mri_ct_CycleGAN_pytorch.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5VIGyIus8Vr7"
      },
      "source": [
        "Take a look at the [repository](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix) for more information"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7wNjDKdQy35h"
      },
      "source": [
        "# Install"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!mkdir ~/.kaggle\n",
        "!touch ~/.kaggle/kaggle.json\n",
        "\n",
        "api_token = {\"username\":\"username\",\"key\":\"key\"}\n",
        "\n",
        "import json\n",
        "\n",
        "with open('/root/.kaggle/kaggle.json', 'w') as file:\n",
        "    json.dump(api_token, file)"
      ],
      "metadata": {
        "id": "HQRGkNKruUol"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!kaggle datasets download -d darren2020/ct-to-mri-cgan"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "THr_7pdp1N45",
        "outputId": "f5c26789-9225-4055-ff94-ec9cd055877f"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Warning: Your Kaggle API key is readable by other users on this system! To fix this, you can run 'chmod 600 /root/.kaggle/kaggle.json'\n",
            "Downloading ct-to-mri-cgan.zip to /content\n",
            " 99% 184M/185M [00:10<00:00, 18.4MB/s]\n",
            "100% 185M/185M [00:11<00:00, 17.6MB/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!unzip -qq /content/ct-to-mri-cgan.zip"
      ],
      "metadata": {
        "id": "EFYtSRvJuZtf"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "TRm-USlsHgEV",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "79949c71-14ca-461f-dcdf-d38b6b6035b2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'pytorch-CycleGAN-and-pix2pix'...\n",
            "remote: Enumerating objects: 2513, done.\u001b[K\n",
            "remote: Total 2513 (delta 0), reused 0 (delta 0), pack-reused 2513\u001b[K\n",
            "Receiving objects: 100% (2513/2513), 8.20 MiB | 12.60 MiB/s, done.\n",
            "Resolving deltas: 100% (1575/1575), done.\n"
          ]
        }
      ],
      "source": [
        "!git clone https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "Pt3igws3eiVp"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "os.chdir('pytorch-CycleGAN-and-pix2pix/')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "z1EySlOXwwoa",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c73d33a4-9ae0-404d-a742-cc6bd61dda24"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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          ]
        }
      ],
      "source": [
        "!pip install -r requirements.txt"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8daqlgVhw29P"
      },
      "source": [
        "# Datasets\n",
        "\n",
        "Download one of the official datasets with:\n",
        "\n",
        "-   `bash ./datasets/download_cyclegan_dataset.sh [apple2orange, summer2winter_yosemite, horse2zebra, monet2photo, cezanne2photo, ukiyoe2photo, vangogh2photo, maps, cityscapes, facades, iphone2dslr_flower, ae_photos]`\n",
        "\n",
        "Or use your own dataset by creating the appropriate folders and adding in the images.\n",
        "\n",
        "-   Create a dataset folder under `/dataset` for your dataset.\n",
        "-   Create subfolders `testA`, `testB`, `trainA`, and `trainB` under your dataset's folder. Place any images you want to transform from a to b (cat2dog) in the `testA` folder, images you want to transform from b to a (dog2cat) in the `testB` folder, and do the same for the `trainA` and `trainB` folders."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "vrdOettJxaCc"
      },
      "outputs": [],
      "source": [
        "!bash ./datasets/download_cyclegan_dataset.sh horse2zebra"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gdUz4116xhpm"
      },
      "source": [
        "# Pretrained models\n",
        "\n",
        "Download one of the official pretrained models with:\n",
        "\n",
        "-   `bash ./scripts/download_cyclegan_model.sh [apple2orange, orange2apple, summer2winter_yosemite, winter2summer_yosemite, horse2zebra, zebra2horse, monet2photo, style_monet, style_cezanne, style_ukiyoe, style_vangogh, sat2map, map2sat, cityscapes_photo2label, cityscapes_label2photo, facades_photo2label, facades_label2photo, iphone2dslr_flower]`\n",
        "\n",
        "Or add your own pretrained model to `./checkpoints/{NAME}_pretrained/latest_net_G.pt`"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "B75UqtKhxznS",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5eecba61-0979-4774-c7fb-9fc70f31bbfa"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Note: available models are apple2orange, orange2apple, summer2winter_yosemite, winter2summer_yosemite, horse2zebra, zebra2horse, monet2photo, style_monet, style_cezanne, style_ukiyoe, style_vangogh, sat2map, map2sat, cityscapes_photo2label, cityscapes_label2photo, facades_photo2label, facades_label2photo, iphone2dslr_flower\n",
            "Specified [horse2zebra]\n",
            "WARNING: timestamping does nothing in combination with -O. See the manual\n",
            "for details.\n",
            "\n",
            "--2023-08-12 13:02:34--  http://efrosgans.eecs.berkeley.edu/cyclegan/pretrained_models/horse2zebra.pth\n",
            "Resolving efrosgans.eecs.berkeley.edu (efrosgans.eecs.berkeley.edu)... 128.32.244.190\n",
            "Connecting to efrosgans.eecs.berkeley.edu (efrosgans.eecs.berkeley.edu)|128.32.244.190|:80... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 45575747 (43M)\n",
            "Saving to: ‘./checkpoints/horse2zebra_pretrained/latest_net_G.pth’\n",
            "\n",
            "./checkpoints/horse 100%[===================>]  43.46M  12.3MB/s    in 3.7s    \n",
            "\n",
            "2023-08-12 13:02:39 (11.7 MB/s) - ‘./checkpoints/horse2zebra_pretrained/latest_net_G.pth’ saved [45575747/45575747]\n",
            "\n"
          ]
        }
      ],
      "source": [
        "!bash ./scripts/download_cyclegan_model.sh horse2zebra"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yFw1kDQBx3LN"
      },
      "source": [
        "# Training\n",
        "\n",
        "-   `python train.py --dataroot ./datasets/horse2zebra --name horse2zebra --model cycle_gan`\n",
        "\n",
        "Change the `--dataroot` and `--name` to your own dataset's path and model's name. Use `--gpu_ids 0,1,..` to train on multiple GPUs and `--batch_size` to change the batch size. I've found that a batch size of 16 fits onto 4 V100s and can finish training an epoch in ~90s.\n",
        "\n",
        "Once your model has trained, copy over the last checkpoint to a format that the testing model can automatically detect:\n",
        "\n",
        "Use `cp ./checkpoints/horse2zebra/latest_net_G_A.pth ./checkpoints/horse2zebra/latest_net_G.pth` if you want to transform images from class A to class B and `cp ./checkpoints/horse2zebra/latest_net_G_B.pth ./checkpoints/horse2zebra/latest_net_G.pth` if you want to transform images from class B to class A.\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pwd"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bMBQi42zwINd",
        "outputId": "620f8fb6-5fff-4922-aab5-fb84f4d4a718"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/pytorch-CycleGAN-and-pix2pix\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0sp7TCT2x9dB",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "eef3ecfd-8110-48b9-e38b-fad315bd98e6"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "----------------- Options ---------------\n",
            "               batch_size: 1                             \n",
            "                    beta1: 0.5                           \n",
            "          checkpoints_dir: ./checkpoints                 \n",
            "           continue_train: False                         \n",
            "                crop_size: 256                           \n",
            "                 dataroot: ../Dataset/images             \t[default: None]\n",
            "             dataset_mode: unaligned                     \n",
            "                direction: AtoB                          \n",
            "              display_env: main                          \n",
            "             display_freq: 400                           \n",
            "               display_id: -1                            \t[default: 1]\n",
            "            display_ncols: 4                             \n",
            "             display_port: 8097                          \n",
            "           display_server: http://localhost              \n",
            "          display_winsize: 256                           \n",
            "                    epoch: latest                        \n",
            "              epoch_count: 1                             \n",
            "                 gan_mode: lsgan                         \n",
            "                  gpu_ids: 0                             \n",
            "                init_gain: 0.02                          \n",
            "                init_type: normal                        \n",
            "                 input_nc: 3                             \n",
            "                  isTrain: True                          \t[default: None]\n",
            "                 lambda_A: 10.0                          \n",
            "                 lambda_B: 10.0                          \n",
            "          lambda_identity: 0.5                           \n",
            "                load_iter: 0                             \t[default: 0]\n",
            "                load_size: 286                           \n",
            "                       lr: 0.0002                        \n",
            "           lr_decay_iters: 50                            \n",
            "                lr_policy: linear                        \n",
            "         max_dataset_size: inf                           \n",
            "                    model: cycle_gan                     \n",
            "                 n_epochs: 100                           \n",
            "           n_epochs_decay: 100                           \n",
            "               n_layers_D: 3                             \n",
            "                     name: horse2zebra                   \t[default: experiment_name]\n",
            "                      ndf: 64                            \n",
            "                     netD: basic                         \n",
            "                     netG: resnet_9blocks                \n",
            "                      ngf: 64                            \n",
            "               no_dropout: True                          \n",
            "                  no_flip: False                         \n",
            "                  no_html: False                         \n",
            "                     norm: instance                      \n",
            "              num_threads: 4                             \n",
            "                output_nc: 3                             \n",
            "                    phase: train                         \n",
            "                pool_size: 50                            \n",
            "               preprocess: resize_and_crop               \n",
            "               print_freq: 100                           \n",
            "             save_by_iter: False                         \n",
            "          save_epoch_freq: 5                             \n",
            "         save_latest_freq: 5000                          \n",
            "           serial_batches: False                         \n",
            "                   suffix:                               \n",
            "         update_html_freq: 1000                          \n",
            "                use_wandb: False                         \n",
            "                  verbose: False                         \n",
            "       wandb_project_name: CycleGAN-and-pix2pix          \n",
            "----------------- End -------------------\n",
            "dataset [UnalignedDataset] was created\n",
            "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py:560: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
            "  warnings.warn(_create_warning_msg(\n",
            "The number of training images = 1744\n",
            "initialize network with normal\n",
            "initialize network with normal\n",
            "initialize network with normal\n",
            "initialize network with normal\n",
            "model [CycleGANModel] was created\n",
            "---------- Networks initialized -------------\n",
            "[Network G_A] Total number of parameters : 11.378 M\n",
            "[Network G_B] Total number of parameters : 11.378 M\n",
            "[Network D_A] Total number of parameters : 2.765 M\n",
            "[Network D_B] Total number of parameters : 2.765 M\n",
            "-----------------------------------------------\n",
            "create web directory ./checkpoints/horse2zebra/web...\n",
            "/usr/local/lib/python3.10/dist-packages/torch/optim/lr_scheduler.py:139: UserWarning: Detected call of `lr_scheduler.step()` before `optimizer.step()`. In PyTorch 1.1.0 and later, you should call them in the opposite order: `optimizer.step()` before `lr_scheduler.step()`.  Failure to do this will result in PyTorch skipping the first value of the learning rate schedule. See more details at https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate\n",
            "  warnings.warn(\"Detected call of `lr_scheduler.step()` before `optimizer.step()`. \"\n",
            "learning rate 0.0002000 -> 0.0002000\n",
            "(epoch: 1, iters: 100, time: 0.596, data: 0.279) D_A: 0.600 G_A: 0.454 cycle_A: 0.789 idt_A: 0.490 D_B: 0.322 G_B: 0.126 cycle_B: 0.811 idt_B: 0.356 \n",
            "(epoch: 1, iters: 200, time: 0.628, data: 0.002) D_A: 0.255 G_A: 0.710 cycle_A: 0.717 idt_A: 3.635 D_B: 0.220 G_B: 0.457 cycle_B: 7.845 idt_B: 0.304 \n",
            "(epoch: 1, iters: 300, time: 0.645, data: 0.008) D_A: 0.171 G_A: 0.172 cycle_A: 0.712 idt_A: 0.741 D_B: 0.279 G_B: 0.824 cycle_B: 1.760 idt_B: 0.333 \n"
          ]
        }
      ],
      "source": [
        "!python train.py --dataroot ../Dataset/images --name horse2zebra --model cycle_gan --display_id -1"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9UkcaFZiyASl"
      },
      "source": [
        "# Testing\n",
        "\n",
        "-   `python test.py --dataroot datasets/horse2zebra/testA --name horse2zebra_pretrained --model test --no_dropout`\n",
        "\n",
        "Change the `--dataroot` and `--name` to be consistent with your trained model's configuration.\n",
        "\n",
        "> from https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix:\n",
        "> The option --model test is used for generating results of CycleGAN only for one side. This option will automatically set --dataset_mode single, which only loads the images from one set. On the contrary, using --model cycle_gan requires loading and generating results in both directions, which is sometimes unnecessary. The results will be saved at ./results/. Use --results_dir {directory_path_to_save_result} to specify the results directory.\n",
        "\n",
        "> For your own experiments, you might want to specify --netG, --norm, --no_dropout to match the generator architecture of the trained model."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "uCsKkEq0yGh0"
      },
      "outputs": [],
      "source": [
        "!python test.py --dataroot datasets/horse2zebra/testA --name horse2zebra_pretrained --model test --no_dropout"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OzSKIPUByfiN"
      },
      "source": [
        "# Visualize"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9Mgg8raPyizq"
      },
      "outputs": [],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "img = plt.imread('./results/horse2zebra_pretrained/test_latest/images/n02381460_1010_fake.png')\n",
        "plt.imshow(img)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0G3oVH9DyqLQ"
      },
      "outputs": [],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "img = plt.imread('./results/horse2zebra_pretrained/test_latest/images/n02381460_1010_real.png')\n",
        "plt.imshow(img)"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "provenance": []
    },
    "environment": {
      "name": "tf2-gpu.2-3.m74",
      "type": "gcloud",
      "uri": "gcr.io/deeplearning-platform-release/tf2-gpu.2-3:m74"
    },
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.7.10"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}