pix2pix.ipynb

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   "source": [
    "<a href=\"https://colab.research.google.com/github/bkkaggle/pytorch-CycleGAN-and-pix2pix/blob/master/pix2pix.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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   "cell_type": "markdown",
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   },
   "source": [
    "# Install"
   ]
  },
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   "outputs": [],
   "source": [
    "!git clone https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix"
   ]
  },
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   "metadata": {
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   "outputs": [],
   "source": [
    "import os\n",
    "os.chdir('pytorch-CycleGAN-and-pix2pix/')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "source": [
    "!pip install -r requirements.txt"
   ]
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  {
   "cell_type": "markdown",
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   "source": [
    "# Datasets\n",
    "\n",
    "Download one of the official datasets with:\n",
    "\n",
    "-   `bash ./datasets/download_pix2pix_dataset.sh [cityscapes, night2day, edges2handbags, edges2shoes, facades, maps]`\n",
    "\n",
    "Or use your own dataset by creating the appropriate folders and adding in the images. Follow the instructions [here](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/blob/master/docs/datasets.md#pix2pix-datasets)."
   ]
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  {
   "cell_type": "code",
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   "outputs": [],
   "source": [
    "!bash ./datasets/download_pix2pix_dataset.sh facades"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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    "id": "gdUz4116xhpm"
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   "source": [
    "# Pretrained models\n",
    "\n",
    "Download one of the official pretrained models with:\n",
    "\n",
    "-   `bash ./scripts/download_pix2pix_model.sh [edges2shoes, sat2map, map2sat, facades_label2photo, and day2night]`\n",
    "\n",
    "Or add your own pretrained model to `./checkpoints/{NAME}_pretrained/latest_net_G.pt`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "GC2DEP4M0OsS"
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   "outputs": [],
   "source": [
    "!bash ./scripts/download_pix2pix_model.sh facades_label2photo"
   ]
  },
  {
   "cell_type": "markdown",
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    "id": "yFw1kDQBx3LN"
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   "source": [
    "# Training\n",
    "\n",
    "-   `python train.py --dataroot ./datasets/facades --name facades_pix2pix --model pix2pix --direction BtoA`\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. Add `--direction BtoA` if you want to train a model to transfrom from class B to A."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "0sp7TCT2x9dB"
   },
   "outputs": [],
   "source": [
    "!python train.py --dataroot ./datasets/facades --name facades_pix2pix --model pix2pix --direction BtoA --display_id -1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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    "id": "9UkcaFZiyASl"
   },
   "source": [
    "# Testing\n",
    "\n",
    "-   `python test.py --dataroot ./datasets/facades --direction BtoA --model pix2pix --name facades_pix2pix`\n",
    "\n",
    "Change the `--dataroot`, `--name`, and `--direction` to be consistent with your trained model's configuration and how you want to transform images.\n",
    "\n",
    "> from https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix:\n",
    "> Note that we specified --direction BtoA as Facades dataset's A to B direction is photos to labels.\n",
    "\n",
    "> If you would like to apply a pre-trained model to a collection of input images (rather than image pairs), please use --model test option. See ./scripts/test_single.sh for how to apply a model to Facade label maps (stored in the directory facades/testB).\n",
    "\n",
    "> See a list of currently available models at ./scripts/download_pix2pix_model.sh"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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    "colab": {},
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    "id": "mey7o6j-0368"
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   "outputs": [],
   "source": [
    "!ls checkpoints/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "uCsKkEq0yGh0"
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   "outputs": [],
   "source": [
    "!python test.py --dataroot ./datasets/facades --direction BtoA --model pix2pix --name facades_label2photo_pretrained --use_wandb"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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    "id": "OzSKIPUByfiN"
   },
   "source": [
    "# Visualize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
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   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "img = plt.imread('./results/facades_label2photo_pretrained/test_latest/images/100_fake_B.png')\n",
    "plt.imshow(img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
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   "outputs": [],
   "source": [
    "img = plt.imread('./results/facades_label2photo_pretrained/test_latest/images/100_real_A.png')\n",
    "plt.imshow(img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ErK5OC1j1LH4"
   },
   "outputs": [],
   "source": [
    "img = plt.imread('./results/facades_label2photo_pretrained/test_latest/images/100_real_B.png')\n",
    "plt.imshow(img)"
   ]
  }
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