ROCT_Net_without_Capsules_Kermany_Dataset.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "ROCT-Net_without Capsules_Kermany Dataset.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "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/mr7495/OCT-classification/blob/main/ROCT_Net_without_Capsules_Kermany_Dataset.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IldnlMR_t1BM"
      },
      "source": [
        "!nvidia-smi"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DzQOkYDRaizC",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "13b9bf43-6a0c-4df5-e54f-f2621b2894ef"
      },
      "source": [
        "pip install tensorflow_addons"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting tensorflow_addons\n",
            "  Downloading tensorflow_addons-0.16.1-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (1.1 MB)\n",
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            "\u001b[?25hRequirement already satisfied: typeguard>=2.7 in /usr/local/lib/python3.7/dist-packages (from tensorflow_addons) (2.7.1)\n",
            "Installing collected packages: tensorflow-addons\n",
            "Successfully installed tensorflow-addons-0.16.1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RgrUHaGTuM-8"
      },
      "source": [
        "import numpy as np\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras import layers\n",
        "import cv2\n",
        "import zipfile\n",
        "import shutil\n",
        "import random\n",
        "import pandas as pd\n",
        "import csv\n",
        "import os\n",
        "import tensorflow as tf"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yqjUPmNDjH1w",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c19f62f1-ac36-4b68-ee97-2578a2939751"
      },
      "source": [
        "!wget -cO - 'https://md-datasets-cache-zipfiles-prod.s3.eu-west-1.amazonaws.com/rscbjbr9sj-3.zip' > data.zip"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2022-03-03 23:16:50--  https://md-datasets-cache-zipfiles-prod.s3.eu-west-1.amazonaws.com/rscbjbr9sj-3.zip\n",
            "Resolving md-datasets-cache-zipfiles-prod.s3.eu-west-1.amazonaws.com (md-datasets-cache-zipfiles-prod.s3.eu-west-1.amazonaws.com)... 52.218.41.184\n",
            "Connecting to md-datasets-cache-zipfiles-prod.s3.eu-west-1.amazonaws.com (md-datasets-cache-zipfiles-prod.s3.eu-west-1.amazonaws.com)|52.218.41.184|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 8432836958 (7.9G) [application/octet-stream]\n",
            "Saving to: ‘STDOUT’\n",
            "\n",
            "-                   100%[===================>]   7.85G  21.6MB/s    in 6m 17s  \n",
            "\n",
            "2022-03-03 23:23:07 (21.4 MB/s) - written to stdout [8432836958/8432836958]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kTl5cmF-vFmc"
      },
      "source": [
        "archive = zipfile.ZipFile('data.zip') \n",
        "for file in archive.namelist():\n",
        "     archive.extract(file, '.')"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BOkqWUduv6oT"
      },
      "source": [
        "archive = zipfile.ZipFile('ZhangLabData.zip') \n",
        "for file in archive.namelist():\n",
        "     archive.extract(file, 'data')"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZINuk8k2wC9E"
      },
      "source": [
        "def find(ch,st):\n",
        "    indexes=[]\n",
        "    index1=0\n",
        "    while(True):\n",
        "        if ch in st[index1:]:\n",
        "            index2=st[index1:].index(ch)\n",
        "            indexes.append(index1+index2)\n",
        "            index1=index2+index1+1\n",
        "        \n",
        "        else:\n",
        "            break\n",
        "    return(indexes)\n",
        "\n",
        "\n",
        "train={'CNV':{},'DME':{},'DRUSEN':{},'NORMAL':{}}\n",
        "test={'CNV':{},'DME':{},'DRUSEN':{},'NORMAL':{}}\n",
        "\n",
        "for r,d,f in os.walk('data/CellData/OCT/train'):\n",
        "    for file in f:\n",
        "        inds=find('-',file)\n",
        "        pid=file[inds[0]+1:inds[1]]\n",
        "        if 'CNV' in r:\n",
        "            if pid not in train['CNV']:\n",
        "                train['CNV'][pid]=[]\n",
        "            train['CNV'][pid].append('{}/{}'.format('CNV',file))\n",
        "        elif 'DME' in r:\n",
        "            if pid not in train['DME']:\n",
        "                train['DME'][pid]=[]\n",
        "            train['DME'][pid].append('{}/{}'.format('DME',file))\n",
        "        elif 'DRUSEN' in r:\n",
        "            if pid not in train['DRUSEN']:\n",
        "                train['DRUSEN'][pid]=[]\n",
        "            train['DRUSEN'][pid].append('{}/{}'.format('DRUSEN',file))\n",
        "        elif 'NORMAL' in r:\n",
        "            if pid not in train['NORMAL']:\n",
        "                train['NORMAL'][pid]=[]\n",
        "            train['NORMAL'][pid].append('{}/{}'.format('NORMAL',file))\n",
        "            \n",
        "for r,d,f in os.walk('data/CellData/OCT/test'):\n",
        "    for file in f:\n",
        "        inds=find('-',file)\n",
        "        pid=file[inds[0]+1:inds[1]]\n",
        "        if 'CNV' in r:\n",
        "            if pid not in test['CNV']:\n",
        "                test['CNV'][pid]=[]\n",
        "            test['CNV'][pid].append('{}/{}'.format('CNV',file))\n",
        "        elif 'DME' in r:\n",
        "            if pid not in test['DME']:\n",
        "                test['DME'][pid]=[]\n",
        "            test['DME'][pid].append('{}/{}'.format('DME',file))\n",
        "        elif 'DRUSEN' in r:\n",
        "            if pid not in test['DRUSEN']:\n",
        "                test['DRUSEN'][pid]=[]\n",
        "            test['DRUSEN'][pid].append('{}/{}'.format('DRUSEN',file))\n",
        "        elif 'NORMAL' in r:\n",
        "            if pid not in test['NORMAL']:\n",
        "                test['NORMAL'][pid]=[]\n",
        "            test['NORMAL'][pid].append('{}/{}'.format('NORMAL',file)) \n",
        "    \n",
        "        \n",
        "with open('train_cell.csv','w',newline='') as f:\n",
        "  csvw=csv.writer(f)\n",
        "  csvw.writerow(['filename','class'])\n",
        "  for disease in train:\n",
        "      if disease=='NORMAL':\n",
        "          count=0\n",
        "          for pid in train[disease]:\n",
        "              csvw.writerow([train[disease][pid][0],disease])\n",
        "              count+=1\n",
        "              if count==1000:\n",
        "                  break\n",
        "      else:\n",
        "          for pid in train[disease]:\n",
        "              csvw.writerow([train[disease][pid][0],disease])\n",
        "    \n",
        "\n",
        "with open('test_cell.csv','w',newline='') as f:\n",
        "  csvw=csv.writer(f)\n",
        "  csvw.writerow(['filename','class'])\n",
        "  for disease in test:\n",
        "      for pid in test[disease]:\n",
        "          for item in test[disease][pid]:\n",
        "              csvw.writerow([item,disease])\n"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JHY3ZdTDU1ar"
      },
      "source": [
        "\n",
        "train_datagen = keras.preprocessing.image.ImageDataGenerator(horizontal_flip=True,vertical_flip=True\n",
        "                                                             ,zoom_range=0.1,rotation_range=360\n",
        "                                                             ,width_shift_range=0.1,height_shift_range=0.1)\n",
        "\n",
        "test_datagen = keras.preprocessing.image.ImageDataGenerator()\n",
        "\n",
        "train_df = pd.read_csv(\"train_cell.csv\")\n",
        "test_df = pd.read_csv(\"test_cell.csv\")"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JgctU8dyU1hq",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "86e3f0ae-b4f1-4aec-9ca1-3aaf41aa2bb4"
      },
      "source": [
        "shape=(512, 512)\n",
        "batch_size=8 #increase when having better GPU\n",
        "train_generator = train_datagen.flow_from_dataframe(\n",
        "      dataframe=train_df,\n",
        "      directory='data/CellData/OCT/train',\n",
        "      x_col=\"filename\",\n",
        "      y_col=\"class\",\n",
        "      target_size=shape,\n",
        "      batch_size=batch_size,\n",
        "      class_mode='categorical',shuffle=True)\n",
        "validation_generator = test_datagen.flow_from_dataframe(\n",
        "        dataframe=test_df,\n",
        "        directory='data/CellData/OCT/test',\n",
        "        x_col=\"filename\",\n",
        "        y_col=\"class\",\n",
        "        target_size=shape,\n",
        "        batch_size=batch_size,\n",
        "        class_mode='categorical',shuffle=True)\n",
        "train_img_num=len(train_generator.filenames)"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found 3213 validated image filenames belonging to 4 classes.\n",
            "Found 1000 validated image filenames belonging to 4 classes.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KDZknqB4ZKuD",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b4a91a2e-f6ab-4f85-af09-e69b290cec53"
      },
      "source": [
        "!git clone https://github.com/mhrahimzadeh1374/automl"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'automl'...\n",
            "remote: Enumerating objects: 3922, done.\u001b[K\n",
            "remote: Counting objects: 100% (309/309), done.\u001b[K\n",
            "remote: Compressing objects: 100% (144/144), done.\u001b[K\n",
            "remote: Total 3922 (delta 192), reused 215 (delta 163), pack-reused 3613\u001b[K\n",
            "Receiving objects: 100% (3922/3922), 23.18 MiB | 20.36 MiB/s, done.\n",
            "Resolving deltas: 100% (2944/2944), done.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VNiNGUQoZ9ey",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3c2673d1-a73e-4783-c948-21ead87a5ad0"
      },
      "source": [
        "cd automl/efficientnetv2"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/automl/efficientnetv2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PVA1nBE4PsfD"
      },
      "source": [
        "from effnetv2_model import get_model"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j8L3JCAfhY-4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d392f3bb-ac9a-4dfe-b2b9-0f8d4ae35f62"
      },
      "source": [
        "cd .."
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/automl\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FIWPFZ-rhZ-Q",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "28d54feb-b4fa-4f74-8eb2-d2ee84c271f7"
      },
      "source": [
        "cd .."
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Js83s0PpU1vL"
      },
      "source": [
        "name=\"ROCT-Net_without Capsules_Kermany Dataset\"\n",
        "!mkdir \"models\"\n",
        "\n",
        "# ROCT-Net model withoud Capsule network\n",
        "keras.backend.clear_session()\n",
        "input_tensor=keras.Input(shape=(shape[0],shape[1],3))\n",
        "base_model1=get_model('efficientnetv2-b0', include_top=False, pretrained=True)(input_tensor)\n",
        "base_model2=keras.applications.Xception(input_tensor=input_tensor,weights='imagenet',include_top=False)(input_tensor)\n",
        "\n",
        "concatenated=keras.layers.concatenate([base_model1,base_model2]) \n",
        "\n",
        "avg=keras.layers.AveragePooling2D(3,padding='valid')(concatenated)\n",
        "depthw=keras.layers.DepthwiseConv2D(5,\n",
        "                                      depthwise_initializer=keras.initializers.RandomNormal(mean=0.0,stddev=0.01),\n",
        "                                      bias_initializer=keras.initializers.Zeros(),depthwise_constraint=keras.constraints.NonNeg())(avg)\n",
        "flat=keras.layers.Flatten()(depthw)\n",
        "dp=keras.layers.Dropout(0.2)(flat)\n",
        "preds=keras.layers.Dense(4,activation='softmax',\n",
        "                          kernel_initializer=keras.initializers.RandomNormal(mean=0.0,stddev=0.01),\n",
        "                          bias_initializer=keras.initializers.Zeros(),)(dp)\n",
        "model=keras.Model(inputs=input_tensor, outputs=preds)  \n",
        "\n",
        "##################################\n",
        "for layer in model.layers:\n",
        "  layer.trainable = True\n",
        "model.summary()\n",
        "filepath=\"models/%s-{epoch:02d}-{val_accuracy:.4f}.hdf5\"%name\n",
        "checkpoint = keras.callbacks.ModelCheckpoint(filepath, monitor='val_accuracy', save_best_only=False, mode='max',save_weights_only=True) #creating checkpoint to save the best validation accuracy\n",
        "callbacks_list = [checkpoint]\n",
        "\n",
        "\n",
        "lr_schedule =keras.optimizers.schedules.ExponentialDecay(\n",
        "    initial_learning_rate=0.045,\n",
        "    decay_steps=2*int(train_img_num/batch_size),\n",
        "    decay_rate=0.94,\n",
        "    staircase=True)\n",
        "optimizer=keras.optimizers.SGD(momentum=0.9,learning_rate=lr_schedule)\n",
        "model.compile(optimizer=optimizer, loss='categorical_crossentropy',metrics=['accuracy'])\n",
        "\n",
        "hist=model.fit_generator(train_generator, epochs=40,validation_data=validation_generator,shuffle=True,callbacks=callbacks_list) #start training\n",
        "with open('{}-results.csv'.format(name), mode='w',newline='') as csv_file: #write evaluation metrics\n",
        "  csv_writer = csv.writer(csv_file, delimiter=',', quotechar='\"', quoting=csv.QUOTE_MINIMAL)\n",
        "  for key in hist.history:\n",
        "    data=[key]\n",
        "    data.extend(hist.history[key])\n",
        "    csv_writer.writerow(data)"
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}