Copia_de_Copia_de_AST.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "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/danielsoy/ALOCC-CVPR2018/blob/master/Copia_de_Copia_de_AST.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "https://github.com/danielsoy/AST"
      ],
      "metadata": {
        "id": "fwSzIRD5WuTs"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install pip"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yF8Dz3dudvrz",
        "outputId": "25cdf542-d7f3-498d-a418-40a9a026809a"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: pip in /usr/local/lib/python3.10/dist-packages (23.1.2)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive',force_remount=True)"
      ],
      "metadata": {
        "id": "TxS3z_oQB0AD",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "882add2b-7741-4de6-f00e-5ad02f7b9cac"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "yLXKg-zlWUxC",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "cce2ff86-f814-450d-f1b1-17abf14fe35c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'AST'...\n",
            "remote: Enumerating objects: 45, done.\u001b[K\n",
            "remote: Counting objects: 100% (45/45), done.\u001b[K\n",
            "remote: Compressing objects: 100% (44/44), done.\u001b[K\n",
            "remote: Total 45 (delta 21), reused 2 (delta 0), pack-reused 0\u001b[K\n",
            "Receiving objects: 100% (45/45), 25.16 KiB | 2.52 MiB/s, done.\n",
            "Resolving deltas: 100% (21/21), done.\n"
          ]
        }
      ],
      "source": [
        "!git clone https://github.com/danielsoy/AST.git"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%cd /content/AST"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AV2Wtcv8eGCq",
        "outputId": "2c14555f-7be5-4aa8-80c9-f4ac09be19f5"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/AST\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install -r requirements.txt"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZKSFNh1BeKXR",
        "outputId": "eadcd1c0-19ad-4326-f1e6-9af30bdc856a"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: scikit-learn>=0.24.2 in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 1)) (1.2.2)\n",
            "Requirement already satisfied: scipy>=1.7.1 in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 2)) (1.10.1)\n",
            "Requirement already satisfied: numpy>=1.21.2 in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 3)) (1.23.5)\n",
            "Requirement already satisfied: torch>=1.9.1 in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 4)) (2.0.1+cu118)\n",
            "Requirement already satisfied: torchvision>=0.10.1 in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 5)) (0.15.2+cu118)\n",
            "Requirement already satisfied: matplotlib>=3.4.2 in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 6)) (3.7.1)\n",
            "Requirement already satisfied: tqdm>=4.62.2 in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 7)) (4.66.1)\n",
            "Collecting efficientnet-pytorch>=0.6.3 (from -r requirements.txt (line 8))\n",
            "  Downloading efficientnet_pytorch-0.7.1.tar.gz (21 kB)\n",
            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Requirement already satisfied: tifffile>=2021.11.2 in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 9)) (2023.8.30)\n",
            "\u001b[31mERROR: Could not find a version that satisfies the requirement PIL>=7.1.2 (from versions: none)\u001b[0m\u001b[31m\n",
            "\u001b[0m\u001b[31mERROR: No matching distribution found for PIL>=7.1.2\u001b[0m\u001b[31m\n",
            "\u001b[0m"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install pillow"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-B8iu7gGhJqb",
        "outputId": "99d1f5dc-efd0-47bf-ff8f-233149170e11"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: pillow in /usr/local/lib/python3.10/dist-packages (9.4.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "import torchvision"
      ],
      "metadata": {
        "id": "qRmUp6mKgV0S"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install --upgrade efficientnet-pytorch"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gbKImy8ahQiF",
        "outputId": "7a359b2c-697e-47f0-bc04-e781d3dfcf50"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: efficientnet-pytorch in /usr/local/lib/python3.10/dist-packages (0.7.1)\n",
            "Requirement already satisfied: torch in /usr/local/lib/python3.10/dist-packages (from efficientnet-pytorch) (2.0.1+cu118)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from torch->efficientnet-pytorch) (3.12.2)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.10/dist-packages (from torch->efficientnet-pytorch) (4.5.0)\n",
            "Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch->efficientnet-pytorch) (1.12)\n",
            "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch->efficientnet-pytorch) (3.1)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch->efficientnet-pytorch) (3.1.2)\n",
            "Requirement already satisfied: triton==2.0.0 in /usr/local/lib/python3.10/dist-packages (from torch->efficientnet-pytorch) (2.0.0)\n",
            "Requirement already satisfied: cmake in /usr/local/lib/python3.10/dist-packages (from triton==2.0.0->torch->efficientnet-pytorch) (3.27.4.1)\n",
            "Requirement already satisfied: lit in /usr/local/lib/python3.10/dist-packages (from triton==2.0.0->torch->efficientnet-pytorch) (16.0.6)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch->efficientnet-pytorch) (2.1.3)\n",
            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch->efficientnet-pytorch) (1.3.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!python preprocess.py\n"
      ],
      "metadata": {
        "id": "-Ccod9JU1ERN",
        "outputId": "e3ca20c2-84f0-4a99-9e32-71da2fd690d6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "my_experiment\n",
            "Loaded pretrained weights for efficientnet-b5\n",
            "Traceback (most recent call last):\n",
            "  File \"/content/AST/preprocess.py\", line 106, in <module>\n",
            "    extract_image_features(c.dataset_dir)\n",
            "  File \"/content/AST/preprocess.py\", line 81, in extract_image_features\n",
            "    model.to(c.device)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\", line 1145, in to\n",
            "    return self._apply(convert)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\", line 797, in _apply\n",
            "    module._apply(fn)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\", line 797, in _apply\n",
            "    module._apply(fn)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\", line 820, in _apply\n",
            "    param_applied = fn(param)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\", line 1143, in convert\n",
            "    return t.to(device, dtype if t.is_floating_point() or t.is_complex() else None, non_blocking)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/torch/cuda/__init__.py\", line 247, in _lazy_init\n",
            "    torch._C._cuda_init()\n",
            "RuntimeError: Found no NVIDIA driver on your system. Please check that you have an NVIDIA GPU and installed a driver from http://www.nvidia.com/Download/index.aspx\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "!python train_teacher.py\n"
      ],
      "metadata": {
        "id": "l54JwwVn1GkK",
        "outputId": "a70c5081-2008-457b-e90b-2f53f2745587",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "my_experiment\n",
            "/usr/local/lib/python3.10/dist-packages/numpy/core/fromnumeric.py:3432: RuntimeWarning: Mean of empty slice.\n",
            "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
            "/usr/local/lib/python3.10/dist-packages/numpy/core/_methods.py:190: RuntimeWarning: invalid value encountered in double_scalars\n",
            "  ret = ret.dtype.type(ret / rcount)\n",
            "\n",
            "AUROC % after last epoch\n",
            "\tmean over maps: nan \t max over maps: nan\n",
            "best AUROC %\n",
            "\tmean over maps: nan \t max over maps: nan\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "!python train_student.py"
      ],
      "metadata": {
        "id": "ltBLKMZ81HD1",
        "outputId": "3a0b4ded-5da8-4368-ee29-c6b7f4995107",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "my_experiment\n",
            "/usr/local/lib/python3.10/dist-packages/numpy/core/fromnumeric.py:3432: RuntimeWarning: Mean of empty slice.\n",
            "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
            "/usr/local/lib/python3.10/dist-packages/numpy/core/_methods.py:190: RuntimeWarning: invalid value encountered in double_scalars\n",
            "  ret = ret.dtype.type(ret / rcount)\n",
            "\n",
            "AUROC % after last epoch\n",
            "\tmean over maps: nan \t max over maps: nan\n",
            "best AUROC %\n",
            "\tmean over maps: nan \t max over maps: nan\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!python eval.py"
      ],
      "metadata": {
        "id": "tKThe2qxI5bc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a6195d4a-92b8-4b68-80e6-cc5fe9b9a56a"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "my_experiment\n",
            "/usr/local/lib/python3.10/dist-packages/numpy/core/fromnumeric.py:3432: RuntimeWarning: Mean of empty slice.\n",
            "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
            "/usr/local/lib/python3.10/dist-packages/numpy/core/_methods.py:190: RuntimeWarning: invalid value encountered in double_scalars\n",
            "  ret = ret.dtype.type(ret / rcount)\n",
            "\n",
            "mean AUROC % over all classes\n",
            "\tmean over maps: nan \t max over maps: nan\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "from google.colab import files\n",
        "\n",
        "!zip -r /content/AZT-viz.zip /content/AST/viz\n",
        "\n",
        "files.download('/content/AZT-viz.zip')"
      ],
      "metadata": {
        "id": "VG6MOOpZlB6O",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 421
        },
        "outputId": "adff4951-bc42-49f3-d7b3-576fa11ede96"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\tzip warning: name not matched: /content/AST/viz\n",
            "\n",
            "zip error: Nothing to do! (try: zip -r /content/AZT-viz.zip . -i /content/AST/viz)\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "FileNotFoundError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-15-219b6479f4f3>\u001b[0m in \u001b[0;36m<cell line: 5>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mget_ipython\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msystem\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'zip -r /content/AZT-viz.zip /content/AST/viz'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mfiles\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdownload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'/content/AZT-viz.zip'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/google/colab/files.py\u001b[0m in \u001b[0;36mdownload\u001b[0;34m(filename)\u001b[0m\n\u001b[1;32m    223\u001b[0m   \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0m_os\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexists\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    224\u001b[0m     \u001b[0mmsg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'Cannot find file: {}'\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 225\u001b[0;31m     \u001b[0;32mraise\u001b[0m \u001b[0mFileNotFoundError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmsg\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# pylint: disable=undefined-variable\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    226\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    227\u001b[0m   \u001b[0mcomm_manager\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_IPython\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_ipython\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkernel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcomm_manager\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mFileNotFoundError\u001b[0m: Cannot find file: /content/AZT-viz.zip"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# !python train_teacher.py\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "W9-efaXElCPE"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}