Copy_of_neural_network_project3.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyN1mU4MBAA5ijcJyNpTmEVR",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/ssvakil/Machine-Learning-in-Python-Workshop/blob/master/Copy_of_neural_network_project3.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MCbaQMdx9IdS",
        "outputId": "a6484026-e0ae-4297-ce58-d5063c51313a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'EResFD'...\n",
            "remote: Enumerating objects: 61, done.\u001b[K\n",
            "remote: Counting objects: 100% (61/61), done.\u001b[K\n",
            "remote: Compressing objects: 100% (55/55), done.\u001b[K\n",
            "remote: Total 61 (delta 5), reused 55 (delta 3), pack-reused 0\u001b[K\n",
            "Unpacking objects: 100% (61/61), done.\n"
          ]
        }
      ],
      "source": [
        "!git clone https://github.com/clovaai/EResFD.git\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ls"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pYsATU6-9ah6",
        "outputId": "30d23431-2458-4c9d-ce87-ef00ef32fcd6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[0m\u001b[01;34mEResFD\u001b[0m/  \u001b[01;34msample_data\u001b[0m/\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install python3.4.8.29"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JIoBMkKJvniT",
        "outputId": "47673009-d102-4974-daaa-86a5b1d8a339"
      },
      "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",
            "\u001b[31mERROR: Could not find a version that satisfies the requirement python3.4.8.29 (from versions: none)\u001b[0m\n",
            "\u001b[31mERROR: No matching distribution found for python3.4.8.29\u001b[0m\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install Opencv-python==3.4.0.14"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "V41EJl0j-5-9",
        "outputId": "63b29147-c5b4-41b4-a343-5f07faa775cd"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting Opencv-python==3.4.0.14\n",
            "  Using cached opencv-python-3.4.0.14.tar.gz (87.3 MB)\n",
            "Requirement already satisfied: numpy>=1.21.6 in /usr/local/lib/python3.8/dist-packages (from Opencv-python==3.4.0.14) (1.21.6)\n",
            "Building wheels for collected packages: Opencv-python\n",
            "  Building wheel for Opencv-python (setup.py) ... \u001b[?25lerror\n",
            "\u001b[31m  ERROR: Failed building wheel for Opencv-python\u001b[0m\n",
            "\u001b[?25h  Running setup.py clean for Opencv-python\n",
            "Failed to build Opencv-python\n",
            "Installing collected packages: Opencv-python\n",
            "  Attempting uninstall: Opencv-python\n",
            "    Found existing installation: opencv-python 4.6.0.66\n",
            "    Uninstalling opencv-python-4.6.0.66:\n",
            "      Successfully uninstalled opencv-python-4.6.0.66\n",
            "    Running setup.py install for Opencv-python ... \u001b[?25l\u001b[?25herror\n",
            "  Rolling back uninstall of opencv-python\n",
            "  Moving to /usr/local/lib/python3.8/dist-packages/cv2/\n",
            "   from /usr/local/lib/python3.8/dist-packages/~v2\n",
            "  Moving to /usr/local/lib/python3.8/dist-packages/opencv_python-4.6.0.66.dist-info/\n",
            "   from /usr/local/lib/python3.8/dist-packages/~pencv_python-4.6.0.66.dist-info\n",
            "  Moving to /usr/local/lib/python3.8/dist-packages/opencv_python.libs/\n",
            "   from /usr/local/lib/python3.8/dist-packages/~pencv_python.libs\n",
            "\u001b[31mERROR: Command errored out with exit status 1: /usr/bin/python3 -u -c 'import io, os, sys, setuptools, tokenize; sys.argv[0] = '\"'\"'/tmp/pip-install-bvjv733d/opencv-python_d5a669e62b604778b79e3546486e9621/setup.py'\"'\"'; __file__='\"'\"'/tmp/pip-install-bvjv733d/opencv-python_d5a669e62b604778b79e3546486e9621/setup.py'\"'\"';f = getattr(tokenize, '\"'\"'open'\"'\"', open)(__file__) if os.path.exists(__file__) else io.StringIO('\"'\"'from setuptools import setup; setup()'\"'\"');code = f.read().replace('\"'\"'\\r\\n'\"'\"', '\"'\"'\\n'\"'\"');f.close();exec(compile(code, __file__, '\"'\"'exec'\"'\"'))' install --record /tmp/pip-record-i51v6593/install-record.txt --single-version-externally-managed --compile --install-headers /usr/local/include/python3.8/Opencv-python Check the logs for full command output.\u001b[0m\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cd EResFD"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3_U8GZkNFeo6",
        "outputId": "87364c84-1f22-4a90-f33f-b3c76df8abd3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/EResFD\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install -r requirements.txt"
      ],
      "metadata": {
        "id": "BVU9v3aWHfkj",
        "outputId": "d5168f85-a677-4b30-c79b-1964ed0e35b8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[31mERROR: Could not open requirements file: [Errno 2] No such file or directory: 'requirements.txt'\u001b[0m\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install scipy"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xmSeJJ4uBpo2",
        "outputId": "04cd2628-9f88-44dc-cc43-7eae143c1d3e"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.8/dist-packages (1.7.3)\n",
            "Requirement already satisfied: numpy<1.23.0,>=1.16.5 in /usr/local/lib/python3.8/dist-packages (from scipy) (1.21.6)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install matplotlib==2.2.2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 592
        },
        "id": "xGmdKvIrDIHM",
        "outputId": "6bab9b62-86a2-41f2-83ae-2190611f7b49"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting matplotlib==2.2.2\n",
            "  Downloading matplotlib-2.2.2.tar.gz (37.3 MB)\n",
            "\u001b[K     |████████████████████████████████| 37.3 MB 129 kB/s \n",
            "\u001b[?25hRequirement already satisfied: numpy>=1.7.1 in /usr/local/lib/python3.8/dist-packages (from matplotlib==2.2.2) (1.21.6)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.8/dist-packages (from matplotlib==2.2.2) (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==2.2.2) (3.0.9)\n",
            "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.8/dist-packages (from matplotlib==2.2.2) (2.8.2)\n",
            "Requirement already satisfied: pytz in /usr/local/lib/python3.8/dist-packages (from matplotlib==2.2.2) (2022.6)\n",
            "Requirement already satisfied: six>=1.10 in /usr/local/lib/python3.8/dist-packages (from matplotlib==2.2.2) (1.15.0)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.8/dist-packages (from matplotlib==2.2.2) (1.4.4)\n",
            "Building wheels for collected packages: matplotlib\n",
            "  Building wheel for matplotlib (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for matplotlib: filename=matplotlib-2.2.2-cp38-cp38-linux_x86_64.whl size=10551459 sha256=509fc6aed2796b042680a1337b29696c559986263dd1e83ef426764425e1d308\n",
            "  Stored in directory: /root/.cache/pip/wheels/5b/99/36/584361dc7788ea9af2bf4dc89881e8c4cc251c15c8dd16dcf6\n",
            "Successfully built matplotlib\n",
            "Installing collected packages: matplotlib\n",
            "  Attempting uninstall: matplotlib\n",
            "    Found existing installation: matplotlib 3.2.2\n",
            "    Uninstalling matplotlib-3.2.2:\n",
            "      Successfully uninstalled matplotlib-3.2.2\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",
            "plotnine 0.8.0 requires matplotlib>=3.1.1, but you have matplotlib 2.2.2 which is incompatible.\n",
            "mizani 0.7.3 requires matplotlib>=3.1.1, but you have matplotlib 2.2.2 which is incompatible.\n",
            "datascience 0.17.5 requires matplotlib>=3.0.0, but you have matplotlib 2.2.2 which is incompatible.\n",
            "arviz 0.12.1 requires matplotlib>=3.0, but you have matplotlib 2.2.2 which is incompatible.\u001b[0m\n",
            "Successfully installed matplotlib-2.2.2\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "matplotlib",
                  "mpl_toolkits"
                ]
              }
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install kaggle"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Tz6V3q_wGfo6",
        "outputId": "a9b943ac-0bb5-4b06-f5d9-5b63a73fbbad"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: kaggle in /usr/local/lib/python3.8/dist-packages (1.5.12)\n",
            "Requirement already satisfied: urllib3 in /usr/local/lib/python3.8/dist-packages (from kaggle) (1.24.3)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.8/dist-packages (from kaggle) (2.23.0)\n",
            "Requirement already satisfied: certifi in /usr/local/lib/python3.8/dist-packages (from kaggle) (2022.12.7)\n",
            "Requirement already satisfied: python-dateutil in /usr/local/lib/python3.8/dist-packages (from kaggle) (2.8.2)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.8/dist-packages (from kaggle) (4.64.1)\n",
            "Requirement already satisfied: six>=1.10 in /usr/local/lib/python3.8/dist-packages (from kaggle) (1.15.0)\n",
            "Requirement already satisfied: python-slugify in /usr/local/lib/python3.8/dist-packages (from kaggle) (7.0.0)\n",
            "Requirement already satisfied: text-unidecode>=1.3 in /usr/local/lib/python3.8/dist-packages (from python-slugify->kaggle) (1.3)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.8/dist-packages (from requests->kaggle) (2.10)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.8/dist-packages (from requests->kaggle) (3.0.4)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import files\n",
        "files.upload()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 89
        },
        "id": "ALkDZUbCG0-1",
        "outputId": "db88d183-3e9c-4a14-b776-1bc2f84c11a9"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-873e79f3-f815-43e1-b048-c15246ec88dd\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-873e79f3-f815-43e1-b048-c15246ec88dd\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script>// Copyright 2017 Google LLC\n",
              "//\n",
              "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
              "// you may not use this file except in compliance with the License.\n",
              "// You may obtain a copy of the License at\n",
              "//\n",
              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
              "//\n",
              "// Unless required by applicable law or agreed to in writing, software\n",
              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
              "// See the License for the specific language governing permissions and\n",
              "// limitations under the License.\n",
              "\n",
              "/**\n",
              " * @fileoverview Helpers for google.colab Python module.\n",
              " */\n",
              "(function(scope) {\n",
              "function span(text, styleAttributes = {}) {\n",
              "  const element = document.createElement('span');\n",
              "  element.textContent = text;\n",
              "  for (const key of Object.keys(styleAttributes)) {\n",
              "    element.style[key] = styleAttributes[key];\n",
              "  }\n",
              "  return element;\n",
              "}\n",
              "\n",
              "// Max number of bytes which will be uploaded at a time.\n",
              "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
              "\n",
              "function _uploadFiles(inputId, outputId) {\n",
              "  const steps = uploadFilesStep(inputId, outputId);\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  // Cache steps on the outputElement to make it available for the next call\n",
              "  // to uploadFilesContinue from Python.\n",
              "  outputElement.steps = steps;\n",
              "\n",
              "  return _uploadFilesContinue(outputId);\n",
              "}\n",
              "\n",
              "// This is roughly an async generator (not supported in the browser yet),\n",
              "// where there are multiple asynchronous steps and the Python side is going\n",
              "// to poll for completion of each step.\n",
              "// This uses a Promise to block the python side on completion of each step,\n",
              "// then passes the result of the previous step as the input to the next step.\n",
              "function _uploadFilesContinue(outputId) {\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  const steps = outputElement.steps;\n",
              "\n",
              "  const next = steps.next(outputElement.lastPromiseValue);\n",
              "  return Promise.resolve(next.value.promise).then((value) => {\n",
              "    // Cache the last promise value to make it available to the next\n",
              "    // step of the generator.\n",
              "    outputElement.lastPromiseValue = value;\n",
              "    return next.value.response;\n",
              "  });\n",
              "}\n",
              "\n",
              "/**\n",
              " * Generator function which is called between each async step of the upload\n",
              " * process.\n",
              " * @param {string} inputId Element ID of the input file picker element.\n",
              " * @param {string} outputId Element ID of the output display.\n",
              " * @return {!Iterable<!Object>} Iterable of next steps.\n",
              " */\n",
              "function* uploadFilesStep(inputId, outputId) {\n",
              "  const inputElement = document.getElementById(inputId);\n",
              "  inputElement.disabled = false;\n",
              "\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  outputElement.innerHTML = '';\n",
              "\n",
              "  const pickedPromise = new Promise((resolve) => {\n",
              "    inputElement.addEventListener('change', (e) => {\n",
              "      resolve(e.target.files);\n",
              "    });\n",
              "  });\n",
              "\n",
              "  const cancel = document.createElement('button');\n",
              "  inputElement.parentElement.appendChild(cancel);\n",
              "  cancel.textContent = 'Cancel upload';\n",
              "  const cancelPromise = new Promise((resolve) => {\n",
              "    cancel.onclick = () => {\n",
              "      resolve(null);\n",
              "    };\n",
              "  });\n",
              "\n",
              "  // Wait for the user to pick the files.\n",
              "  const files = yield {\n",
              "    promise: Promise.race([pickedPromise, cancelPromise]),\n",
              "    response: {\n",
              "      action: 'starting',\n",
              "    }\n",
              "  };\n",
              "\n",
              "  cancel.remove();\n",
              "\n",
              "  // Disable the input element since further picks are not allowed.\n",
              "  inputElement.disabled = true;\n",
              "\n",
              "  if (!files) {\n",
              "    return {\n",
              "      response: {\n",
              "        action: 'complete',\n",
              "      }\n",
              "    };\n",
              "  }\n",
              "\n",
              "  for (const file of files) {\n",
              "    const li = document.createElement('li');\n",
              "    li.append(span(file.name, {fontWeight: 'bold'}));\n",
              "    li.append(span(\n",
              "        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
              "        `last modified: ${\n",
              "            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
              "                                    'n/a'} - `));\n",
              "    const percent = span('0% done');\n",
              "    li.appendChild(percent);\n",
              "\n",
              "    outputElement.appendChild(li);\n",
              "\n",
              "    const fileDataPromise = new Promise((resolve) => {\n",
              "      const reader = new FileReader();\n",
              "      reader.onload = (e) => {\n",
              "        resolve(e.target.result);\n",
              "      };\n",
              "      reader.readAsArrayBuffer(file);\n",
              "    });\n",
              "    // Wait for the data to be ready.\n",
              "    let fileData = yield {\n",
              "      promise: fileDataPromise,\n",
              "      response: {\n",
              "        action: 'continue',\n",
              "      }\n",
              "    };\n",
              "\n",
              "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
              "    let position = 0;\n",
              "    do {\n",
              "      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
              "      const chunk = new Uint8Array(fileData, position, length);\n",
              "      position += length;\n",
              "\n",
              "      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
              "      yield {\n",
              "        response: {\n",
              "          action: 'append',\n",
              "          file: file.name,\n",
              "          data: base64,\n",
              "        },\n",
              "      };\n",
              "\n",
              "      let percentDone = fileData.byteLength === 0 ?\n",
              "          100 :\n",
              "          Math.round((position / fileData.byteLength) * 100);\n",
              "      percent.textContent = `${percentDone}% done`;\n",
              "\n",
              "    } while (position < fileData.byteLength);\n",
              "  }\n",
              "\n",
              "  // All done.\n",
              "  yield {\n",
              "    response: {\n",
              "      action: 'complete',\n",
              "    }\n",
              "  };\n",
              "}\n",
              "\n",
              "scope.google = scope.google || {};\n",
              "scope.google.colab = scope.google.colab || {};\n",
              "scope.google.colab._files = {\n",
              "  _uploadFiles,\n",
              "  _uploadFilesContinue,\n",
              "};\n",
              "})(self);\n",
              "</script> "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving kaggle.json to kaggle.json\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'kaggle.json': b'{\"username\":\"ssvakil\",\"key\":\"ec3e865ca81abc215c5872731698381f\"}'}"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!mkdir -p ~/.kaggle\n",
        "!cp kaggle.json ~/.kaggle/kaggle.json\n",
        "!chmod 600 ~/.kaggle/kaggle.json"
      ],
      "metadata": {
        "id": "OW2lv8cvJjdl"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pwd"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "MK6pn25EK71S",
        "outputId": "8d8969ec-ef0b-45cc-e907-38b5d6b70bba"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'/content/EResFD'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!kaggle datasets download -d vinayakshanawad/face-mask-segmentation-wider-face-dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Hx0h45M5GZrt",
        "outputId": "0b527c3e-12aa-41c3-bfba-19cb4cc32dda"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading face-mask-segmentation-wider-face-dataset.zip to /content\n",
            " 99% 783M/795M [00:08<00:00, 145MB/s]\n",
            "100% 795M/795M [00:08<00:00, 94.5MB/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!python3 /content/EResFD/data/config.py "
      ],
      "metadata": {
        "id": "wDwQEDHhl1sJ"
      },
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!python3 /content/EResFD/prepare_wider_data.py"
      ],
      "metadata": {
        "id": "MqF4TQUkX29A"
      },
      "execution_count": 60,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip3 install tensorbay"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GpYab5d6zBST",
        "outputId": "60a5ba08-7c69-4f5d-ac98-01eb214a79b0"
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: tensorbay in /usr/local/lib/python3.8/dist-packages (1.24.1)\n",
            "Requirement already satisfied: numpy-quaternion<=2021.11.4.15.26.3,>=2021.3.17.16.51.43 in /usr/local/lib/python3.8/dist-packages (from tensorbay) (2021.11.4.15.26.3)\n",
            "Requirement already satisfied: numpy>=1.16.0 in /usr/local/lib/python3.8/dist-packages (from tensorbay) (1.21.6)\n",
            "Requirement already satisfied: tqdm>=4.14.0 in /usr/local/lib/python3.8/dist-packages (from tensorbay) (4.64.1)\n",
            "Requirement already satisfied: requests>=2.4.2 in /usr/local/lib/python3.8/dist-packages (from tensorbay) (2.23.0)\n",
            "Requirement already satisfied: requests-toolbelt>=0.3.0 in /usr/local/lib/python3.8/dist-packages (from tensorbay) (0.10.1)\n",
            "Requirement already satisfied: urllib3>=1.15 in /usr/local/lib/python3.8/dist-packages (from tensorbay) (1.24.3)\n",
            "Requirement already satisfied: ulid-py>=1.1.0 in /usr/local/lib/python3.8/dist-packages (from tensorbay) (1.1.0)\n",
            "Requirement already satisfied: typing-extensions>=3.7.2 in /usr/local/lib/python3.8/dist-packages (from tensorbay) (4.4.0)\n",
            "Requirement already satisfied: click>=7.0.0 in /usr/local/lib/python3.8/dist-packages (from tensorbay) (7.1.2)\n",
            "Requirement already satisfied: filetype>=1.0.0 in /usr/local/lib/python3.8/dist-packages (from tensorbay) (1.2.0)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.8/dist-packages (from requests>=2.4.2->tensorbay) (2022.12.7)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.8/dist-packages (from requests>=2.4.2->tensorbay) (2.10)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.8/dist-packages (from requests>=2.4.2->tensorbay) (3.0.4)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tensorbay.opendataset.WIDER_FACE /content/EResFD/images.npy"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 165
        },
        "id": "cSt8ldS5uWJH",
        "outputId": "45233d54-2748-4e97-bba3-e6bc859a4254"
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-55-aa6ef8eede38>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtensorbay\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopendataset\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mWIDER_FACE\u001b[0m \u001b[0;34m/\u001b[0m\u001b[0mcontent\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0mEResFD\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0mimages\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnpy\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m: name 'tensorbay' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!wget http://mmlab.ie.cuhk.edu.hk/projects/WIDERFace/support/bbx_annotation/wider_face_split.zip\n",
        "     "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vbpQQHXiv1Jo",
        "outputId": "0c82bd4b-f56f-410b-e3c2-bd5c65df7cae"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2022-12-20 18:15:27--  http://mmlab.ie.cuhk.edu.hk/projects/WIDERFace/support/bbx_annotation/wider_face_split.zip\n",
            "Resolving mmlab.ie.cuhk.edu.hk (mmlab.ie.cuhk.edu.hk)... 137.189.99.12\n",
            "Connecting to mmlab.ie.cuhk.edu.hk (mmlab.ie.cuhk.edu.hk)|137.189.99.12|:80... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 3591642 (3.4M) [application/zip]\n",
            "Saving to: ‘wider_face_split.zip’\n",
            "\n",
            "wider_face_split.zi 100%[===================>]   3.42M  2.58MB/s    in 1.3s    \n",
            "\n",
            "2022-12-20 18:15:29 (2.58 MB/s) - ‘wider_face_split.zip’ saved [3591642/3591642]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!python3 config.py /images.npy"
      ],
      "metadata": {
        "id": "j9rBdwuweVnN",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0ecabf10-a07c-42c7-ed08-1b277fcc425a"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "python3: can't open file 'config.py': [Errno 2] No such file or directory\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cd EResFD"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3CiQSS98jQOn",
        "outputId": "5b2c8e69-4ffd-4487-8b3d-273420af2c50"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/EResFD\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cd eval_tools"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hH9OglgIfNGa",
        "outputId": "5cd727cd-9eff-40ca-af6e-a4a566164998"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/EResFD/eval_tools\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "lVPQeNXfULZh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "cd \n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FsEtHOMwZjrF",
        "outputId": "c023af34-ca2a-4ff3-a0e9-0cc1d76d28e6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/EResFD\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!sudo apt install python3.4.3.18"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lFaNyLFs0_DL",
        "outputId": "f7ab423d-dfa5-4fb3-f821-fd7dd64897f7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Reading package lists... Done\n",
            "Building dependency tree       \n",
            "Reading state information... Done\n",
            "E: Unable to locate package python3.4.3.18\n",
            "E: Couldn't find any package by glob 'python3.4.3.18'\n",
            "E: Couldn't find any package by regex 'python3.4.3.18'\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ls"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EgUT2jYIgpMe",
        "outputId": "84fee715-4110-4b3e-bf9d-338d2e92120b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[0m\u001b[01;34mdata\u001b[0m/                                          \u001b[01;34mmodels\u001b[0m/\n",
            "demo.py                                        NOTICE\n",
            "\u001b[01;34meval_tools\u001b[0m/                                    nsml_package.txt\n",
            "face-mask-segmentation-wider-face-dataset.zip  prepare_wider_data.py\n",
            "images.npy                                     README.md\n",
            "\u001b[01;34mimg\u001b[0m/                                           requirements.txt\n",
            "kaggle.json                                    test_wider.py\n",
            "\u001b[01;34mlayers\u001b[0m/                                        \u001b[01;34mtmp\u001b[0m/\n",
            "LICENSE                                        \u001b[01;34mutils\u001b[0m/\n",
            "\u001b[01;34mmisc\u001b[0m/                                          \u001b[01;34mweights\u001b[0m/\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from zipfile import ZipFile\n",
        "file_name=\"face-mask-segmentation-wider-face-dataset.zip\"\n",
        "with ZipFile(file_name,'r')as zip:\n",
        "  zip.extractall()\n",
        "print('done')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0QSlH-b9YblO",
        "outputId": "97227642-91a3-4340-d196-ffecb7c299f6"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "done\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pwd"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "NniFh7tXwsj3",
        "outputId": "a17099d0-905f-4465-c278-f0c693bb0e79"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'/content/EResFD'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from zipfile import ZipFile\n",
        "file_name=\"wider_face_split.zip\"\n",
        "with ZipFile(file_name,'r')as zip:\n",
        "  zip.extractall()\n",
        "print('done')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6PRBFiLKbvNj",
        "outputId": "f683737f-fa19-4e0a-97a0-02c028e247f0"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "done\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!python3 test_wider.py --wider_root [./images.npy] --model ./weights/eresfd_16.pth"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ts7fwIUKYDam",
        "outputId": "96efce51-f2b0-4804-8dfa-888f6d2cfb66"
      },
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "test_wider.py:219: SyntaxWarning: \"is\" with a literal. Did you mean \"==\"?\n",
            "  if subset is \"val\":\n",
            "Namespace(anchor_scales=[1.0], anchor_size_ratio=[1.25], anchor_sizes=[16, 32, 64, 128, 256, 512], anchor_steps=[4, 8, 16, 32, 64, 128], detect_log_write=False, eval_only=False, exp='prediction_wider_for_eval', gt_path='./eval_tools/wider_ground_truth', iou_thresh=0.5, iteration=0, legend_name='Ours', model='./weights/eresfd_16.pth', nms_thresh=0.3, nms_top_k=5000, no_max_im_shrink=False, pause=0, session_name=0, single_scale_test=False, thresh=0.05, top_k=750, wider_root='[./images.npy]', width_mult=0.0625)\n",
            "ANCHOR ratio settings:  [[1.25], [1.25], [1.25], [1.25], [1.25], [1.25]]\n",
            "ANCHOR stride settings:  [4, 8, 16, 32, 64, 128]\n",
            "Detect: self.thresh: 0.050000\n",
            "Detect: self.nms_thresh(IOU-overlap th.): 0.300000\n",
            "Detect: self.nms_top_k: 5000.000000\n",
            "Detect: self.top_k: 750.000000\n",
            "EResFD(\n",
            "  (base): EResNet(\n",
            "    (conv1): Sequential(\n",
            "      (0): Conv2d(3, 8, kernel_size=(5, 5), stride=(4, 4), padding=(2, 2), bias=False)\n",
            "      (1): BatchNorm2d(8, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    )\n",
            "    (conv2): Sequential(\n",
            "      (0): Conv2d(8, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "      (1): BatchNorm2d(8, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (2): ReLU(inplace=True)\n",
            "    )\n",
            "    (conv3): Sequential(\n",
            "      (0): Conv2d(8, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "      (1): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (2): ReLU(inplace=True)\n",
            "    )\n",
            "    (conv4): ConvBlock(\n",
            "      (res_layer): Sequential(\n",
            "        (0): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "        (1): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "        (2): ReLU(inplace=True)\n",
            "        (3): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "        (4): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      )\n",
            "    )\n",
            "    (m0): DetEncoder(\n",
            "      (b2_1): Sequential(\n",
            "        (0): ConvBlock(\n",
            "          (shortcut_layer): Sequential(\n",
            "            (0): Conv2d(16, 16, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
            "            (1): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "          )\n",
            "          (res_layer): Sequential(\n",
            "            (0): Conv2d(16, 16, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
            "            (1): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (2): ReLU(inplace=True)\n",
            "            (3): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "            (4): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "          )\n",
            "        )\n",
            "        (1): ConvBlock(\n",
            "          (res_layer): Sequential(\n",
            "            (0): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "            (1): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (2): ReLU(inplace=True)\n",
            "            (3): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "            (4): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "          )\n",
            "        )\n",
            "        (2): ConvBlock(\n",
            "          (res_layer): Sequential(\n",
            "            (0): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "            (1): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (2): ReLU(inplace=True)\n",
            "            (3): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "            (4): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "          )\n",
            "        )\n",
            "      )\n",
            "      (b2_2): Sequential(\n",
            "        (0): ConvBlock(\n",
            "          (shortcut_layer): Sequential(\n",
            "            (0): Conv2d(16, 16, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
            "            (1): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "          )\n",
            "          (res_layer): Sequential(\n",
            "            (0): Conv2d(16, 16, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
            "            (1): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (2): ReLU(inplace=True)\n",
            "            (3): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "            (4): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "          )\n",
            "        )\n",
            "        (1): ConvBlock(\n",
            "          (res_layer): Sequential(\n",
            "            (0): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "            (1): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (2): ReLU(inplace=True)\n",
            "            (3): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "            (4): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "          )\n",
            "        )\n",
            "        (2): ConvBlock(\n",
            "          (res_layer): Sequential(\n",
            "            (0): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "            (1): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (2): ReLU(inplace=True)\n",
            "            (3): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "            (4): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "          )\n",
            "        )\n",
            "      )\n",
            "      (b2_3): Sequential(\n",
            "        (0): ConvBlock(\n",
            "          (shortcut_layer): Sequential(\n",
            "            (0): Conv2d(16, 16, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
            "            (1): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "          )\n",
            "          (res_layer): Sequential(\n",
            "            (0): Conv2d(16, 16, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
            "            (1): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (2): ReLU(inplace=True)\n",
            "            (3): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "            (4): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "          )\n",
            "        )\n",
            "        (1): ConvBlock(\n",
            "          (res_layer): Sequential(\n",
            "            (0): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "            (1): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (2): ReLU(inplace=True)\n",
            "            (3): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "            (4): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "          )\n",
            "        )\n",
            "        (2): ConvBlock(\n",
            "          (res_layer): Sequential(\n",
            "            (0): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "            (1): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (2): ReLU(inplace=True)\n",
            "            (3): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "            (4): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "          )\n",
            "        )\n",
            "      )\n",
            "      (b2_4): Sequential(\n",
            "        (0): ConvBlock(\n",
            "          (shortcut_layer): Sequential(\n",
            "            (0): Conv2d(16, 16, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
            "            (1): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "          )\n",
            "          (res_layer): Sequential(\n",
            "            (0): Conv2d(16, 16, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
            "            (1): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (2): ReLU(inplace=True)\n",
            "            (3): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "            (4): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "          )\n",
            "        )\n",
            "        (1): ConvBlock(\n",
            "          (res_layer): Sequential(\n",
            "            (0): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "            (1): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (2): ReLU(inplace=True)\n",
            "            (3): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "            (4): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "          )\n",
            "        )\n",
            "      )\n",
            "      (b2_5): Sequential(\n",
            "        (0): ConvBlock(\n",
            "          (shortcut_layer): Sequential(\n",
            "            (0): Conv2d(16, 16, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
            "            (1): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "          )\n",
            "          (res_layer): Sequential(\n",
            "            (0): Conv2d(16, 16, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
            "            (1): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (2): ReLU(inplace=True)\n",
            "            (3): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "            (4): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "          )\n",
            "        )\n",
            "        (1): ConvBlock(\n",
            "          (res_layer): Sequential(\n",
            "            (0): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "            (1): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (2): ReLU(inplace=True)\n",
            "            (3): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "            (4): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "          )\n",
            "        )\n",
            "      )\n",
            "      (fpn): FullySepFPN(\n",
            "        (laterals): ModuleList(\n",
            "          (0): Conv(\n",
            "            (conv): Sequential(\n",
            "              (0): Conv2d(16, 16, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "              (1): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "              (2): ReLU(inplace=True)\n",
            "            )\n",
            "          )\n",
            "          (1): Conv(\n",
            "            (conv): Sequential(\n",
            "              (0): Conv2d(16, 16, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "              (1): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "              (2): ReLU(inplace=True)\n",
            "            )\n",
            "          )\n",
            "          (2): Conv(\n",
            "            (conv): Sequential(\n",
            "              (0): Conv2d(16, 16, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "              (1): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "              (2): ReLU(inplace=True)\n",
            "            )\n",
            "          )\n",
            "          (3): Conv(\n",
            "            (conv): Sequential(\n",
            "              (0): Conv2d(16, 16, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "              (1): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "              (2): ReLU(inplace=True)\n",
            "            )\n",
            "          )\n",
            "          (4): Conv(\n",
            "            (conv): Sequential(\n",
            "              (0): Conv2d(16, 16, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "              (1): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "              (2): ReLU(inplace=True)\n",
            "            )\n",
            "          )\n",
            "        )\n",
            "        (itm): ModuleList(\n",
            "          (0): Conv(\n",
            "            (conv): Sequential(\n",
            "              (0): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "              (1): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "              (2): ReLU(inplace=True)\n",
            "            )\n",
            "          )\n",
            "          (1): Conv(\n",
            "            (conv): Sequential(\n",
            "              (0): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "              (1): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "              (2): ReLU(inplace=True)\n",
            "            )\n",
            "          )\n",
            "          (2): Conv(\n",
            "            (conv): Sequential(\n",
            "              (0): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "              (1): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "              (2): ReLU(inplace=True)\n",
            "            )\n",
            "          )\n",
            "          (3): Conv(\n",
            "            (conv): Sequential(\n",
            "              (0): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "              (1): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "              (2): ReLU(inplace=True)\n",
            "            )\n",
            "          )\n",
            "        )\n",
            "        (relu1): ReLU()\n",
            "      )\n",
            "      (FEM_0): CPM3_BN(\n",
            "        (res_branch1): Sequential(\n",
            "          (0): Conv2d(16, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "          (1): BatchNorm2d(8, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "          (2): ReLU(inplace=True)\n",
            "        )\n",
            "        (res_branch2): Sequential(\n",
            "          (0): Conv2d(8, 4, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "          (1): BatchNorm2d(4, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "          (2): ReLU(inplace=True)\n",
            "          (3): Conv2d(4, 4, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "          (4): BatchNorm2d(4, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "          (5): ReLU(inplace=True)\n",
            "        )\n",
            "        (res_branch3): Sequential(\n",
            "          (0): Conv2d(4, 4, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "          (1): BatchNorm2d(4, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "          (2): ReLU(inplace=True)\n",
            "          (3): Conv2d(4, 4, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "          (4): BatchNorm2d(4, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "          (5): ReLU(inplace=True)\n",
            "        )\n",
            "      )\n",
            "      (FEM_1): CPM3_BN(\n",
            "        (res_branch1): Sequential(\n",
            "          (0): Conv2d(16, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "          (1): BatchNorm2d(8, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "          (2): ReLU(inplace=True)\n",
            "        )\n",
            "        (res_branch2): Sequential(\n",
            "          (0): Conv2d(8, 4, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "          (1): BatchNorm2d(4, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "          (2): ReLU(inplace=True)\n",
            "          (3): Conv2d(4, 4, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "          (4): BatchNorm2d(4, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "          (5): ReLU(inplace=True)\n",
            "        )\n",
            "        (res_branch3): Sequential(\n",
            "          (0): Conv2d(4, 4, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "          (1): BatchNorm2d(4, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "          (2): ReLU(inplace=True)\n",
            "          (3): Conv2d(4, 4, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "          (4): BatchNorm2d(4, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "          (5): ReLU(inplace=True)\n",
            "        )\n",
            "      )\n",
            "      (FEM_2): CPM3_BN(\n",
            "        (res_branch1): Sequential(\n",
            "          (0): Conv2d(16, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "          (1): BatchNorm2d(8, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "          (2): ReLU(inplace=True)\n",
            "        )\n",
            "        (res_branch2): Sequential(\n",
            "          (0): Conv2d(8, 4, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "          (1): BatchNorm2d(4, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "          (2): ReLU(inplace=True)\n",
            "          (3): Conv2d(4, 4, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "          (4): BatchNorm2d(4, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "          (5): ReLU(inplace=True)\n",
            "        )\n",
            "        (res_branch3): Sequential(\n",
            "          (0): Conv2d(4, 4, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "          (1): BatchNorm2d(4, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "          (2): ReLU(inplace=True)\n",
            "          (3): Conv2d(4, 4, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "          (4): BatchNorm2d(4, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "          (5): ReLU(inplace=True)\n",
            "        )\n",
            "      )\n",
            "      (FEM_3): CPM3_BN(\n",
            "        (res_branch1): Sequential(\n",
            "          (0): Conv2d(16, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "          (1): BatchNorm2d(8, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "          (2): ReLU(inplace=True)\n",
            "        )\n",
            "        (res_branch2): Sequential(\n",
            "          (0): Conv2d(8, 4, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "          (1): BatchNorm2d(4, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "          (2): ReLU(inplace=True)\n",
            "          (3): Conv2d(4, 4, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "          (4): BatchNorm2d(4, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "          (5): ReLU(inplace=True)\n",
            "        )\n",
            "        (res_branch3): Sequential(\n",
            "          (0): Conv2d(4, 4, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "          (1): BatchNorm2d(4, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "          (2): ReLU(inplace=True)\n",
            "          (3): Conv2d(4, 4, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "          (4): BatchNorm2d(4, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "          (5): ReLU(inplace=True)\n",
            "        )\n",
            "      )\n",
            "      (FEM_4): CPM3_BN(\n",
            "        (res_branch1): Sequential(\n",
            "          (0): Conv2d(16, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "          (1): BatchNorm2d(8, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "          (2): ReLU(inplace=True)\n",
            "        )\n",
            "        (res_branch2): Sequential(\n",
            "          (0): Conv2d(8, 4, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "          (1): BatchNorm2d(4, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "          (2): ReLU(inplace=True)\n",
            "          (3): Conv2d(4, 4, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "          (4): BatchNorm2d(4, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "          (5): ReLU(inplace=True)\n",
            "        )\n",
            "        (res_branch3): Sequential(\n",
            "          (0): Conv2d(4, 4, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "          (1): BatchNorm2d(4, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "          (2): ReLU(inplace=True)\n",
            "          (3): Conv2d(4, 4, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "          (4): BatchNorm2d(4, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "          (5): ReLU(inplace=True)\n",
            "        )\n",
            "      )\n",
            "      (FEM_5): CPM3_BN(\n",
            "        (res_branch1): Sequential(\n",
            "          (0): Conv2d(16, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "          (1): BatchNorm2d(8, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "          (2): ReLU(inplace=True)\n",
            "        )\n",
            "        (res_branch2): Sequential(\n",
            "          (0): Conv2d(8, 4, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "          (1): BatchNorm2d(4, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "          (2): ReLU(inplace=True)\n",
            "          (3): Conv2d(4, 4, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "          (4): BatchNorm2d(4, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "          (5): ReLU(inplace=True)\n",
            "        )\n",
            "        (res_branch3): Sequential(\n",
            "          (0): Conv2d(4, 4, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "          (1): BatchNorm2d(4, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "          (2): ReLU(inplace=True)\n",
            "          (3): Conv2d(4, 4, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "          (4): BatchNorm2d(4, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "          (5): ReLU(inplace=True)\n",
            "        )\n",
            "      )\n",
            "    )\n",
            "  )\n",
            "  (act_loc): ModuleList(\n",
            "    (0): ReLU()\n",
            "    (1): ReLU()\n",
            "    (2): ReLU()\n",
            "    (3): ReLU()\n",
            "    (4): ReLU()\n",
            "    (5): ReLU()\n",
            "  )\n",
            "  (act_conf): ModuleList(\n",
            "    (0): ReLU()\n",
            "    (1): ReLU()\n",
            "    (2): ReLU()\n",
            "    (3): ReLU()\n",
            "    (4): ReLU()\n",
            "    (5): ReLU()\n",
            "  )\n",
            "  (loc): ModuleList(\n",
            "    (0): Conv2d(16, 4, kernel_size=(1, 1), stride=(1, 1))\n",
            "    (1): Conv2d(16, 4, kernel_size=(1, 1), stride=(1, 1))\n",
            "    (2): Conv2d(16, 4, kernel_size=(1, 1), stride=(1, 1))\n",
            "    (3): Conv2d(16, 4, kernel_size=(1, 1), stride=(1, 1))\n",
            "    (4): Conv2d(16, 4, kernel_size=(1, 1), stride=(1, 1))\n",
            "    (5): Conv2d(16, 4, kernel_size=(1, 1), stride=(1, 1))\n",
            "  )\n",
            "  (conf): ModuleList(\n",
            "    (0): Conv2d(16, 4, kernel_size=(1, 1), stride=(1, 1))\n",
            "    (1): Conv2d(16, 2, kernel_size=(1, 1), stride=(1, 1))\n",
            "    (2): Conv2d(16, 2, kernel_size=(1, 1), stride=(1, 1))\n",
            "    (3): Conv2d(16, 2, kernel_size=(1, 1), stride=(1, 1))\n",
            "    (4): Conv2d(16, 2, kernel_size=(1, 1), stride=(1, 1))\n",
            "    (5): Conv2d(16, 2, kernel_size=(1, 1), stride=(1, 1))\n",
            "  )\n",
            "  (softmax): Softmax(dim=-1)\n",
            "  (detect): Detect()\n",
            ")\n",
            "model loading done, ./weights/eresfd_16.pth\n",
            "Traceback (most recent call last):\n",
            "  File \"test_wider.py\", line 468, in <module>\n",
            "    predict_wider(\n",
            "  File \"test_wider.py\", line 248, in predict_wider\n",
            "    img = Image.open(in_file)\n",
            "  File \"/usr/local/lib/python3.8/dist-packages/PIL/Image.py\", line 2843, in open\n",
            "    fp = builtins.open(filename, \"rb\")\n",
            "FileNotFoundError: [Errno 2] No such file or directory: '[./images.npy]/WIDER_val/images/0--Parade/0_Parade_marchingband_1_465.jpg'\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cd .."
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LGNqvjrsf2dp",
        "outputId": "4674f827-c76f-4007-e7cc-bdbdd9f86b6f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/EResFD\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install thop"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OyG5nP9pIZ2y",
        "outputId": "69079fec-95b3-4bad-85a7-a9fec381639f"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting thop\n",
            "  Downloading thop-0.1.1.post2209072238-py3-none-any.whl (15 kB)\n",
            "Requirement already satisfied: torch in /usr/local/lib/python3.8/dist-packages (from thop) (1.13.0+cu116)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.8/dist-packages (from torch->thop) (4.4.0)\n",
            "Installing collected packages: thop\n",
            "Successfully installed thop-0.1.1.post2209072238\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!python --version"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KqHuUt0uS_cS",
        "outputId": "47d7b75f-c2b6-47f2-e3b2-980520eb87c8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Python 3.8.15\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!python3 demo.py --model ./weights/eresfd_16.pth"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AcDZDkxyiac_",
        "outputId": "fce62c71-be54-4daf-c3ca-b6085cfb5fe1"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ANCHOR ratio settings:  [[1.25], [1.25], [1.25], [1.25], [1.25], [1.25]]\n",
            "ANCHOR stride settings:  [4, 8, 16, 32, 64, 128]\n",
            "Detect: self.thresh: 0.050000\n",
            "Detect: self.nms_thresh(IOU-overlap th.): 0.300000\n",
            "Detect: self.nms_top_k: 5000.000000\n",
            "Detect: self.top_k: 750.000000\n",
            "th: 0.050000, rejection rate: 0.001177(0.001177) 130/110415, survived: 110285(110285.000000)\n",
            "elapsed (NMS): 0.219662(0.219662), survived: 110285(110285.000000)\n",
            "detect:./img/0_Parade_marchingband_1_629.jpg timer:0.8847198486328125\n",
            "th: 0.050000, rejection rate: 0.000462(0.000820) 51/110418, survived: 110367(110326.000000)\n",
            "elapsed (NMS): 0.247150(0.233406), survived: 110367(110326.000000)\n",
            "detect:./img/0_Parade_marchingband_1_488.jpg timer:0.6293065547943115\n",
            "th: 0.050000, rejection rate: 0.001141(0.000927) 126/110418, survived: 110292(110314.666667)\n",
            "elapsed (NMS): 0.139080(0.201964), survived: 110292(110314.666667)\n",
            "detect:./img/0_Parade_marchingband_1_234.jpg timer:0.5313186645507812\n",
            "th: 0.050000, rejection rate: 0.000715(0.000874) 79/110415, survived: 110336(110320.000000)\n",
            "elapsed (NMS): 0.128948(0.183710), survived: 110336(110320.000000)\n",
            "detect:./img/0_Parade_marchingband_1_20.jpg timer:0.5034849643707275\n",
            "th: 0.050000, rejection rate: 0.001008(0.000901) 111/110068, survived: 109957(110247.400000)\n",
            "elapsed (NMS): 0.092635(0.165495), survived: 109957(110247.400000)\n",
            "detect:./img/0_Parade_marchingband_1_329.jpg timer:0.45366549491882324\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pwd"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "k7gRPiddf7Zi",
        "outputId": "6ad1d459-fa4b-4ada-8a46-41be5d4eed2b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/EResFD\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cd EResFD"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e82qNYHhgKG1",
        "outputId": "47588405-bb8d-43e7-e2a6-9c286df2947a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/EResFD\n"
          ]
        }
      ]
    }
  ]
}