Plant Disease Detection (VGG).ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5m8C-ZNuyGX5",
        "outputId": "3d982aa6-6cf6-45fb-f502-e429ab76a4ab"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'/device:GPU:0'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 39
        }
      ],
      "source": [
        "#' ' means CPU whereas '/device:G:0' means GPU\n",
        "import tensorflow as tf\n",
        "tf.test.gpu_device_name()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PvEn6ImO3cxC",
        "outputId": "ee0b431a-f47e-4a99-b787-4d563ba9dd55"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found GPU at: /device:GPU:0\n"
          ]
        }
      ],
      "source": [
        "device_name = tf.test.gpu_device_name()\n",
        "if len(device_name) > 0:\n",
        "    print(\"Found GPU at: {}\".format(device_name))\n",
        "else:\n",
        "    device_name = \"/device:CPU:0\"\n",
        "    print(\"No GPU, using {}.\".format(device_name))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-ub6xnbSyH4l",
        "outputId": "7d4e379f-7870-4394-c4c5-16e61ab7036d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: gputil in /usr/local/lib/python3.8/dist-packages (1.4.0)\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: psutil in /usr/local/lib/python3.8/dist-packages (5.4.8)\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: humanize in /usr/local/lib/python3.8/dist-packages (0.5.1)\n",
            "Gen RAM Free: 11.5 GB  | Proc size: 1.3 GB\n",
            "GPU RAM Free: 14799MB | Used: 310MB | Util   2% | Total 15360MB\n"
          ]
        }
      ],
      "source": [
        "# memory footprint support libraries/code\n",
        "!ln -sf /opt/bin/nvidia-smi /usr/bin/nvidia-smi\n",
        "!pip install gputil\n",
        "!pip install psutil\n",
        "!pip install humanize\n",
        "import psutil\n",
        "import humanize\n",
        "import os\n",
        "import GPUtil as GPU\n",
        "GPUs = GPU.getGPUs()\n",
        "# XXX: only one GPU on Colab and isn’t guaranteed\n",
        "gpu = GPUs[0]\n",
        "def printm():\n",
        " process = psutil.Process(os.getpid())\n",
        " print(\"Gen RAM Free: \" + humanize.naturalsize( psutil.virtual_memory().available ), \" | Proc size: \" + humanize.naturalsize( process.memory_info().rss))\n",
        " print(\"GPU RAM Free: {0:.0f}MB | Used: {1:.0f}MB | Util {2:3.0f}% | Total {3:.0f}MB\".format(gpu.memoryFree, gpu.memoryUsed, gpu.memoryUtil*100, gpu.memoryTotal))\n",
        "printm() "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "gSYT19hNyXsT"
      },
      "outputs": [],
      "source": [
        "# !kill -9 -1"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QDq9XXRUnbOj",
        "outputId": "57368586-44a4-40bc-abdc-d379d69b9736"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "vftneHTGnrBP"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "import cv2\n",
        "from PIL import Image\n",
        "import tensorflow as tf\n",
        "from keras import backend as K\n",
        "from keras.models import load_model\n",
        "from tensorflow.keras.optimizers import Adam, RMSprop\n",
        "from tensorflow.keras.callbacks import ReduceLROnPlateau\n",
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DqDucHTWtbIT",
        "outputId": "0fee1cdf-3ef6-41b0-abd1-5e6bcaef4afa"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "ERROR:root:Internal Python error in the inspect module.\n",
            "Below is the traceback from this internal error.\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.8/dist-packages/IPython/core/interactiveshell.py\", line 3326, in run_code\n",
            "    exec(code_obj, self.user_global_ns, self.user_ns)\n",
            "  File \"<ipython-input-44-c58fb5024c45>\", line 1, in <module>\n",
            "    get_ipython().run_line_magic('cd', \"'drive/MyDrive/AgriWise/ML/plant diseases'\")\n",
            "  File \"/usr/local/lib/python3.8/dist-packages/IPython/core/interactiveshell.py\", line 2314, in run_line_magic\n",
            "    result = fn(*args, **kwargs)\n",
            "  File \"<decorator-gen-85>\", line 2, in cd\n",
            "  File \"/usr/local/lib/python3.8/dist-packages/IPython/core/magic.py\", line 187, in <lambda>\n",
            "    call = lambda f, *a, **k: f(*a, **k)\n",
            "  File \"/usr/local/lib/python3.8/dist-packages/IPython/core/magics/osm.py\", line 334, in cd\n",
            "    oldcwd = os.getcwd()\n",
            "OSError: [Errno 107] Transport endpoint is not connected\n",
            "\n",
            "During handling of the above exception, another exception occurred:\n",
            "\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.8/dist-packages/IPython/core/interactiveshell.py\", line 2040, in showtraceback\n",
            "    stb = value._render_traceback_()\n",
            "AttributeError: 'OSError' object has no attribute '_render_traceback_'\n",
            "\n",
            "During handling of the above exception, another exception occurred:\n",
            "\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.8/dist-packages/IPython/core/ultratb.py\", line 1101, in get_records\n",
            "    return _fixed_getinnerframes(etb, number_of_lines_of_context, tb_offset)\n",
            "  File \"/usr/local/lib/python3.8/dist-packages/IPython/core/ultratb.py\", line 319, in wrapped\n",
            "    return f(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.8/dist-packages/IPython/core/ultratb.py\", line 353, in _fixed_getinnerframes\n",
            "    records = fix_frame_records_filenames(inspect.getinnerframes(etb, context))\n",
            "  File \"/usr/lib/python3.8/inspect.py\", line 1515, in getinnerframes\n",
            "    frameinfo = (tb.tb_frame,) + getframeinfo(tb, context)\n",
            "  File \"/usr/lib/python3.8/inspect.py\", line 1473, in getframeinfo\n",
            "    filename = getsourcefile(frame) or getfile(frame)\n",
            "  File \"/usr/lib/python3.8/inspect.py\", line 708, in getsourcefile\n",
            "    if getattr(getmodule(object, filename), '__loader__', None) is not None:\n",
            "  File \"/usr/lib/python3.8/inspect.py\", line 737, in getmodule\n",
            "    file = getabsfile(object, _filename)\n",
            "  File \"/usr/lib/python3.8/inspect.py\", line 721, in getabsfile\n",
            "    return os.path.normcase(os.path.abspath(_filename))\n",
            "  File \"/usr/lib/python3.8/posixpath.py\", line 379, in abspath\n",
            "    cwd = os.getcwd()\n",
            "OSError: [Errno 107] Transport endpoint is not connected\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "OSError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m"
          ]
        }
      ],
      "source": [
        "cd 'drive/MyDrive/AgriWise/ML/plant diseases'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "CAuE-MYD0-MR"
      },
      "outputs": [],
      "source": [
        "# gpus = tf.config.experimental.list_physical_devices('GPU')\n",
        "\n",
        "# for gpu in gpus:\n",
        "#   tf.experimental.set_memory_growth(gpu, True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FJl91iR6hNpo"
      },
      "source": [
        "# Preprocessing "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jbXtEcPNh-5p"
      },
      "outputs": [],
      "source": [
        "batch_size = 7"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wuDLMiS-tpvk",
        "outputId": "fb22d97f-1663-4e99-b291-28c03e888255"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found 37077 files belonging to 25 classes.\n",
            "Found 9282 files belonging to 25 classes.\n",
            "Found 11588 files belonging to 25 classes.\n"
          ]
        }
      ],
      "source": [
        "root_dir = \"/content/drive/MyDrive/AgriWise/ML/plant diseases/fiveClasses/\"\n",
        "\n",
        "train_set = tf.keras.utils.image_dataset_from_directory(\n",
        "    root_dir + \"train\", \n",
        "    labels='inferred',\n",
        "    label_mode = 'categorical',\n",
        "    batch_size=batch_size,\n",
        "    image_size=(256, 256),\n",
        "    seed = 42)\n",
        "\n",
        "val_set = tf.keras.utils.image_dataset_from_directory(\n",
        "    root_dir + \"val\", \n",
        "    labels='inferred',\n",
        "    label_mode = 'categorical',\n",
        "    batch_size=batch_size,\n",
        "    image_size=(256, 256),\n",
        "    seed = 42)\n",
        "\n",
        "test_set = tf.keras.utils.image_dataset_from_directory(\n",
        "    root_dir + \"test\", \n",
        "    labels='inferred',\n",
        "    label_mode = 'categorical',\n",
        "    batch_size=batch_size,\n",
        "    image_size=(256, 256),\n",
        "    seed = 42)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "t-PDPSi4SScn"
      },
      "outputs": [],
      "source": [
        "normalization_layer = tf.keras.layers.experimental.preprocessing.Rescaling(1./256)\n",
        "normalized_training_set = train_set.map(lambda x, y: (normalization_layer(x), y))\n",
        "training_image_batch, training_labels_batch = next(iter(normalized_training_set))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "kaCSelhOS_m-"
      },
      "outputs": [],
      "source": [
        "normalized_val_set = val_set.map(lambda x, y: (normalization_layer(x), y))\n",
        "val_image_batch, val_labels_batch = next(iter(normalized_val_set))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-Gg_TdFYRtuT"
      },
      "source": [
        "# VGG Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ApKXee6On4QV"
      },
      "outputs": [],
      "source": [
        "# keep include_top false to retrain it with new classes \n",
        "pre_trained_model = tf.keras.applications.VGG16(input_shape=(256, 256, 3), include_top=False, weights=\"imagenet\", classifier_activation=\"softmax\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "37KeTaeRogFu",
        "outputId": "b87a1804-48a9-49a1-ebe1-fb1d658fb336"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "input_3\n",
            "block1_conv1\n",
            "block1_conv2\n",
            "block1_pool\n",
            "block2_conv1\n",
            "block2_conv2\n",
            "block2_pool\n",
            "block3_conv1\n",
            "block3_conv2\n",
            "block3_conv3\n",
            "block3_pool\n",
            "block4_conv1\n",
            "block4_conv2\n",
            "block4_conv3\n",
            "block4_pool\n",
            "block5_conv1\n",
            "block5_conv2\n",
            "block5_conv3\n",
            "block5_pool\n"
          ]
        }
      ],
      "source": [
        "for layer in pre_trained_model.layers:\n",
        "  print(layer.name)\n",
        "  layer.trainable = False"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 755
        },
        "id": "WtuBhc0LoqnA",
        "outputId": "05743111-8319-4f1a-932d-892272ea08bd"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "ERROR:root:Internal Python error in the inspect module.\n",
            "Below is the traceback from this internal error.\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.8/dist-packages/IPython/core/interactiveshell.py\", line 3326, in run_code\n",
            "    exec(code_obj, self.user_global_ns, self.user_ns)\n",
            "  File \"<ipython-input-53-ca63462ee3c8>\", line 4, in <module>\n",
            "    x = tf.keras.layers.Dense(512, activity_regularizer = regularizers.L2 (1e-5), activation='relu')(x)\n",
            "NameError: name 'regularizers' is not defined\n",
            "\n",
            "During handling of the above exception, another exception occurred:\n",
            "\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.8/dist-packages/IPython/core/interactiveshell.py\", line 2040, in showtraceback\n",
            "    stb = value._render_traceback_()\n",
            "AttributeError: 'NameError' object has no attribute '_render_traceback_'\n",
            "\n",
            "During handling of the above exception, another exception occurred:\n",
            "\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.8/dist-packages/IPython/core/ultratb.py\", line 1101, in get_records\n",
            "    return _fixed_getinnerframes(etb, number_of_lines_of_context, tb_offset)\n",
            "  File \"/usr/local/lib/python3.8/dist-packages/IPython/core/ultratb.py\", line 319, in wrapped\n",
            "    return f(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.8/dist-packages/IPython/core/ultratb.py\", line 353, in _fixed_getinnerframes\n",
            "    records = fix_frame_records_filenames(inspect.getinnerframes(etb, context))\n",
            "  File \"/usr/lib/python3.8/inspect.py\", line 1515, in getinnerframes\n",
            "    frameinfo = (tb.tb_frame,) + getframeinfo(tb, context)\n",
            "  File \"/usr/lib/python3.8/inspect.py\", line 1473, in getframeinfo\n",
            "    filename = getsourcefile(frame) or getfile(frame)\n",
            "  File \"/usr/lib/python3.8/inspect.py\", line 708, in getsourcefile\n",
            "    if getattr(getmodule(object, filename), '__loader__', None) is not None:\n",
            "  File \"/usr/lib/python3.8/inspect.py\", line 737, in getmodule\n",
            "    file = getabsfile(object, _filename)\n",
            "  File \"/usr/lib/python3.8/inspect.py\", line 721, in getabsfile\n",
            "    return os.path.normcase(os.path.abspath(_filename))\n",
            "  File \"/usr/lib/python3.8/posixpath.py\", line 379, in abspath\n",
            "    cwd = os.getcwd()\n",
            "OSError: [Errno 107] Transport endpoint is not connected\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m"
          ]
        }
      ],
      "source": [
        "last_layer = pre_trained_model.get_layer('block5_pool')\n",
        "last_output = last_layer.output\n",
        "x = tf.keras.layers.GlobalMaxPooling2D()(last_output)\n",
        "x = tf.keras.layers.Dense(512, activity_regularizer = regularizers.L2 (1e-5), activation='relu')(x)\n",
        "x = tf.keras.layers.Dropout(0.5)(x)\n",
        "x = tf.keras.layers.Dense(25, activity_regularizer = regularizers.L2 (1e-5), activation='softmax')(x)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "gKDXzf0L4Im8"
      },
      "outputs": [],
      "source": [
        "with tf.device(device_name):\n",
        "    model = tf.keras.Model(pre_trained_model.input, x)      # Create a TensorFlow model.\n",
        "    model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['acc']) # Compile the model on the GPU.\n",
        "    model.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "mkRGYhdE4WKw"
      },
      "outputs": [],
      "source": [
        "vgg_clf = model.fit(normalized_training_set,\n",
        "epochs = 50,\n",
        "validation_data=normalized_val_set,\n",
        "batch_size = batch_size,\n",
        "verbose = 1)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "07E9KG5AIbLd"
      },
      "source": [
        "## Evaluate the model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "GJHIzNcSISeB"
      },
      "outputs": [],
      "source": [
        "result = model.evaluate(test_set, batch_size=batch_size)\n",
        "print(\"test_loss, test accuracy\",result)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "T4tBpAYWIgFQ"
      },
      "source": [
        "## Save the model\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0f2wY2-NIStB"
      },
      "outputs": [],
      "source": [
        "model_json = model.to_json()\n",
        "with open(\"/content/drive/MyDrive/Image Classification/VGG_Skin_Classifier.json\", \"w\") as json_file:\n",
        "  json_file.write(model_json)\n",
        "  model.save(\"/content/drive/MyDrive/Image Classification/VGG_Skin_Classifier.h5\")\n",
        "  print(\"Saved model to disk\")\n",
        "  model.save_weights(\"/content/drive/MyDrive/Image Classification/VGG_Skin.h5\")"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "provenance": []
    },
    "gpuClass": "standard",
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}