AST_GPU.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "T4",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "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/AST_GPU.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": "03898634-19ce-4a77-cf37-d8d4297f953b"
      },
      "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": "9c08a128-6beb-4664-aadc-0b1f47576938"
      },
      "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": "104a49bd-197e-4147-9c1b-a463f48a4900"
      },
      "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",
            "Unpacking objects: 100% (45/45), 25.14 KiB | 1.93 MiB/s, 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": "98a93802-5789-4212-b05a-ff484a3a0ed0"
      },
      "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": "b7f2868f-b110-4b48-d550-77ee0a910558"
      },
      "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)) (1.22.4)\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)) (4.65.0)\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)) (2023.4.12)\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 torch"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6gAgPGRVQK02",
        "outputId": "517c44d9-6e1d-4fe6-d1f5-8be5b9b21502"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: torch in /usr/local/lib/python3.10/dist-packages (2.0.1+cu118)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from torch) (3.12.2)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.10/dist-packages (from torch) (4.6.3)\n",
            "Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch) (1.11.1)\n",
            "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch) (3.1)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch) (3.1.2)\n",
            "Requirement already satisfied: triton==2.0.0 in /usr/local/lib/python3.10/dist-packages (from torch) (2.0.0)\n",
            "Requirement already satisfied: cmake in /usr/local/lib/python3.10/dist-packages (from triton==2.0.0->torch) (3.25.2)\n",
            "Requirement already satisfied: lit in /usr/local/lib/python3.10/dist-packages (from triton==2.0.0->torch) (16.0.6)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch) (2.1.3)\n",
            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch) (1.3.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install pillow"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-B8iu7gGhJqb",
        "outputId": "148efc7e-1d27-47d5-f674-650a2a941c11"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: pillow in /usr/local/lib/python3.10/dist-packages (8.4.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "import torchvision"
      ],
      "metadata": {
        "id": "qRmUp6mKgV0S"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install --upgrade efficientnet-pytorch"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gbKImy8ahQiF",
        "outputId": "fc26fd01-866a-4023-ff84-1775291a183d"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting efficientnet-pytorch\n",
            "  Using cached efficientnet_pytorch-0.7.1.tar.gz (21 kB)\n",
            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\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) (4.6.3)\n",
            "Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch->efficientnet-pytorch) (1.11.1)\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) (3.25.2)\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",
            "Building wheels for collected packages: efficientnet-pytorch\n",
            "  Building wheel for efficientnet-pytorch (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for efficientnet-pytorch: filename=efficientnet_pytorch-0.7.1-py3-none-any.whl size=16427 sha256=4899a1489967378f89f6a3d30a3b1f8793b4e04e6c8d6d3799c7c436b2f093a8\n",
            "  Stored in directory: /root/.cache/pip/wheels/03/3f/e9/911b1bc46869644912bda90a56bcf7b960f20b5187feea3baf\n",
            "Successfully built efficientnet-pytorch\n",
            "Installing collected packages: efficientnet-pytorch\n",
            "Successfully installed efficientnet-pytorch-0.7.1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!python preprocess.py"
      ],
      "metadata": {
        "id": "50jWt6-HNla4",
        "outputId": "25701864-7230-4651-f1f5-f73cdd004170",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "my_experiment\n",
            "Downloading: \"https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/efficientnet-b5-b6417697.pth\" to /root/.cache/torch/hub/checkpoints/efficientnet-b5-b6417697.pth\n",
            "100% 117M/117M [00:07<00:00, 16.3MB/s]\n",
            "Loaded pretrained weights for efficientnet-b5\n",
            "bottle\n",
            "100% 27/27 [00:30<00:00,  1.14s/it]\n",
            "data/features/bottle\n",
            "100% 6/6 [01:14<00:00, 12.41s/it]\n",
            "data/features/bottle\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!python train_teacher.py"
      ],
      "metadata": {
        "id": "EjiiWlgMNpw1",
        "outputId": "cad19709-d4c9-430a-d6a6-e2d398252c04",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "my_experiment\n",
            "\n",
            "\n",
            "Train class bottle\n",
            "\n",
            "Train epoch 0\n",
            "Epoch: 0.0 \t train loss: 2684.2170\n",
            "Epoch: 0.4 \t train loss: 523.1699\n",
            "Epoch: 0.8 \t train loss: 471.0775\n",
            "Epoch: 0.12 \t train loss: 425.8354\n",
            "Epoch: 0.16 \t train loss: 376.8799\n",
            "Epoch: 0.20 \t train loss: 328.0721\n",
            "\n",
            "Compute loss and scores on test set:\n",
            "Epoch: 0 \t test_loss: 379.6383\n",
            "AUROC mean over maps: \t last: 98.97 \t best: 98.97\n",
            "AUROC  max over maps: \t last: 95.16 \t best: 95.16\n",
            "\n",
            "Train epoch 1\n",
            "Epoch: 1.0 \t train loss: 285.1472\n",
            "Epoch: 1.4 \t train loss: 251.2624\n",
            "Epoch: 1.8 \t train loss: 226.4112\n",
            "Epoch: 1.12 \t train loss: 205.3142\n",
            "Epoch: 1.16 \t train loss: 189.3883\n",
            "Epoch: 1.20 \t train loss: 175.7590\n",
            "\n",
            "Compute loss and scores on test set:\n",
            "Epoch: 1 \t test_loss: 323.1492\n",
            "AUROC mean over maps: \t last: 98.81 \t best: 98.97\n",
            "AUROC  max over maps: \t last: 97.78 \t best: 97.78\n",
            "\n",
            "Train epoch 2\n",
            "Epoch: 2.0 \t train loss: 164.9021\n",
            "Epoch: 2.4 \t train loss: 154.7758\n",
            "Epoch: 2.8 \t train loss: 146.9374\n",
            "Epoch: 2.12 \t train loss: 138.1286\n",
            "Epoch: 2.16 \t train loss: 131.5252\n",
            "Epoch: 2.20 \t train loss: 124.0949\n",
            "\n",
            "Compute loss and scores on test set:\n",
            "Epoch: 2 \t test_loss: 307.9567\n",
            "AUROC mean over maps: \t last: 98.65 \t best: 98.97\n",
            "AUROC  max over maps: \t last: 96.35 \t best: 97.78\n",
            "student saved\n",
            "\n",
            "AUROC % after last epoch\n",
            "\tmean over maps: 98.65 \t max over maps: 96.35\n",
            "best AUROC %\n",
            "\tmean over maps: 98.97 \t max over maps: 97.78\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!python train_student.py"
      ],
      "metadata": {
        "id": "-um9MkFVNu3w",
        "outputId": "48b4a0d8-144c-493a-a9a7-5645d88bc0d4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "my_experiment\n",
            "\n",
            "\n",
            "Train class bottle\n",
            "\n",
            "Train epoch 0\n",
            "Epoch: 0.0 \t train loss: 2.1736\n",
            "Epoch: 0.4 \t train loss: 0.8693\n",
            "Epoch: 0.8 \t train loss: 0.5329\n",
            "Epoch: 0.12 \t train loss: 0.3741\n",
            "Epoch: 0.16 \t train loss: 0.2931\n",
            "Epoch: 0.20 \t train loss: 0.2456\n",
            "\n",
            "Compute loss and scores on test set:\n",
            "Epoch: 0 \t test_loss: 2.3285\n",
            "AUROC mean over maps: \t last: 99.76 \t best: 99.76\n",
            "AUROC  max over maps: \t last: 98.89 \t best: 98.89\n",
            "\n",
            "Train epoch 1\n",
            "Epoch: 1.0 \t train loss: 0.2060\n",
            "Epoch: 1.4 \t train loss: 0.1833\n",
            "Epoch: 1.8 \t train loss: 0.1675\n",
            "Epoch: 1.12 \t train loss: 0.1473\n",
            "Epoch: 1.16 \t train loss: 0.1309\n",
            "Epoch: 1.20 \t train loss: 0.1208\n",
            "\n",
            "Compute loss and scores on test set:\n",
            "Epoch: 1 \t test_loss: 2.9534\n",
            "AUROC mean over maps: \t last: 100.00 \t best: 100.00\n",
            "AUROC  max over maps: \t last: 99.21 \t best: 99.21\n",
            "\n",
            "Train epoch 2\n",
            "Epoch: 2.0 \t train loss: 0.1180\n",
            "Epoch: 2.4 \t train loss: 0.1057\n",
            "Epoch: 2.8 \t train loss: 0.1031\n",
            "Epoch: 2.12 \t train loss: 0.0911\n",
            "Epoch: 2.16 \t train loss: 0.0857\n",
            "Epoch: 2.20 \t train loss: 0.0924\n",
            "\n",
            "Compute loss and scores on test set:\n",
            "Epoch: 2 \t test_loss: 2.9915\n",
            "AUROC mean over maps: \t last: 100.00 \t best: 100.00\n",
            "AUROC  max over maps: \t last: 99.29 \t best: 99.29\n",
            "student saved\n",
            "\n",
            "AUROC % after last epoch\n",
            "\tmean over maps: 100.00 \t max over maps: 99.29\n",
            "best AUROC %\n",
            "\tmean over maps: 100.00 \t max over maps: 99.29\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# antes de evaluar, modificar las fotos de las subcarpetas de test\n",
        ".\n"
      ],
      "metadata": {
        "id": "TcD1z5iaYx40"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!python eval.py"
      ],
      "metadata": {
        "id": "tKThe2qxI5bc",
        "outputId": "5cdb798c-d4d6-4fa0-b9fa-d52946e7984e",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "my_experiment\n",
            "\n",
            "Evaluate class bottle\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"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# !python train_teacher.py\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "W9-efaXElCPE"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}