Copia_de_alibi1.ipynb

{
  "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_alibi1.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "Fl-NAckBZCvb"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from PIL import Image\n",
        "import glob\n",
        "import os\n",
        "import shutil\n",
        "from collections import Counter\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras.layers import Conv2D, Conv2DTranspose, UpSampling2D, Dense, Layer, Reshape, InputLayer, Flatten, Input, MaxPooling2D\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!git clone https://github.com/SeldonIO/alibi-detect.git\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XxQx9zNQZavW",
        "outputId": "a147c3fd-44ad-415e-9ff5-41abd0fc7d7f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'alibi-detect'...\n",
            "remote: Enumerating objects: 4042, done.\u001b[K\n",
            "remote: Counting objects: 100% (4/4), done.\u001b[K\n",
            "remote: Compressing objects: 100% (4/4), done.\u001b[K\n",
            "remote: Total 4042 (delta 0), reused 0 (delta 0), pack-reused 4038\u001b[K\n",
            "Receiving objects: 100% (4042/4042), 26.10 MiB | 30.20 MiB/s, done.\n",
            "Resolving deltas: 100% (2584/2584), done.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%cd /content/alibi-detect/alibi_detect/od"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Haxc9zqlaUlZ",
        "outputId": "7c17bb7b-190b-4e12-d9f5-d50dfa5e8348"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/alibi-detect/alibi_detect/od\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install alibi-detect"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SjGwpKBnaD6h",
        "outputId": "1371f40a-4dec-4910-a79f-ef103b10c939"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "      Successfully uninstalled wrapt-1.13.3\n",
            "  Attempting uninstall: tensorflow-estimator\n",
            "    Found existing installation: tensorflow-estimator 2.7.0\n",
            "    Uninstalling tensorflow-estimator-2.7.0:\n",
            "      Successfully uninstalled tensorflow-estimator-2.7.0\n",
            "  Attempting uninstall: tensorboard\n",
            "    Found existing installation: tensorboard 2.7.0\n",
            "    Uninstalling tensorboard-2.7.0:\n",
            "      Successfully uninstalled tensorboard-2.7.0\n",
            "  Attempting uninstall: keras\n",
            "    Found existing installation: keras 2.7.0\n",
            "    Uninstalling keras-2.7.0:\n",
            "      Successfully uninstalled keras-2.7.0\n",
            "  Attempting uninstall: flatbuffers\n",
            "    Found existing installation: flatbuffers 2.0\n",
            "    Uninstalling flatbuffers-2.0:\n",
            "      Successfully uninstalled flatbuffers-2.0\n",
            "  Attempting uninstall: tensorflow-probability\n",
            "    Found existing installation: tensorflow-probability 0.15.0\n",
            "    Uninstalling tensorflow-probability-0.15.0:\n",
            "      Successfully uninstalled tensorflow-probability-0.15.0\n",
            "  Attempting uninstall: tensorflow\n",
            "    Found existing installation: tensorflow 2.7.0\n",
            "    Uninstalling tensorflow-2.7.0:\n",
            "      Successfully uninstalled tensorflow-2.7.0\n",
            "Successfully installed alibi-detect-0.8.0 clang-5.0 flatbuffers-1.12 huggingface-hub-0.2.1 keras-2.6.0 pyyaml-6.0 sacremoses-0.0.46 tensorboard-2.6.0 tensorflow-2.6.2 tensorflow-estimator-2.6.0 tensorflow-probability-0.12.2 tokenizers-0.10.3 transformers-4.14.1 typing-extensions-3.7.4.3 wrapt-1.12.1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from alibi_detect.od import OutlierAE\n",
        "from alibi_detect.utils.visualize import plot_instance_score, plot_feature_outlier_image"
      ],
      "metadata": {
        "id": "i72rg-awdPJ9"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive"
      ],
      "metadata": {
        "id": "r7O3X1bBe_XT"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "id": "PttdKLmtfL4s",
        "outputId": "e5ad561c-0b70-4a60-f6fe-6bf61066ac41",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "3gfxDil0fNWC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "I1QdiVU5e-2w"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "Q6l5Z7enZCvd"
      },
      "outputs": [],
      "source": [
        "def img_to_np(path, resize = True):  \n",
        "    img_array = []\n",
        "    fpaths = glob.glob(path, recursive=True)\n",
        "    for fname in fpaths:\n",
        "        img = Image.open(fname).convert(\"RGB\")\n",
        "        if(resize): img = img.resize((64,64))\n",
        "        img_array.append(np.asarray(img))\n",
        "    images = np.array(img_array)\n",
        "    return images\n",
        "  \n",
        "path_train = \"/content/drive/MyDrive/data/train/ok.jpg\"\n",
        "path_test = \"/content/drive/MyDrive/data/test/bad.jpg\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "ipNsY6wpZCve"
      },
      "outputs": [],
      "source": [
        "train = img_to_np(path_train)\n",
        "test = img_to_np(path_test)\n",
        "train = train.astype('float32') / 255.\n",
        "test = test.astype('float32') / 255."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "10fzGSHIZCve"
      },
      "outputs": [],
      "source": [
        "encoding_dim = 1024\n",
        "dense_dim = [8, 8, 128]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "Xdh42-tFZCvf"
      },
      "outputs": [],
      "source": [
        "encoder_net = tf.keras.Sequential(\n",
        "  [\n",
        "      InputLayer(input_shape=train[0].shape),\n",
        "      Conv2D(64, 4, strides=2, padding='same', activation=tf.nn.relu),\n",
        "      Conv2D(128, 4, strides=2, padding='same', activation=tf.nn.relu),\n",
        "      Conv2D(512, 4, strides=2, padding='same', activation=tf.nn.relu),\n",
        "      Flatten(),\n",
        "      Dense(encoding_dim,)\n",
        "  ])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "s0jcLzhvZCvf"
      },
      "outputs": [],
      "source": [
        "decoder_net = tf.keras.Sequential(\n",
        "  [\n",
        "      InputLayer(input_shape=(encoding_dim,)),\n",
        "      Dense(np.prod(dense_dim)),\n",
        "      Reshape(target_shape=dense_dim),\n",
        "      Conv2DTranspose(256, 4, strides=2, padding='same', activation=tf.nn.relu),\n",
        "      Conv2DTranspose(64, 4, strides=2, padding='same', activation=tf.nn.relu),\n",
        "      Conv2DTranspose(3, 4, strides=2, padding='same', activation='sigmoid')\n",
        "  ])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "8QD1ARcVZCvf"
      },
      "outputs": [],
      "source": [
        "od = OutlierAE( threshold = 0.001,\n",
        "                encoder_net=encoder_net,\n",
        "                decoder_net=decoder_net)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "typcdEtiZCvg",
        "outputId": "6a0fbdd4-d4a3-4723-df94-9d230457ac53",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/keras/optimizer_v2/optimizer_v2.py:356: UserWarning:\n",
            "\n",
            "The `lr` argument is deprecated, use `learning_rate` instead.\n",
            "\n"
          ]
        }
      ],
      "source": [
        "adam = tf.keras.optimizers.Adam(lr=1e-4)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "QvB3msfGZCvg",
        "outputId": "d3b35e80-172b-445d-f132-8cba0d698c56",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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          ]
        }
      ],
      "source": [
        "od.fit(train, epochs=100, verbose=True,\n",
        "       optimizer = adam)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "KWz2yHntZCvh"
      },
      "outputs": [],
      "source": [
        "od.infer_threshold(test, threshold_perc=95)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "RPVQUw7SZCvh"
      },
      "outputs": [],
      "source": [
        "preds = od.predict(test, outlier_type='instance',\n",
        "            return_instance_score=True,\n",
        "            return_feature_score=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "l8SK4_wQZCvh"
      },
      "outputs": [],
      "source": [
        "for i, fpath in enumerate(glob.glob(path_test)):\n",
        "    if(preds['data']['is_outlier'][i] == 1):\n",
        "        source = fpath\n",
        "        shutil.copy(source, 'img\\\\')\n",
        "        \n",
        "filenames = [os.path.basename(x) for x in glob.glob(path_test, recursive=True)]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "-ohnBYUMZCvi"
      },
      "outputs": [],
      "source": [
        "dict1 = {'Filename': filenames,\n",
        "     'instance_score': preds['data']['instance_score'],\n",
        "     'is_outlier': preds['data']['is_outlier']}\n",
        "     \n",
        "df = pd.DataFrame(dict1)\n",
        "df_outliers = df[df['is_outlier'] == 1]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "_D-3YcqsZCvi",
        "outputId": "6514c10a-b3a1-4cce-b4b0-1417b55a199a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Empty DataFrame\n",
            "Columns: [Filename, instance_score, is_outlier]\n",
            "Index: []\n"
          ]
        }
      ],
      "source": [
        "print(df_outliers)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "qcaDweyqZCvi"
      },
      "outputs": [],
      "source": [
        "recon = od.ae(test).numpy()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "ytpe0XgwZCvj",
        "outputId": "e0021c87-b55a-4092-b2c0-f65608999989",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x1080 with 0 Axes>"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "plot_feature_outlier_image(preds, test, \n",
        "                           X_recon=recon,  \n",
        "                           max_instances=5,\n",
        "                           outliers_only=True,\n",
        "                           figsize=(15,15))"
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.6.4"
    },
    "colab": {
      "name": "Copia de alibi1.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}