch=15, teacher_model_path=None, temperature=20, workers=8, world_size=1, write_trainbatch_tb=False)\n",
            "\n",
            "Train: Final numbers of valid images: 51942/ labels: 51942. \n",
            "1.9s for dataset initialization.\n",
            "Convert to COCO format\n",
            "100% 6433/6433 [00:00<00:00, 76764.95it/s]\n",
            "Convert to COCO format finished. Resutls saved in /content/data/split/annotations/instances_val.json\n",
            "Val: Final numbers of valid images: 6433/ labels: 6433. \n",
            "1.0s for dataset initialization.\n",
            "Loading state_dict from weights/yolov6s.pt for fine-tuning...\n",
            "Model: Model(\n",
            "  (backbone): EfficientRep(\n",
            "    (stem): RepVGGBlock(\n",
            "      (nonlinearity): ReLU(inplace=True)\n",
            "      (se): Identity()\n",
            "      (rbr_dense): Sequential(\n",
            "        (conv): Conv2d(3, 32, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
            "        (bn): BatchNorm2d(32, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "      )\n",
            "      (rbr_1x1): Sequential(\n",
            "        (conv): Conv2d(3, 32, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
            "        (bn): BatchNorm2d(32, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "      )\n",
            "    )\n",
            "    (ERBlock_2): Sequential(\n",
            "      (0): RepVGGBlock(\n",
            "        (nonlinearity): ReLU(inplace=True)\n",
            "        (se): Identity()\n",
            "        (rbr_dense): Sequential(\n",
            "          (conv): Conv2d(32, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
            "          (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "        )\n",
            "        (rbr_1x1): Sequential(\n",
            "          (conv): Conv2d(32, 64, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
            "          (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "        )\n",
            "      )\n",
            "      (1): RepBlock(\n",
            "        (conv1): RepVGGBlock(\n",
            "          (nonlinearity): ReLU(inplace=True)\n",
            "          (se): Identity()\n",
            "          (rbr_identity): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "          (rbr_dense): Sequential(\n",
            "            (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "            (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "          )\n",
            "          (rbr_1x1): Sequential(\n",
            "            (conv): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "          )\n",
            "        )\n",
            "        (block): Sequential(\n",
            "          (0): RepVGGBlock(\n",
            "            (nonlinearity): ReLU(inplace=True)\n",
            "            (se): Identity()\n",
            "            (rbr_identity): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "            (rbr_dense): Sequential(\n",
            "              (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "              (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "            )\n",
            "            (rbr_1x1): Sequential(\n",
            "              (conv): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "              (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "            )\n",
            "          )\n",
            "        )\n",
            "      )\n",
            "    )\n",
            "    (ERBlock_3): Sequential(\n",
            "      (0): RepVGGBlock(\n",
            "        (nonlinearity): ReLU(inplace=True)\n",
            "        (se): Identity()\n",
            "        (rbr_dense): Sequential(\n",
            "          (conv): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
            "          (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "        )\n",
            "        (rbr_1x1): Sequential(\n",
            "          (conv): Conv2d(64, 128, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
            "          (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "        )\n",
            "      )\n",
            "      (1): RepBlock(\n",
            "        (conv1): RepVGGBlock(\n",
            "          (nonlinearity): ReLU(inplace=True)\n",
            "          (se): Identity()\n",
            "          (rbr_identity): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "          (rbr_dense): Sequential(\n",
            "            (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "            (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "          )\n",
            "          (rbr_1x1): Sequential(\n",
            "            (conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "          )\n",
            "        )\n",
            "        (block): Sequential(\n",
            "          (0): RepVGGBlock(\n",
            "            (nonlinearity): ReLU(inplace=True)\n",
            "            (se): Identity()\n",
            "            (rbr_identity): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "            (rbr_dense): Sequential(\n",
            "              (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "              (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "            )\n",
            "            (rbr_1x1): Sequential(\n",
            "              (conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "              (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "            )\n",
            "          )\n",
            "          (1): RepVGGBlock(\n",
            "            (nonlinearity): ReLU(inplace=True)\n",
            "            (se): Identity()\n",
            "            (rbr_identity): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "            (rbr_dense): Sequential(\n",
            "              (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "              (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "            )\n",
            "            (rbr_1x1): Sequential(\n",
            "              (conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "              (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "            )\n",
            "          )\n",
            "          (2): RepVGGBlock(\n",
            "            (nonlinearity): ReLU(inplace=True)\n",
            "            (se): Identity()\n",
            "            (rbr_identity): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "            (rbr_dense): Sequential(\n",
            "              (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "              (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "            )\n",
            "            (rbr_1x1): Sequential(\n",
            "              (conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "              (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "            )\n",
            "          )\n",
            "        )\n",
            "      )\n",
            "    )\n",
            "    (ERBlock_4): Sequential(\n",
            "      (0): RepVGGBlock(\n",
            "        (nonlinearity): ReLU(inplace=True)\n",
            "        (se): Identity()\n",
            "        (rbr_dense): Sequential(\n",
            "          (conv): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
            "          (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "        )\n",
            "        (rbr_1x1): Sequential(\n",
            "          (conv): Conv2d(128, 256, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
            "          (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "        )\n",
            "      )\n",
            "      (1): RepBlock(\n",
            "        (conv1): RepVGGBlock(\n",
            "          (nonlinearity): ReLU(inplace=True)\n",
            "          (se): Identity()\n",
            "          (rbr_identity): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "          (rbr_dense): Sequential(\n",
            "            (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "            (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "          )\n",
            "          (rbr_1x1): Sequential(\n",
            "            (conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "          )\n",
            "        )\n",
            "        (block): Sequential(\n",
            "          (0): RepVGGBlock(\n",
            "            (nonlinearity): ReLU(inplace=True)\n",
            "            (se): Identity()\n",
            "            (rbr_identity): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "            (rbr_dense): Sequential(\n",
            "              (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "              (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "            )\n",
            "            (rbr_1x1): Sequential(\n",
            "              (conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "              (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "            )\n",
            "          )\n",
            "          (1): RepVGGBlock(\n",
            "            (nonlinearity): ReLU(inplace=True)\n",
            "            (se): Identity()\n",
            "            (rbr_identity): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "            (rbr_dense): Sequential(\n",
            "              (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "              (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "            )\n",
            "            (rbr_1x1): Sequential(\n",
            "              (conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "              (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "            )\n",
            "          )\n",
            "          (2): RepVGGBlock(\n",
            "            (nonlinearity): ReLU(inplace=True)\n",
            "            (se): Identity()\n",
            "            (rbr_identity): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "            (rbr_dense): Sequential(\n",
            "              (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "              (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "            )\n",
            "            (rbr_1x1): Sequential(\n",
            "              (conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "              (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "            )\n",
            "          )\n",
            "          (3): RepVGGBlock(\n",
            "            (nonlinearity): ReLU(inplace=True)\n",
            "            (se): Identity()\n",
            "            (rbr_identity): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "            (rbr_dense): Sequential(\n",
            "              (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "              (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "            )\n",
            "            (rbr_1x1): Sequential(\n",
            "              (conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "              (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "            )\n",
            "          )\n",
            "          (4): RepVGGBlock(\n",
            "            (nonlinearity): ReLU(inplace=True)\n",
            "            (se): Identity()\n",
            "            (rbr_identity): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "            (rbr_dense): Sequential(\n",
            "              (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "              (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "            )\n",
            "            (rbr_1x1): Sequential(\n",
            "              (conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "              (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "            )\n",
            "          )\n",
            "        )\n",
            "      )\n",
            "    )\n",
            "    (ERBlock_5): Sequential(\n",
            "      (0): RepVGGBlock(\n",
            "        (nonlinearity): ReLU(inplace=True)\n",
            "        (se): Identity()\n",
            "        (rbr_dense): Sequential(\n",
            "          (conv): Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
            "          (bn): BatchNorm2d(512, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "        )\n",
            "        (rbr_1x1): Sequential(\n",
            "          (conv): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
            "          (bn): BatchNorm2d(512, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "        )\n",
            "      )\n",
            "      (1): RepBlock(\n",
            "        (conv1): RepVGGBlock(\n",
            "          (nonlinearity): ReLU(inplace=True)\n",
            "          (se): Identity()\n",
            "          (rbr_identity): BatchNorm2d(512, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "          (rbr_dense): Sequential(\n",
            "            (conv): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "            (bn): BatchNorm2d(512, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "          )\n",
            "          (rbr_1x1): Sequential(\n",
            "            (conv): Conv2d(512, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (bn): BatchNorm2d(512, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "          )\n",
            "        )\n",
            "        (block): Sequential(\n",
            "          (0): RepVGGBlock(\n",
            "            (nonlinearity): ReLU(inplace=True)\n",
            "            (se): Identity()\n",
            "            (rbr_identity): BatchNorm2d(512, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "            (rbr_dense): Sequential(\n",
            "              (conv): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "              (bn): BatchNorm2d(512, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "            )\n",
            "            (rbr_1x1): Sequential(\n",
            "              (conv): Conv2d(512, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "              (bn): BatchNorm2d(512, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "            )\n",
            "          )\n",
            "        )\n",
            "      )\n",
            "      (2): SimSPPF(\n",
            "        (cv1): SimConv(\n",
            "          (conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "          (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "          (act): ReLU(inplace=True)\n",
            "        )\n",
            "        (cv2): SimConv(\n",
            "          (conv): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "          (bn): BatchNorm2d(512, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "          (act): ReLU(inplace=True)\n",
            "        )\n",
            "        (m): MaxPool2d(kernel_size=5, stride=1, padding=2, dilation=1, ceil_mode=False)\n",
            "      )\n",
            "    )\n",
            "  )\n",
            "  (neck): RepPANNeck(\n",
            "    (Rep_p4): RepBlock(\n",
            "      (conv1): RepVGGBlock(\n",
            "        (nonlinearity): ReLU(inplace=True)\n",
            "        (se): Identity()\n",
            "        (rbr_dense): Sequential(\n",
            "          (conv): Conv2d(384, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "          (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "        )\n",
            "        (rbr_1x1): Sequential(\n",
            "          (conv): Conv2d(384, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "          (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "        )\n",
            "      )\n",
            "      (block): Sequential(\n",
            "        (0): RepVGGBlock(\n",
            "          (nonlinearity): ReLU(inplace=True)\n",
            "          (se): Identity()\n",
            "          (rbr_identity): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "          (rbr_dense): Sequential(\n",
            "            (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "            (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "          )\n",
            "          (rbr_1x1): Sequential(\n",
            "            (conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "          )\n",
            "        )\n",
            "        (1): RepVGGBlock(\n",
            "          (nonlinearity): ReLU(inplace=True)\n",
            "          (se): Identity()\n",
            "          (rbr_identity): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "          (rbr_dense): Sequential(\n",
            "            (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "            (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "          )\n",
            "          (rbr_1x1): Sequential(\n",
            "            (conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "          )\n",
            "        )\n",
            "        (2): RepVGGBlock(\n",
            "          (nonlinearity): ReLU(inplace=True)\n",
            "          (se): Identity()\n",
            "          (rbr_identity): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "          (rbr_dense): Sequential(\n",
            "            (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "            (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "          )\n",
            "          (rbr_1x1): Sequential(\n",
            "            (conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "          )\n",
            "        )\n",
            "      )\n",
            "    )\n",
            "    (Rep_p3): RepBlock(\n",
            "      (conv1): RepVGGBlock(\n",
            "        (nonlinearity): ReLU(inplace=True)\n",
            "        (se): Identity()\n",
            "        (rbr_dense): Sequential(\n",
            "          (conv): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "          (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "        )\n",
            "        (rbr_1x1): Sequential(\n",
            "          (conv): Conv2d(192, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "          (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "        )\n",
            "      )\n",
            "      (block): Sequential(\n",
            "        (0): RepVGGBlock(\n",
            "          (nonlinearity): ReLU(inplace=True)\n",
            "          (se): Identity()\n",
            "          (rbr_identity): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "          (rbr_dense): Sequential(\n",
            "            (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "            (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "          )\n",
            "          (rbr_1x1): Sequential(\n",
            "            (conv): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "          )\n",
            "        )\n",
            "        (1): RepVGGBlock(\n",
            "          (nonlinearity): ReLU(inplace=True)\n",
            "          (se): Identity()\n",
            "          (rbr_identity): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "          (rbr_dense): Sequential(\n",
            "            (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "            (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "          )\n",
            "          (rbr_1x1): Sequential(\n",
            "            (conv): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "          )\n",
            "        )\n",
            "        (2): RepVGGBlock(\n",
            "          (nonlinearity): ReLU(inplace=True)\n",
            "          (se): Identity()\n",
            "          (rbr_identity): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "          (rbr_dense): Sequential(\n",
            "            (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "            (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "          )\n",
            "          (rbr_1x1): Sequential(\n",
            "            (conv): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "          )\n",
            "        )\n",
            "      )\n",
            "    )\n",
            "    (Rep_n3): RepBlock(\n",
            "      (conv1): RepVGGBlock(\n",
            "        (nonlinearity): ReLU(inplace=True)\n",
            "        (se): Identity()\n",
            "        (rbr_identity): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "        (rbr_dense): Sequential(\n",
            "          (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "          (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "        )\n",
            "        (rbr_1x1): Sequential(\n",
            "          (conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "          (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "        )\n",
            "      )\n",
            "      (block): Sequential(\n",
            "        (0): RepVGGBlock(\n",
            "          (nonlinearity): ReLU(inplace=True)\n",
            "          (se): Identity()\n",
            "          (rbr_identity): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "          (rbr_dense): Sequential(\n",
            "            (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "            (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "          )\n",
            "          (rbr_1x1): Sequential(\n",
            "            (conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "          )\n",
            "        )\n",
            "        (1): RepVGGBlock(\n",
            "          (nonlinearity): ReLU(inplace=True)\n",
            "          (se): Identity()\n",
            "          (rbr_identity): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "          (rbr_dense): Sequential(\n",
            "            (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "            (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "          )\n",
            "          (rbr_1x1): Sequential(\n",
            "            (conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "          )\n",
            "        )\n",
            "        (2): RepVGGBlock(\n",
            "          (nonlinearity): ReLU(inplace=True)\n",
            "          (se): Identity()\n",
            "          (rbr_identity): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "          (rbr_dense): Sequential(\n",
            "            (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "            (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "          )\n",
            "          (rbr_1x1): Sequential(\n",
            "            (conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "          )\n",
            "        )\n",
            "      )\n",
            "    )\n",
            "    (Rep_n4): RepBlock(\n",
            "      (conv1): RepVGGBlock(\n",
            "        (nonlinearity): ReLU(inplace=True)\n",
            "        (se): Identity()\n",
            "        (rbr_identity): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "        (rbr_dense): Sequential(\n",
            "          (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "          (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "        )\n",
            "        (rbr_1x1): Sequential(\n",
            "          (conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "          (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "        )\n",
            "      )\n",
            "      (block): Sequential(\n",
            "        (0): RepVGGBlock(\n",
            "          (nonlinearity): ReLU(inplace=True)\n",
            "          (se): Identity()\n",
            "          (rbr_identity): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "          (rbr_dense): Sequential(\n",
            "            (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "            (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "          )\n",
            "          (rbr_1x1): Sequential(\n",
            "            (conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "          )\n",
            "        )\n",
            "        (1): RepVGGBlock(\n",
            "          (nonlinearity): ReLU(inplace=True)\n",
            "          (se): Identity()\n",
            "          (rbr_identity): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "          (rbr_dense): Sequential(\n",
            "            (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "            (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "          )\n",
            "          (rbr_1x1): Sequential(\n",
            "            (conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "          )\n",
            "        )\n",
            "        (2): RepVGGBlock(\n",
            "          (nonlinearity): ReLU(inplace=True)\n",
            "          (se): Identity()\n",
            "          (rbr_identity): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "          (rbr_dense): Sequential(\n",
            "            (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "            (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "          )\n",
            "          (rbr_1x1): Sequential(\n",
            "            (conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "          )\n",
            "        )\n",
            "      )\n",
            "    )\n",
            "    (reduce_layer0): SimConv(\n",
            "      (conv): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "      (act): ReLU(inplace=True)\n",
            "    )\n",
            "    (upsample0): Transpose(\n",
            "      (upsample_transpose): ConvTranspose2d(128, 128, kernel_size=(2, 2), stride=(2, 2))\n",
            "    )\n",
            "    (reduce_layer1): SimConv(\n",
            "      (conv): Conv2d(128, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "      (act): ReLU(inplace=True)\n",
            "    )\n",
            "    (upsample1): Transpose(\n",
            "      (upsample_transpose): ConvTranspose2d(64, 64, kernel_size=(2, 2), stride=(2, 2))\n",
            "    )\n",
            "    (downsample2): SimConv(\n",
            "      (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
            "      (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "      (act): ReLU(inplace=True)\n",
            "    )\n",
            "    (downsample1): SimConv(\n",
            "      (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
            "      (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "      (act): ReLU(inplace=True)\n",
            "    )\n",
            "  )\n",
            "  (detect): Detect(\n",
            "    (proj_conv): Conv2d(17, 1, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "    (stems): ModuleList(\n",
            "      (0): Conv(\n",
            "        (conv): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "        (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "        (act): SiLU(inplace=True)\n",
            "      )\n",
            "      (1): Conv(\n",
            "        (conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "        (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "        (act): SiLU(inplace=True)\n",
            "      )\n",
            "      (2): Conv(\n",
            "        (conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "        (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "        (act): SiLU(inplace=True)\n",
            "      )\n",
            "    )\n",
            "    (cls_convs): ModuleList(\n",
            "      (0): Conv(\n",
            "        (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "        (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "        (act): SiLU(inplace=True)\n",
            "      )\n",
            "      (1): Conv(\n",
            "        (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "        (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "        (act): SiLU(inplace=True)\n",
            "      )\n",
            "      (2): Conv(\n",
            "        (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "        (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "        (act): SiLU(inplace=True)\n",
            "      )\n",
            "    )\n",
            "    (reg_convs): ModuleList(\n",
            "      (0): Conv(\n",
            "        (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "        (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "        (act): SiLU(inplace=True)\n",
            "      )\n",
            "      (1): Conv(\n",
            "        (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "        (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "        (act): SiLU(inplace=True)\n",
            "      )\n",
            "      (2): Conv(\n",
            "        (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "        (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "        (act): SiLU(inplace=True)\n",
            "      )\n",
            "    )\n",
            "    (cls_preds): ModuleList(\n",
            "      (0): Conv2d(64, 55, kernel_size=(1, 1), stride=(1, 1))\n",
            "      (1): Conv2d(128, 55, kernel_size=(1, 1), stride=(1, 1))\n",
            "      (2): Conv2d(256, 55, kernel_size=(1, 1), stride=(1, 1))\n",
            "    )\n",
            "    (reg_preds): ModuleList(\n",
            "      (0): Conv2d(64, 4, kernel_size=(1, 1), stride=(1, 1))\n",
            "      (1): Conv2d(128, 4, kernel_size=(1, 1), stride=(1, 1))\n",
            "      (2): Conv2d(256, 4, kernel_size=(1, 1), stride=(1, 1))\n",
            "    )\n",
            "  )\n",
            ")\n",
            "Training start...\n",
            "\n",
            "     Epoch  iou_loss  dfl_loss  cls_loss\n",
            "  0% 0/1624 [00:00<?, ?it/s]\n",
            "ERROR in training steps.\n",
            "ERROR in training loop or eval/save model.\n",
            "Traceback (most recent call last):\n",
            "  File \"/content/YOLOv6/tools/train.py\", line 126, in <module>\n",
            "    main(args)\n",
            "  File \"/content/YOLOv6/tools/train.py\", line 116, in main\n",
            "    trainer.train()\n",
            "  File \"/content/YOLOv6/yolov6/core/engine.py\", line 99, in train\n",
            "    self.train_in_loop(self.epoch)\n",
            "  File \"/content/YOLOv6/yolov6/core/engine.py\", line 113, in train_in_loop\n",
            "    self.train_in_steps(epoch_num, self.step)\n",
            "  File \"/content/YOLOv6/yolov6/core/engine.py\", line 142, in train_in_steps\n",
            "    total_loss, loss_items = self.compute_loss(preds, targets, epoch_num, step_num)\n",
            "  File \"/content/YOLOv6/yolov6/models/loss.py\", line 85, in __call__\n",
            "    pred_bboxes.detach() * stride_tensor)\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py\", line 889, in _call_impl\n",
            "    result = self.forward(*input, **kwargs)\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/torch/autograd/grad_mode.py\", line 27, in decorate_context\n",
            "    return func(*args, **kwargs)\n",
            "  File \"/content/YOLOv6/yolov6/assigners/atss_assigner.py\", line 78, in forward\n",
            "    gt_labels, gt_bboxes, target_gt_idx, fg_mask)\n",
            "  File \"/content/YOLOv6/yolov6/assigners/atss_assigner.py\", line 158, in get_targets\n",
            "    target_scores = F.one_hot(target_labels.long(), self.num_classes + 1).float()\n",
            "RuntimeError: Class values must be smaller than num_classes.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!nvidia-smi"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7cRtRY6XG3Yp",
        "outputId": "70c3f664-9c6e-4587-a595-d57104089387"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fri Sep 30 06:35:51 2022       \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   44C    P8    10W /  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"
          ]
        }
      ]
    }
  ]
}