Data_Synthesis_For_Image_Classification.ipynb

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  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/mdanyalmalik/chest-xray-synthesis/blob/main/Data_Synthesis_For_Image_Classification.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#CS 437 Final Project\n",
        "## Evaluating the feasibility of using Generative Models to generate Chest XRay Data\n",
        "### M. Danyal Malik, Danish Humair\n"
      ],
      "metadata": {
        "id": "6U9yKfWsPjoe"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Check Runtime GPU\n",
        "\n",
        "!nvidia-smi # Make sure you are using a GPU runtime"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9TwHI5AEliTz",
        "outputId": "00e8db07-9592-4a23-9a66-4219ec538a16",
        "cellView": "form"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Thu Apr 27 10:59:00 2023       \n",
            "+-----------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 525.85.12    Driver Version: 525.85.12    CUDA Version: 12.0     |\n",
            "|-------------------------------+----------------------+----------------------+\n",
            "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
            "|                               |                      |               MIG M. |\n",
            "|===============================+======================+======================|\n",
            "|   0  NVIDIA A100-SXM...  Off  | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   33C    P0    42W / 400W |      0MiB / 40960MiB |      0%      Default |\n",
            "|                               |                      |             Disabled |\n",
            "+-------------------------------+----------------------+----------------------+\n",
            "                                                                               \n",
            "+-----------------------------------------------------------------------------+\n",
            "| Processes:                                                                  |\n",
            "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
            "|        ID   ID                                                   Usage      |\n",
            "|=============================================================================|\n",
            "|  No running processes found                                                 |\n",
            "+-----------------------------------------------------------------------------+\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Imports\n",
        "\n",
        "!pip install diffusers==0.11.1 transformers scipy ftfy accelerate\n",
        "\n",
        "import os\n",
        "import torch, gc\n",
        "import IPython\n",
        "from google.colab import drive\n",
        "from diffusers import StableDiffusionPipeline\n",
        "from PIL import Image\n",
        "\n",
        "IPython.display.clear_output()\n",
        "print(\"Installed all dependencies\")\n",
        "\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "id": "f6qeMdIHQBuC",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b188141d-37bc-4f76-8483-bc4f96bcf337",
        "cellView": "form"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Installed all dependencies\n",
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Upload Model\n",
        "\n",
        "path_type = \"Google Drive\" #@param [\"Google Drive\", \"Hugging Face\", \"Local Path\"]\n",
        "path = \"CXR/CXR-F-3000.ckpt\" #@param {type:\"string\"}\n",
        "clean = False #@param {type:\"boolean\"}\n",
        "model_name = path.split(\"/\")[-1].replace(\".ckpt\", \"\")\n",
        "\n",
        "if path_type != \"Hugging Face\":\n",
        "    if clean and os.path.exists(f\"/content/{model_name}\"):\n",
        "        !rm -rf {\"/content/\"+model_name}\n",
        "        print(\"Deleted old model\")\n",
        "    \n",
        "    if not os.path.exists(f\"/content/{model_name}\"):\n",
        "        if path_type == \"Google Drive\":\n",
        "            path = \"/content/drive/MyDrive/\" + path\n",
        "        \n",
        "        !wget -q -O convertodiff.py https://raw.githubusercontent.com/TheLastBen/fast-stable-diffusion/main/Dreambooth/convertodiffv2.py\n",
        "        !python /content/convertodiff.py {path} {\"/content/\"+model_name} --v2 --reference_model stabilityai/stable-diffusion-2-1-base\n",
        "        !rm /content/convertodiff.py\n",
        "        \n",
        "        path = \"/content/\"+model_name\n",
        "        print(\"Uploaded model to\", path)\n",
        "    else:\n",
        "        path = \"/content/\"+model_name\n",
        "        print(\"Model is already uploaded\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7ttCqWRgNa15",
        "outputId": "792fb140-e6e5-46bc-a886-60e9c2217d96",
        "cellView": "form"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2023-04-27 12:03:03.082760: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n",
            "\u001b[1;32mConverting to Diffusers ...\n",
            "Downloading (…)cheduler_config.json: 100% 346/346 [00:00<00:00, 210kB/s]\n",
            "Downloading (…)tokenizer/vocab.json: 100% 1.06M/1.06M [00:00<00:00, 5.03MB/s]\n",
            "Downloading (…)tokenizer/merges.txt: 100% 525k/525k [00:00<00:00, 1.23MB/s]\n",
            "Downloading (…)cial_tokens_map.json: 100% 460/460 [00:00<00:00, 276kB/s]\n",
            "Downloading (…)okenizer_config.json: 100% 807/807 [00:00<00:00, 487kB/s]\n",
            "You have disabled the safety checker for <class 'diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline'> by passing `safety_checker=None`. Ensure that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered results in services or applications open to the public. Both the diffusers team and Hugging Face strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling it only for use-cases that involve analyzing network behavior or auditing its results. For more information, please have a look at https://github.com/huggingface/diffusers/pull/254 .\n",
            "Uploaded model to /content/CXR-F-3000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Load Model\n",
        "\n",
        "pipe = StableDiffusionPipeline.from_pretrained(path, torch_dtype=torch.float16)\n",
        "pipe = pipe.to(\"cuda\")\n",
        "\n",
        "print(\"Loaded model to pipeline\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mcU4aMT8mosh",
        "outputId": "2cf92847-cbd1-4781-c193-b24c8d3fa9dd",
        "cellView": "form"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "You have disabled the safety checker for <class 'diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline'> by passing `safety_checker=None`. Ensure that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered results in services or applications open to the public. Both the diffusers team and Hugging Face strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling it only for use-cases that involve analyzing network behavior or auditing its results. For more information, please have a look at https://github.com/huggingface/diffusers/pull/254 .\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Loaded model to pipeline\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Batch Generate Images\n",
        "\n",
        "prompt = \"ChestXRay Edema\" #@param [\"ChestXRay No Finding\", \"ChestXRay Hernia\", \"ChestXRay Emphysema\", \"ChestXRay Nodule\", \"ChestXRay Pneumonia\", \"ChestXRay Consolidation\", \"ChestXRay Cardiomegaly\", \"ChestXRay Effusion\", \"ChestXRay Mass\", \"ChestXRay Pleural_Thickening\", \"ChestXRay Atelectasis\", \"ChestXRay Pneumothorax\", \"ChestXRay Fibrosis\", \"ChestXRay Infiltration\", \"ChestXRay Edema\"] {allow-input: true}\n",
        "iterations = 39 #@param {type:\"integer\"}\n",
        "amount = 16 #@param {type:\"integer\"}\n",
        "steps = 60 #@param {type:\"slider\", min:10, max:120}\n",
        "out_gdrive = False #@param {type:\"boolean\"}\n",
        "out_path = \"output\" #@param {type:\"string\"}\n",
        "keep_for_CheXNet_evaluation = False #@param {type:\"boolean\"}\n",
        "repeat_for_all_prompts = False #@param {type:\"boolean\"}\n",
        "clean = False #@param {type:\"boolean\"}\n",
        "\n",
        "if clean:\n",
        "    !rm -rf eval\n",
        "    !rm -rf output\n",
        "\n",
        "diseases = [\n",
        "    'Atelectasis', 'Cardiomegaly', 'Effusion', 'Infiltration', 'Mass', 'Nodule', 'Pneumonia',\n",
        "    'Pneumothorax', 'Consolidation', 'Edema', 'Emphysema', 'Fibrosis', 'Pleural_Thickening', 'Hernia'\n",
        "]\n",
        "\n",
        "prompts = []\n",
        "if repeat_for_all_prompts:\n",
        "    prompts.append(f\"ChestXRay No Finding\")\n",
        "    for d in diseases:\n",
        "        prompts.append(f\"ChestXRay {d}\")\n",
        "else:\n",
        "    prompts = [prompt]\n",
        "\n",
        "for prompt in prompts:\n",
        "    names_for_evaluation = []\n",
        "    label = [\"0\" for i in range(14)]\n",
        "    disease = 0\n",
        "\n",
        "    for x in range(len(diseases)):\n",
        "        if diseases[x] in prompt:\n",
        "            label[x] = \"1\"\n",
        "            disease = x + 1\n",
        "\n",
        "    if keep_for_CheXNet_evaluation:\n",
        "        if not os.path.exists(\"eval\"):\n",
        "            os.makedirs(\"eval\")\n",
        "            os.makedirs(\"eval/images\")\n",
        "            os.makedirs(\"eval/labels\")\n",
        "\n",
        "    if out_gdrive:\n",
        "        out_path = \"/content/drive/MyDrive/\" + out_path\n",
        "        if out_path[-1] == \"/\":\n",
        "            out_path = out_path[:-1]\n",
        "\n",
        "    if not os.path.exists(out_path):\n",
        "        os.makedirs(out_path)\n",
        "\n",
        "    for x in range(iterations):\n",
        "        print(\"Generating\", amount, \"images...\")\n",
        "        print(\"Seeds:\", end=\" \")\n",
        "\n",
        "        generator = []\n",
        "        for i in range(amount):\n",
        "            seed = torch.Generator(device=\"cuda\")\n",
        "            print(seed.seed(), end=\" \")\n",
        "            generator.append(seed)\n",
        "        print()\n",
        "\n",
        "        images = pipe(prompt, num_inference_steps=steps, generator=generator, num_images_per_prompt=amount).images\n",
        "        for i in range(len(images)):\n",
        "            images[i].save(f\"{out_path}/{model_name}_{prompt}_{generator[i].seed()}.png\")\n",
        "            if keep_for_CheXNet_evaluation:\n",
        "              filename = f\"{str(disease).zfill(2)}_{str(x).zfill(8)}_{str(i).zfill(3)}.png\"\n",
        "              images[i].save(f\"eval/images/{filename}\")\n",
        "              names_for_evaluation.append(filename)\n",
        "        \n",
        "        print(\"Saved\", amount, \"images\")\n",
        "\n",
        "        del images\n",
        "\n",
        "    if keep_for_CheXNet_evaluation:\n",
        "        f = open(\"eval/labels/test_list.txt\", \"a\")\n",
        "        for name in names_for_evaluation:\n",
        "            f.write(f\"{name} {' '.join(label)}\\n\")\n",
        "        f.close()"
      ],
      "metadata": {
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        },
        "outputId": "1a763cf5-d78b-47b3-fb69-294924edb999",
        "cellView": "form"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Generating 16 images...\n",
            "Seeds: 8862491197341514 2518009630868855 3120905984946487 602692621491855 1324832566548277 1749451329257558 5824604316661194 6664623499321179 8521395191782364 4553074812860518 4945267998106311 4458854016633106 1760559109653620 8102523352841352 3837142977095801 74291267294107 \n"
          ]
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        {
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          "data": {
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              "  0%|          | 0/60 [00:00<?, ?it/s]"
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            "application/vnd.jupyter.widget-view+json": {
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              "version_minor": 0,
              "model_id": "b5fa3976bad34798b7fc82f09fa16029"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saved 16 images\n",
            "Generating 16 images...\n",
            "Seeds: 8660438958584271 8553767388716502 8955197768013770 5744744603452596 1391399888419422 1316772649201288 1398954662190793 4867051043898335 7198260644959249 3469282874115330 8358810852138284 5267220826661236 5489485156450049 2288487821675438 1144406036327651 6022543810599257 \n"
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        {
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          "data": {
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              "  0%|          | 0/60 [00:00<?, ?it/s]"
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            "application/vnd.jupyter.widget-view+json": {
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              "model_id": "830c388c316e4bdf85719e64cf7c3324"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saved 16 images\n",
            "Generating 16 images...\n",
            "Seeds: 9940061383376 8548217097490028 286720762749621 1825638822881228 6359399808631717 3124264839946656 5948206730580145 8468957880118326 4460989885407539 2326210960174377 90702290820785 943902614362557 4539949783004314 4558255127757151 8898907239038514 3730941704526097 \n"
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        {
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          "data": {
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              "  0%|          | 0/60 [00:00<?, ?it/s]"
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            "application/vnd.jupyter.widget-view+json": {
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              "model_id": "2cb34a74c6114680aaf6509c7b2e5888"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saved 16 images\n",
            "Generating 16 images...\n",
            "Seeds: 8580856901316730 5405047315860679 2102767776607172 2183601762208144 6045046857738199 1314377663683700 4818738206364817 5610699297371438 3009879143876826 8251913626107802 2443132933016360 5986659590141567 1803986254949952 3753251159032711 5133560466038629 836998003524181 \n"
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        {
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          "data": {
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              "  0%|          | 0/60 [00:00<?, ?it/s]"
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              "model_id": "806836aa439b48158a221e87356e3ded"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saved 16 images\n",
            "Generating 16 images...\n",
            "Seeds: 7447573369828402 5545577683823714 7465924389608063 6117755424301476 1818454583394663 5588921034224977 3799772943239961 5588139447613185 7138523560311671 4586050882962044 6673626800665915 6926314136615240 8026734119868448 3411334655127989 8175390128534168 7855012026158719 \n"
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          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saved 16 images\n",
            "Generating 16 images...\n",
            "Seeds: 3347863991648728 8694795530994760 5594458200391979 8255442947885841 4332489075488946 219661493212415 6896391073263027 1135183984236829 3912586918044914 7587082527491098 2002135063913467 4880429179612209 1158763578940345 3145682770551712 8019304811689694 6689050780835666 \n"
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              "  0%|          | 0/60 [00:00<?, ?it/s]"
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          "metadata": {}
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        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saved 16 images\n",
            "Generating 16 images...\n",
            "Seeds: 2978581380144323 3885525403176098 1375243528084443 8271538441600243 656975924444973 7612389256165644 144589519687620 1742425527858259 3183592621938784 6087414006490207 3303950639245258 6931450194829155 7785199950653757 3342920325616429 6085847846167893 7495192186742612 \n"
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              "  0%|          | 0/60 [00:00<?, ?it/s]"
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          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saved 16 images\n",
            "Generating 16 images...\n",
            "Seeds: 6570248349809469 5792152937929282 4132551250997574 7323972731485056 3779681237820987 5189882883313725 6232457516134151 6809010421102463 3938291819555804 6164208712184338 423656396473678 7994409548916980 114722954733711 1294958232604122 6861872466006451 7417572883730712 \n"
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          "data": {
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              "  0%|          | 0/60 [00:00<?, ?it/s]"
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          "text": [
            "Saved 16 images\n",
            "Generating 16 images...\n",
            "Seeds: 7418386810123943 4799908481030055 5928398495770109 7830748286580074 8320458150958685 8482569405756145 3135483000027052 8617782589126313 6973349568922735 4726688817476077 7570989913561593 3692443302393901 4863842952013120 5713963899537746 884187767029322 2922797826837197 \n"
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            "Saved 16 images\n",
            "Generating 16 images...\n",
            "Seeds: 1782107294853907 8574239114716722 8721259652971477 2066134684006976 3079794029652106 1673664850677464 4236970030227333 4038417087759903 611946917528899 6988049003617184 1274504645475530 3694297693387460 6825795033027519 2926963209408431 4452224095832594 1767715030931994 \n"
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            "Saved 16 images\n",
            "Generating 16 images...\n",
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            "Saved 16 images\n",
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            "Saved 16 images\n",
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            "Saved 16 images\n",
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            "Saved 16 images\n",
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          "text": [
            "Saved 16 images\n",
            "Generating 16 images...\n",
            "Seeds: 3750488802513311 5127331174957644 2518774734536862 6667545148090473 2398593340743659 3654221499959295 2834983363424144 1429014097075222 4391687062889923 88639817931223 3356880755989202 3217120458582594 5568694297297098 750840535909497 481839313175786 6028001968711405 \n"
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          "text": [
            "Saved 16 images\n",
            "Generating 16 images...\n",
            "Seeds: 7859426901005759 3686713797279129 6348725800284441 6385904847578406 6113689100950145 4309032402180046 3258497823464615 2015391312409056 2570592073512770 4181876391486635 7923219572246081 5177183171609187 3567566605224845 1239479444961510 2042322480324208 1175578235600688 \n"
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          "text": [
            "Saved 16 images\n",
            "Generating 16 images...\n",
            "Seeds: 291015549887823 6040236503588214 3718805720503566 8049184716669550 8116115203361036 7744364396359866 5572536291295550 6147087577527574 3066635177121011 6881851764540083 1765083088592289 3239581700216959 7869074781453002 4177358781294107 191480305276936 2524532508841758 \n"
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            "Saved 16 images\n",
            "Generating 16 images...\n",
            "Seeds: 4251845392757658 3124872753718325 7018495562155976 3869156757549208 5972768386886208 2820439652042272 7919726554841596 1027907630722177 7136353822110210 2749449778793083 2562557431303589 6097052929512768 5842887263289458 7355231061585865 6564721382660564 1340158649296639 \n"
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          "text": [
            "Saved 16 images\n",
            "Generating 16 images...\n",
            "Seeds: 5810510970294280 3729176227165988 4202440551431137 6083414285059736 5071450790432200 7139608449168332 7699750648700944 4294726108648155 7147834076122833 7467160877731818 783074994871522 1015170753661034 8991239107890214 5586282691418019 4368690692680171 8923869279224272 \n"
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          "text": [
            "Saved 16 images\n",
            "Generating 16 images...\n",
            "Seeds: 1892695357489172 2455418397221857 5373777221227447 892413895090932 112472956556498 5851266561818878 2683855089594000 6044171709177825 5756191340325369 549820572797708 199786348978235 5530277785240340 2407646864757888 3813537251393346 9005699061656269 2619535890764664 \n"
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          "text": [
            "Saved 16 images\n",
            "Generating 16 images...\n",
            "Seeds: 7083415522259935 401912848885772 7207765781176826 2325869314600510 6352925114302014 5216951470630710 302541545797894 2136336020627358 3487241803649354 4343383547702601 8477793424808510 844549990392329 855626751048017 2104207025211199 4268988476776252 4220726204322797 \n"
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            "Saved 16 images\n",
            "Generating 16 images...\n",
            "Seeds: 3625852281470799 8227027307413539 8653520636572981 5749692256595262 7010989362341006 8429365087400624 4011659535431936 8275198775818866 4805601501389823 8435654283975158 1010987576668554 7505639907925059 1317360467684308 6350045098678813 2761113085846479 5735058254282637 \n"
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          "text": [
            "Saved 16 images\n",
            "Generating 16 images...\n",
            "Seeds: 8988356483222760 272939388548421 5185272637156973 2358529589653425 7950248656636767 8019315062218859 6803797892215710 1607856413178001 5098308039911411 2782241727764393 317515522667900 802774001437898 7617552699887445 4606050653024972 8850241371903239 3723266726196390 \n"
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          "text": [
            "Saved 16 images\n",
            "Generating 16 images...\n",
            "Seeds: 4083963134897247 3355799876226952 4096971513399664 1971380498342886 5645203794126218 946451460494086 4120994537467320 1625234808143144 7045293867642535 3295576674664284 5299728599288349 3693822987752790 1358367767935515 7019698867875686 8066366248520061 55516385034382 \n"
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          "text": [
            "Saved 16 images\n",
            "Generating 16 images...\n",
            "Seeds: 605787474950730 4868616045516568 8741549910665141 7157017906321322 7247472707147540 4943744573168338 4550109352607691 4716780888715679 102675197537466 6850840521505539 8853155781677536 8767139138054117 2937938992552944 4583000688685085 878484824050890 8417613526307699 \n"
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          "text": [
            "Saved 16 images\n",
            "Generating 16 images...\n",
            "Seeds: 7697787075985374 2341609287834970 6059981886750881 5192501098987060 1608128511783122 920904916501128 2872669530420658 3314665050393409 2492464418980866 8578186691071879 7968863493105157 3018725204521945 6490567440210478 8291862718945558 2435218737589458 5214315715262716 \n"
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          "text": [
            "Saved 16 images\n",
            "Generating 16 images...\n",
            "Seeds: 6207711029935544 620256310191390 5393896432356308 956336313713452 118800192425771 2753161803157299 5873159632543670 2414578826909688 5388787145506013 5692228291124242 664836793621469 1148025705165079 2727894832633338 8771587554167776 1026898204186121 772566212744131 \n"
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          "text": [
            "Saved 16 images\n",
            "Generating 16 images...\n",
            "Seeds: 8670839537481981 1520493322745532 6994317267107943 4950390205606858 337994547893363 1582146588098620 4909906424723274 2301553262585546 7243150754182304 1643648331745664 5021135633048681 1640906838290035 5623763682316699 7426000036273870 7208876080583222 7212872951529838 \n"
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          "text": [
            "Saved 16 images\n",
            "Generating 16 images...\n",
            "Seeds: 2814086054289904 1666017677238453 7887287059153730 6561637039067328 2512473249382517 2638722102614897 6759212991318459 7574094320967679 951313364138293 4756803972249546 8371512082687888 5517952239621449 2450761607411882 6030291259036128 885712396882038 1482761905788955 \n"
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          "text": [
            "Saved 16 images\n",
            "Generating 16 images...\n",
            "Seeds: 7095198898691145 3784082570996629 228392240006295 1426570281399956 943183255411373 8253327666539192 5625555101780948 8789969629986747 6861812425031347 3789062680825273 7430510931190029 8002639896512043 7059799839845627 6969407670728622 1990487130796559 2561719761458103 \n"
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          "text": [
            "Saved 16 images\n",
            "Generating 16 images...\n",
            "Seeds: 4421502443659291 3179831712484815 4613114428487056 7384208601748988 1245376796539441 7896236953684231 2562107406519199 515640118674577 6309929973545313 4006055739816284 3997663154048006 8584254725481382 4312803850109332 3347916207237711 2755968449095865 5043449014051852 \n"
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          "text": [
            "Saved 16 images\n",
            "Generating 16 images...\n",
            "Seeds: 4282163411539874 5380726700575316 7011911274171158 5621090606817008 6418680340015450 5553231505511733 2399062993807932 7813785989738052 4384567034989297 5519534712185445 5624647654166923 5762807077919871 7759615723422519 4678708324365836 329802784192689 5389471281356537 \n"
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              "version_major": 2,
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              "model_id": "cff02eb248a048c1a56d59cb572b6750"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saved 16 images\n",
            "Generating 16 images...\n",
            "Seeds: 827452218190457 6965429916829454 3324832997165218 7140326361340878 4597347521965254 2244024372074080 5423654875143743 6434024524574887 7080240851457957 8879136311028220 6189025865349269 6903231020201821 5293018404210318 223371257891440 3090184421396174 7847318874800788 \n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "2998822b0431492d90e90c20a1510a4c"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saved 16 images\n",
            "Generating 16 images...\n",
            "Seeds: 5075501589357076 6279173729190452 7486068401372780 5038555710850088 103480494258688 1872791900512275 6511318394266690 1513954802475075 1835304705541796 3548601942811946 2310989132692477 3268512379382658 875092226754601 4728896255012262 2431730783068164 7100140710451834 \n"
          ]
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        {
          "output_type": "display_data",
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              "version_major": 2,
              "version_minor": 0,
              "model_id": "63ff74195b0b449a8343c91a0ade80c4"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saved 16 images\n",
            "Generating 16 images...\n",
            "Seeds: 7456806357718287 2815951386390551 4937764750489563 8626521161507839 7813985068682668 7250257683500801 1760189341608309 847428204543420 6178358204792656 7824738125879537 1177157699043453 7439085003754925 310099326478556 2273993068477706 4708883920660695 8650704236843552 \n"
          ]
        },
        {
          "output_type": "display_data",
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            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "dd5a8972ae0d4ef0ac97c70eb8aaf19b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saved 16 images\n",
            "Generating 16 images...\n",
            "Seeds: 2688746180553928 7391782067225500 5970674563901995 5589596132021718 8281149954232571 1316508254686026 7159049273662968 3710944719931113 5058394196445123 7366928943921963 2774519267756935 3513106490093036 172808944282523 335783956903389 8071996921409596 2420550808952495 \n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "3b04306f32414705af4ab98647146fc7"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saved 16 images\n",
            "Generating 16 images...\n",
            "Seeds: 1073665280853653 858310542787928 4323363172702225 1154433985746255 577899493076530 2954858970852853 6792764291993724 2852762814154032 659436572380655 6113610701033985 847912968459929 8555686394174412 6936132898723541 3090353711244909 5595126682797626 793269543578462 \n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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              "version_major": 2,
              "version_minor": 0,
              "model_id": "ac0265b0ae0c43db9d6cde5ca0d14725"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saved 16 images\n",
            "Generating 16 images...\n",
            "Seeds: 4147752829830900 5400111133069095 1227621371817426 5749779364740020 1069750043173038 2033728396208984 7190195001809140 3384076501618335 5252251986368183 4334850715538348 746560723341472 5502349891345635 3991334212277467 8280794474884799 7505084650068815 2792534395327954 \n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "3156aa2576d04a679068113d8d1035d5"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saved 16 images\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Save Output to Tar\n",
        "\n",
        "!tar -cvf \"/content/drive/MyDrive/CXR/output.tar\" \"output\""
      ],
      "metadata": {
        "cellView": "form",
        "id": "L20oDvhGLS48"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Load Output from tar\n",
        "\n",
        "!tar -xvf \"/content/drive/MyDrive/CXR/output.tar\" -C \"./\""
      ],
      "metadata": {
        "cellView": "form",
        "id": "IsB_n9mBLxqw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Clear GPU RAM\n",
        "\n",
        "del pipe\n",
        "gc.collect()\n",
        "torch.cuda.empty_cache()"
      ],
      "metadata": {
        "cellView": "form",
        "id": "X4hE2-lgbRZh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Evaluate Synthesized Images using pre-trained CheXNet Classifier\n",
        "\n",
        "BATCH_SIZE = 64 #@param {type:\"integer\"}\n",
        "clean = False #@param {type:\"boolean\"}\n",
        "\n",
        "if clean:\n",
        "    !rm -rf CheXNet\n",
        "\n",
        "%cd /content\n",
        "# !git clone https://github.com/arnoweng/CheXNet.git\n",
        "!git clone https://github.com/icekang/CheXNet.git\n",
        "!rm -rf CheXNet/ChestX-ray14/*\n",
        "!cp -r eval/images CheXNet/ChestX-ray14/\n",
        "!cp -r eval/labels CheXNet/ChestX-ray14/\n",
        "\n",
        "%cd /content/CheXNet\n",
        "\n",
        "with open('model.py', 'r') as file:\n",
        "    data = file.readlines()\n",
        "\n",
        "data[25] = f\"BATCH_SIZE = {BATCH_SIZE}\\n\"\n",
        "data[39] = \"        model.load_state_dict(checkpoint['state_dict'], strict=False)\\n\"\n",
        "\n",
        "with open('model.py', 'w') as file:\n",
        "    file.writelines(data)\n",
        "\n",
        "!python model.py\n",
        "%cd /content"
      ],
      "metadata": {
        "id": "6eCEhdgr6_km",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "31ab813e-6547-4937-960c-63a8d97fdf35",
        "cellView": "form"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content\n",
            "fatal: destination path 'CheXNet' already exists and is not an empty directory.\n",
            "/content/CheXNet\n",
            "/usr/local/lib/python3.9/dist-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.9/dist-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=DenseNet121_Weights.IMAGENET1K_V1`. You can also use `weights=DenseNet121_Weights.DEFAULT` to get the most up-to-date weights.\n",
            "  warnings.warn(msg)\n",
            "=> loading checkpoint\n",
            "=> loaded checkpoint\n",
            "/content/CheXNet/model.py:74: UserWarning: volatile was removed and now has no effect. Use `with torch.no_grad():` instead.\n",
            "  input_var = torch.autograd.Variable(inp.view(-1, c, h, w).cuda(), volatile=True)\n",
            "The average AUROC is 0.501\n",
            "The AUROC of Atelectasis is 0.48486328125\n",
            "The AUROC of Cardiomegaly is 0.5553850446428571\n",
            "The AUROC of Effusion is 0.4489397321428572\n",
            "The AUROC of Infiltration is 0.5298549107142858\n",
            "The AUROC of Mass is 0.46575055803571425\n",
            "The AUROC of Nodule is 0.3872767857142857\n",
            "The AUROC of Pneumonia is 0.4673549107142857\n",
            "The AUROC of Pneumothorax is 0.6513671875\n",
            "The AUROC of Consolidation is 0.5784040178571428\n",
            "The AUROC of Edema is 0.54833984375\n",
            "The AUROC of Emphysema is 0.5233677455357142\n",
            "The AUROC of Fibrosis is 0.45654296875\n",
            "The AUROC of Pleural_Thickening is 0.52734375\n",
            "The AUROC of Hernia is 0.3949497767857143\n",
            "/content\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Training Simple Classifier"
      ],
      "metadata": {
        "id": "ECjlrcrUx7oQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#@markdown ## Imports\n",
        "\n",
        "!pip install deeplake\n",
        "IPython.display.clear_output()\n",
        "print(\"Installed deeplake\")\n",
        "\n",
        "import deeplake\n",
        "from keras.layers import Input, Lambda, Dense, Flatten\n",
        "from keras.models import Model\n",
        "from keras.applications.vgg16 import VGG16\n",
        "from keras.applications.vgg16 import preprocess_input\n",
        "from keras.preprocessing import image\n",
        "from keras.preprocessing.image import ImageDataGenerator\n",
        "from keras.models import Sequential\n",
        "import numpy as np\n",
        "from glob import glob\n",
        "import matplotlib.pyplot as plt\n",
        "import os\n",
        "import cv2\n",
        "from PIL import Image\n",
        "print(\"Imported all dependencies\")\n",
        "\n",
        "hist = dict()\n",
        "ds = deeplake.load('hub://activeloop/nih-chest-xray-train', access_method='stream')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "cellView": "form",
        "id": "LwjNafbPuq3G",
        "outputId": "e9c73566-0c86-4afe-ff8a-fb8ef8875800"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Installed deeplake\n",
            "Imported all dependencies\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "This dataset can be visualized in Jupyter Notebook by ds.visualize() or at https://app.activeloop.ai/activeloop/nih-chest-xray-train\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\\"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "hub://activeloop/nih-chest-xray-train loaded successfully.\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r \r\r \r"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#@markdown ## Download ChestXRay Dataset\n",
        "import cv2\n",
        "from PIL import Image\n",
        "\n",
        "!rm -rf vgg\n",
        "\n",
        "os.makedirs('./vgg/train/disease')\n",
        "os.makedirs('./vgg/train/normal')\n",
        "\n",
        "os.makedirs('./vgg/test/disease')\n",
        "os.makedirs('./vgg/test/normal')\n",
        "\n",
        "os.makedirs('./vgg/val/disease')\n",
        "os.makedirs('./vgg/val/normal')\n",
        "\n",
        "model_name = \"real\"\n",
        "\n",
        "info = [\n",
        "  \"No Finding\",\n",
        "  \"Hernia\",\n",
        "  \"Emphysema\",\n",
        "  \"Nodule\",\n",
        "  \"Pneumonia\",\n",
        "  \"Consolidation\",\n",
        "  \"Cardiomegaly\",\n",
        "  \"Effusion\",\n",
        "  \"Mass\",\n",
        "  \"Pleural_Thickening\",\n",
        "  \"Atelectasis\",\n",
        "  \"Pneumothorax\",\n",
        "  \"Fibrosis\",\n",
        "  \"Infiltration\",\n",
        "  \"Edema\"\n",
        "]\n",
        "\n",
        "disease_to_detect = \"Pneumonia\"  #@param [\"Hernia\", \"Emphysema\", \"Nodule\", \"Pneumonia\", \"Consolidation\", \"Cardiomegaly\", \"Effusion\", \"Mass\", \"Pleural_Thickening\", \"Atelectasis\", \"Pneumothorax\", \"Fibrosis\", \"Infiltration\", \"Edema\"] {allow-input: true}\n",
        "f = info.index(disease_to_detect)\n",
        "\n",
        "print(f)\n",
        "\n",
        "normal_count = 0\n",
        "disease_count = 0\n",
        "count = 0\n",
        "\n",
        "n_train = 1000 #@param {type:\"integer\"}\n",
        "n_val   = 10 #@param {type:\"integer\"}\n",
        "n_test  = 300 #@param {type:\"integer\"}\n",
        "\n",
        "for i in range(len(ds.images)):\n",
        "    count += 1\n",
        "\n",
        "    y = ds.findings[i].numpy()\n",
        "    path = \"\"\n",
        "    \n",
        "    if f in y:\n",
        "        name = f\"{str(20+f).zfill(2)}_{str(disease_count+1).zfill(8)}_{str(1).zfill(3)}.png\"\n",
        "\n",
        "        if disease_count < n_train:\n",
        "            path = \"vgg/train/disease/\"+name\n",
        "            disease_count += 1\n",
        "        elif disease_count < n_train+n_val:\n",
        "            path = \"vgg/val/disease/\"+name\n",
        "            disease_count += 1\n",
        "        elif disease_count < n_train+n_val+n_test:\n",
        "            path = \"vgg/test/disease/\"+name\n",
        "            disease_count += 1\n",
        "\n",
        "    if 0 in y:\n",
        "        name = f\"{str(20+0).zfill(2)}_{str(normal_count+1).zfill(8)}_{str(1).zfill(3)}.png\"\n",
        "\n",
        "        if normal_count < n_train:\n",
        "            path = \"vgg/train/normal/\"+name\n",
        "            normal_count += 1\n",
        "        elif normal_count < n_train+n_val:\n",
        "            path = \"vgg/val/normal/\"+name\n",
        "            normal_count += 1\n",
        "        elif normal_count < n_train+n_val+n_test:\n",
        "            path = \"vgg/test/normal/\"+name\n",
        "            normal_count += 1\n",
        "    \n",
        "    if path != \"\":\n",
        "        try:\n",
        "            x = ds.images[i]\n",
        "            img = x.numpy()\n",
        "            img = cv2.resize(img, dsize=(512, 512), interpolation=cv2.INTER_CUBIC)\n",
        "            img = Image.fromarray(img, \"L\")\n",
        "\n",
        "            img.save(path)\n",
        "            print(\"Saved\", path)\n",
        "        except:\n",
        "            pass\n",
        "    \n",
        "    if normal_count + disease_count > (n_train+n_val+n_test)*2:\n",
        "        break"
      ],
      "metadata": {
        "cellView": "form",
        "id": "1B9mdp6lvP04"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#@markdown ## Load ChestXRay Dataset from Tar (optional)\n",
        "\n",
        "force_extract_tar = False #@param {type:\"boolean\"}\n",
        "\n",
        "if force_extract_tar or not os.path.exists(\"images\"):\n",
        "    !rm -rf images\n",
        "    !tar -xvf \"/content/drive/MyDrive/CXR/images.tar\" -C \"./\"\n",
        "\n",
        "!rm -rf vgg\n",
        "\n",
        "os.makedirs('./vgg/train/disease')\n",
        "os.makedirs('./vgg/train/normal')\n",
        "\n",
        "os.makedirs('./vgg/test/disease')\n",
        "os.makedirs('./vgg/test/normal')\n",
        "\n",
        "os.makedirs('./vgg/val/disease')\n",
        "os.makedirs('./vgg/val/normal')\n",
        "\n",
        "model_name = \"real\"\n",
        "\n",
        "info = [\n",
        "  \"No Finding\",\n",
        "  \"Hernia\",\n",
        "  \"Emphysema\",\n",
        "  \"Nodule\",\n",
        "  \"Pneumonia\",\n",
        "  \"Consolidation\",\n",
        "  \"Cardiomegaly\",\n",
        "  \"Effusion\",\n",
        "  \"Mass\",\n",
        "  \"Pleural_Thickening\",\n",
        "  \"Atelectasis\",\n",
        "  \"Pneumothorax\",\n",
        "  \"Fibrosis\",\n",
        "  \"Infiltration\",\n",
        "  \"Edema\"\n",
        "]\n",
        "\n",
        "disease_to_detect = \"Edema\"  #@param [\"Hernia\", \"Emphysema\", \"Nodule\", \"Pneumonia\", \"Consolidation\", \"Cardiomegaly\", \"Effusion\", \"Mass\", \"Pleural_Thickening\", \"Atelectasis\", \"Pneumothorax\", \"Fibrosis\", \"Infiltration\", \"Edema\"] {allow-input: true}\n",
        "\n",
        "images_dir = \"images/\" #@param {type:\"string\"}\n",
        "normal_dir = \"vgg/train/normal/\" #@param {type:\"string\"}\n",
        "disease_dir = \"vgg/train/disease/\" #@param {type:\"string\"}\n",
        "\n",
        "diseases = [\n",
        "    'Atelectasis', 'Cardiomegaly', 'Effusion', 'Infiltration', 'Mass', 'Nodule', 'Pneumonia',\n",
        "    'Pneumothorax', 'Consolidation', 'Edema', 'Emphysema', 'Fibrosis', 'Pleural_Thickening', 'Hernia'\n",
        "]\n",
        "\n",
        "normal_count = 0\n",
        "disease_count = 0\n",
        "count = 0\n",
        "\n",
        "n_train = 500 #@param {type:\"integer\"}\n",
        "n_val   = 10 #@param {type:\"integer\"}\n",
        "n_test  = 300 #@param {type:\"integer\"}\n",
        "\n",
        "for file_name in os.listdir(images_dir):\n",
        "    if \"No Finding\" in file_name:\n",
        "        with open(images_dir + file_name, 'rb') as src_file:\n",
        "            if normal_count > n_train:\n",
        "                if normal_count < n_train+n_val:\n",
        "                    normal_dir = \"vgg/val/normal/\"\n",
        "                elif normal_count < n_train+n_val+n_test:\n",
        "                    normal_dir = \"vgg/test/normal/\"\n",
        "            if normal_count < n_train+n_val+n_test:\n",
        "                with open(normal_dir + file_name, 'wb') as dest_file:\n",
        "                    dest_file.write(src_file.read())\n",
        "                    normal_count += 1\n",
        "    elif disease_to_detect in file_name:\n",
        "        with open(images_dir + file_name, 'rb') as src_file:\n",
        "            if disease_count > n_train:\n",
        "                if disease_count < n_train+n_val:\n",
        "                    disease_dir = \"vgg/val/disease/\"\n",
        "                elif disease_count < n_train+n_val+n_test:\n",
        "                    disease_dir = \"vgg/test/disease/\"\n",
        "            if disease_count < n_train+n_val+n_test:\n",
        "                with open(disease_dir + file_name, 'wb') as dest_file:\n",
        "                    dest_file.write(src_file.read())\n",
        "                    disease_count += 1\n",
        "\n",
        "print(\"Normal:\", normal_count)\n",
        "print(\"Disease:\", disease_count)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "cellView": "form",
        "id": "gJdCBTpnhpT7",
        "outputId": "4e0de634-8a18-4040-fe3f-f2f7a7c42357"
      },
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Normal: 810\n",
            "Disease: 810\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#@markdown ## Load Synthesized Images (optional)\n",
        "\n",
        "output_dir = \"output/\" #@param {type:\"string\"}\n",
        "normal_dir = \"vgg/train/normal/\" #@param {type:\"string\"}\n",
        "disease_dir = \"vgg/train/disease/\" #@param {type:\"string\"}\n",
        "\n",
        "model_name = \"synthesized\"\n",
        "\n",
        "diseases = [\n",
        "    'Atelectasis', 'Cardiomegaly', 'Effusion', 'Infiltration', 'Mass', 'Nodule', 'Pneumonia',\n",
        "    'Pneumothorax', 'Consolidation', 'Edema', 'Emphysema', 'Fibrosis', 'Pleural_Thickening', 'Hernia'\n",
        "]\n",
        "\n",
        "normal_count = 0\n",
        "disease_count = 0\n",
        "\n",
        "n_train = 500 #@param {type:\"integer\"}\n",
        "\n",
        "for file_name in os.listdir(output_dir):\n",
        "    if \"No Finding\" in file_name:\n",
        "        with open(output_dir + file_name, 'rb') as src_file:\n",
        "            if normal_count < n_train:\n",
        "                with open(normal_dir + file_name, 'wb') as dest_file:\n",
        "                    dest_file.write(src_file.read())\n",
        "                    normal_count += 1\n",
        "    elif disease_to_detect in file_name:\n",
        "        with open(output_dir + file_name, 'rb') as src_file:\n",
        "            if disease_count < n_train:\n",
        "                with open(disease_dir + file_name, 'wb') as dest_file:\n",
        "                    dest_file.write(src_file.read())\n",
        "                    disease_count += 1\n",
        "\n",
        "print(\"Normal:\", normal_count)\n",
        "print(\"Disease:\", disease_count)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p05p7_TE_zfl",
        "outputId": "fc55c93c-8c00-4c12-ace9-5359548433d0"
      },
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Normal: 500\n",
            "Disease: 500\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#@markdown ## Train Classifier\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import cv2\n",
        "import random\n",
        "import os\n",
        "import glob\n",
        "from tqdm.notebook import tqdm\n",
        "\n",
        "import albumentations as A\n",
        "\n",
        "from tensorflow.keras.layers import Conv2D, Flatten, MaxPooling2D, Dense\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
        "\n",
        "BATCH_SIZE = 64 #@param {type:\"integer\"}\n",
        "IMAGE_SIZE = [224, 224]\n",
        "\n",
        "train_path = './vgg/train' #@param {type:\"string\"}\n",
        "val_path = './vgg/val' #@param {type:\"string\"}\n",
        "test_path = './vgg/test' #@param {type:\"string\"}\n",
        "\n",
        "CLASSES = os.listdir(train_path)\n",
        "NUM_CLASSES = len(CLASSES)\n",
        "\n",
        "print(\"Class --> {} \\n and the length is : {}\".format(CLASSES, NUM_CLASSES))\n",
        "\n",
        "train_gen = ImageDataGenerator(\n",
        "    rescale=1/255.,\n",
        "    horizontal_flip=True,\n",
        "    vertical_flip=True,\n",
        "    rotation_range=0.4,\n",
        "    zoom_range=0.4\n",
        ")\n",
        "val_gen = ImageDataGenerator(\n",
        "    rescale=1/255.,\n",
        ")\n",
        "\n",
        "Train = train_gen.flow_from_directory(\n",
        "    train_path,\n",
        "    target_size=(224, 224),\n",
        "    batch_size=BATCH_SIZE\n",
        ")\n",
        "Test = train_gen.flow_from_directory(\n",
        "    test_path,\n",
        "    target_size=(224, 224),\n",
        "    batch_size=BATCH_SIZE\n",
        ")\n",
        "\n",
        "model = Sequential()\n",
        "model.add(Conv2D(filters=128, kernel_size=(3,3), activation='relu', input_shape=(224, 224, 3)))\n",
        "model.add(MaxPooling2D(pool_size=(2,2)))\n",
        "model.add(Conv2D(filters=128, kernel_size=(3,3), activation='relu'))\n",
        "model.add(MaxPooling2D(pool_size=(2,2)))\n",
        "model.add(Conv2D(filters=64, kernel_size=(3,3), activation='relu'))\n",
        "model.add(MaxPooling2D(pool_size=(2,2)))\n",
        "model.add(Conv2D(filters=32, kernel_size=(3,3), activation='relu'))\n",
        "model.add(MaxPooling2D(pool_size=(2,2)))\n",
        "model.add(Flatten())\n",
        "\n",
        "model.add(Dense(256, activation='relu'))\n",
        "model.add(Dense(128, activation='relu'))\n",
        "model.add(Dense(64, activation='relu'))\n",
        "model.add(Dense(2, activation='softmax'))\n",
        "\n",
        "model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])\n",
        "model.summary()\n",
        "\n",
        "NUM_EPOCHS = 20 #@param {type:\"integer\"}\n",
        "\n",
        "hist[model_name] = model.fit_generator(\n",
        "    Train,\n",
        "    epochs=NUM_EPOCHS,\n",
        "    validation_data=Test\n",
        ")\n",
        "\n",
        "model.save(f\"{model_name}.hdf5\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "cellView": "form",
        "id": "LH8seCFp7JKU",
        "outputId": "6f47c6c0-5380-4e85-fa00-05ec7a97ced2"
      },
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Class --> ['disease', 'normal'] \n",
            " and the length is : 2\n",
            "Found 2002 images belonging to 2 classes.\n",
            "Found 600 images belonging to 2 classes.\n",
            "Model: \"sequential_7\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv2d_28 (Conv2D)          (None, 222, 222, 128)     3584      \n",
            "                                                                 \n",
            " max_pooling2d_28 (MaxPoolin  (None, 111, 111, 128)    0         \n",
            " g2D)                                                            \n",
            "                                                                 \n",
            " conv2d_29 (Conv2D)          (None, 109, 109, 128)     147584    \n",
            "                                                                 \n",
            " max_pooling2d_29 (MaxPoolin  (None, 54, 54, 128)      0         \n",
            " g2D)                                                            \n",
            "                                                                 \n",
            " conv2d_30 (Conv2D)          (None, 52, 52, 64)        73792     \n",
            "                                                                 \n",
            " max_pooling2d_30 (MaxPoolin  (None, 26, 26, 64)       0         \n",
            " g2D)                                                            \n",
            "                                                                 \n",
            " conv2d_31 (Conv2D)          (None, 24, 24, 32)        18464     \n",
            "                                                                 \n",
            " max_pooling2d_31 (MaxPoolin  (None, 12, 12, 32)       0         \n",
            " g2D)                                                            \n",
            "                                                                 \n",
            " flatten_7 (Flatten)         (None, 4608)              0         \n",
            "                                                                 \n",
            " dense_28 (Dense)            (None, 256)               1179904   \n",
            "                                                                 \n",
            " dense_29 (Dense)            (None, 128)               32896     \n",
            "                                                                 \n",
            " dense_30 (Dense)            (None, 64)                8256      \n",
            "                                                                 \n",
            " dense_31 (Dense)            (None, 2)                 130       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 1,464,610\n",
            "Trainable params: 1,464,610\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "Epoch 1/20\n",
            "32/32 [==============================] - 46s 1s/step - loss: 0.7057 - accuracy: 0.5085 - val_loss: 0.6895 - val_accuracy: 0.5000\n",
            "Epoch 2/20\n",
            "32/32 [==============================] - 42s 1s/step - loss: 0.6919 - accuracy: 0.5205 - val_loss: 0.6934 - val_accuracy: 0.5000\n",
            "Epoch 3/20\n",
            "32/32 [==============================] - 41s 1s/step - loss: 0.6929 - accuracy: 0.5160 - val_loss: 0.6920 - val_accuracy: 0.5233\n",
            "Epoch 4/20\n",
            "32/32 [==============================] - 42s 1s/step - loss: 0.6938 - accuracy: 0.5125 - val_loss: 0.6942 - val_accuracy: 0.5000\n",
            "Epoch 5/20\n",
            "32/32 [==============================] - 41s 1s/step - loss: 0.6931 - accuracy: 0.5060 - val_loss: 0.6932 - val_accuracy: 0.5167\n",
            "Epoch 6/20\n",
            "32/32 [==============================] - 42s 1s/step - loss: 0.6921 - accuracy: 0.5145 - val_loss: 0.6842 - val_accuracy: 0.5000\n",
            "Epoch 7/20\n",
            "32/32 [==============================] - 41s 1s/step - loss: 0.6767 - accuracy: 0.5485 - val_loss: 0.6799 - val_accuracy: 0.6217\n",
            "Epoch 8/20\n",
            "32/32 [==============================] - 41s 1s/step - loss: 0.6597 - accuracy: 0.6059 - val_loss: 0.5878 - val_accuracy: 0.7333\n",
            "Epoch 9/20\n",
            "32/32 [==============================] - 41s 1s/step - loss: 0.6473 - accuracy: 0.6169 - val_loss: 0.5812 - val_accuracy: 0.7283\n",
            "Epoch 10/20\n",
            "32/32 [==============================] - 41s 1s/step - loss: 0.6477 - accuracy: 0.6214 - val_loss: 0.6340 - val_accuracy: 0.6700\n",
            "Epoch 11/20\n",
            "32/32 [==============================] - 42s 1s/step - loss: 0.6522 - accuracy: 0.6049 - val_loss: 0.5972 - val_accuracy: 0.7200\n",
            "Epoch 12/20\n",
            "32/32 [==============================] - 42s 1s/step - loss: 0.6437 - accuracy: 0.6414 - val_loss: 0.5882 - val_accuracy: 0.7300\n",
            "Epoch 13/20\n",
            "32/32 [==============================] - 41s 1s/step - loss: 0.6338 - accuracy: 0.6354 - val_loss: 0.5719 - val_accuracy: 0.7317\n",
            "Epoch 14/20\n",
            "32/32 [==============================] - 42s 1s/step - loss: 0.6251 - accuracy: 0.6558 - val_loss: 0.5803 - val_accuracy: 0.7283\n",
            "Epoch 15/20\n",
            "32/32 [==============================] - 41s 1s/step - loss: 0.6278 - accuracy: 0.6354 - val_loss: 0.5655 - val_accuracy: 0.7483\n",
            "Epoch 16/20\n",
            "32/32 [==============================] - 42s 1s/step - loss: 0.6226 - accuracy: 0.6479 - val_loss: 0.5757 - val_accuracy: 0.7383\n",
            "Epoch 17/20\n",
            "32/32 [==============================] - 41s 1s/step - loss: 0.6159 - accuracy: 0.6528 - val_loss: 0.5330 - val_accuracy: 0.7567\n",
            "Epoch 18/20\n",
            "32/32 [==============================] - 42s 1s/step - loss: 0.6195 - accuracy: 0.6648 - val_loss: 0.5462 - val_accuracy: 0.7367\n",
            "Epoch 19/20\n",
            "32/32 [==============================] - 41s 1s/step - loss: 0.6118 - accuracy: 0.6678 - val_loss: 0.5250 - val_accuracy: 0.7650\n",
            "Epoch 20/20\n",
            "32/32 [==============================] - 42s 1s/step - loss: 0.6039 - accuracy: 0.6743 - val_loss: 0.5555 - val_accuracy: 0.7317\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#@markdown ## Evaluate Model\n",
        "\n",
        "for h in hist.keys():\n",
        "    his = hist[h]\n",
        "\n",
        "    plt.style.use(\"ggplot\")\n",
        "\n",
        "    plt.subplot(2, 1, 1)\n",
        "    plt.title(f\"{h} Images\")\n",
        "    plt.plot(his.history['loss'], label=\"Train Loss\")\n",
        "    plt.plot(his.history['val_loss'], label=\"Valid Loss\")\n",
        "    plt.legend()\n",
        "    plt.xlabel(\"Epochs\")\n",
        "    plt.ylabel(\"Loss\")\n",
        "\n",
        "    plt.subplot(2, 1, 2)\n",
        "    plt.plot(his.history['accuracy'], label=\"Train Accuracy\")\n",
        "    plt.plot(his.history['val_accuracy'], label=\"Valid Accuracy\")\n",
        "    plt.legend()\n",
        "    plt.xlabel(\"Epochs\")\n",
        "    plt.ylabel(\"Accuracy\")\n",
        "\n",
        "    plt.show()\n",
        "\n",
        "    print(h.upper(), \"\\nTrain:\", his.history['accuracy'][-1], \"\\nValid:\", his.history['val_accuracy'][-1])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "cellView": "form",
        "id": "pBTPt2sN7oeb",
        "outputId": "0dc72ff2-d75e-40bf-92dd-98f85031be2d"
      },
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "REAL \n",
            "Train: 0.8026973009109497 \n",
            "Valid: 0.8133333325386047\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "SYNTHESIZED \n",
            "Train: 0.6743256449699402 \n",
            "Valid: 0.7316666841506958\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#@markdown ## Compare Models\n",
        "\n",
        "plt.style.use(\"ggplot\")\n",
        "plt.subplot(1, 1, 1)\n",
        "plt.title(f\"Comparison of Models\")\n",
        "\n",
        "for h in hist.keys():\n",
        "    his = hist[h]\n",
        "    plt.plot(his.history['val_accuracy'], label=f\"{h.capitalize()} Accuracy\")\n",
        "\n",
        "plt.legend()\n",
        "plt.xlabel(\"Epochs\")\n",
        "plt.ylabel(\"Accuracy\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "cellView": "form",
        "id": "LKky7buSz3V3",
        "outputId": "4735bd95-2c9d-47d9-a5c4-2053f386ef6c"
      },
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}