Delete pix2pix.ipynb

pix2pix.ipynb

-{
-  "nbformat": 4,
-  "nbformat_minor": 0,
-  "metadata": {
-    "colab": {
-      "name": "pix2pix",
-      "provenance": [],
-      "collapsed_sections": [],
-      "include_colab_link": true
-    },
-    "kernelspec": {
-      "name": "python3",
-      "display_name": "Python 3"
-    },
-    "accelerator": "GPU"
-  },
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "view-in-github",
-        "colab_type": "text"
-      },
-      "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>"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "7wNjDKdQy35h",
-        "colab_type": "text"
-      },
-      "source": [
-        "# Install"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "TRm-USlsHgEV",
-        "colab_type": "code",
-        "colab": {}
-      },
-      "source": [
-        "!git clone https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix"
-      ],
-      "execution_count": 0,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "Pt3igws3eiVp",
-        "colab_type": "code",
-        "colab": {}
-      },
-      "source": [
-        "import os\n",
-        "os.chdir('pytorch-CycleGAN-and-pix2pix/')"
-      ],
-      "execution_count": 0,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "z1EySlOXwwoa",
-        "colab_type": "code",
-        "colab": {}
-      },
-      "source": [
-        "!pip install -r requirements.txt"
-      ],
-      "execution_count": 0,
-      "outputs": []
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "8daqlgVhw29P",
-        "colab_type": "text"
-      },
-      "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)."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "vrdOettJxaCc",
-        "colab_type": "code",
-        "colab": {}
-      },
-      "source": [
-        "!bash ./datasets/download_pix2pix_dataset.sh facades"
-      ],
-      "execution_count": 0,
-      "outputs": []
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "gdUz4116xhpm",
-        "colab_type": "text"
-      },
-      "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",
-      "metadata": {
-        "id": "GC2DEP4M0OsS",
-        "colab_type": "code",
-        "colab": {}
-      },
-      "source": [
-        "!bash ./scripts/download_pix2pix_model.sh facades_label2photo"
-      ],
-      "execution_count": 0,
-      "outputs": []
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "yFw1kDQBx3LN",
-        "colab_type": "text"
-      },
-      "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",
-      "metadata": {
-        "id": "0sp7TCT2x9dB",
-        "colab_type": "code",
-        "colab": {}
-      },
-      "source": [
-        "!python train.py --dataroot ./datasets/facades --name facades_pix2pix --model pix2pix --direction BtoA"
-      ],
-      "execution_count": 0,
-      "outputs": []
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "9UkcaFZiyASl",
-        "colab_type": "text"
-      },
-      "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",
-      "metadata": {
-        "id": "mey7o6j-0368",
-        "colab_type": "code",
-        "colab": {}
-      },
-      "source": [
-        "!ls checkpoints/"
-      ],
-      "execution_count": 0,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "uCsKkEq0yGh0",
-        "colab_type": "code",
-        "colab": {}
-      },
-      "source": [
-        "!python test.py --dataroot ./datasets/facades --direction BtoA --model pix2pix --name facades_label2photo_pretrained"
-      ],
-      "execution_count": 0,
-      "outputs": []
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "OzSKIPUByfiN",
-        "colab_type": "text"
-      },
-      "source": [
-        "# Visualize"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "9Mgg8raPyizq",
-        "colab_type": "code",
-        "colab": {}
-      },
-      "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)"
-      ],
-      "execution_count": 0,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "0G3oVH9DyqLQ",
-        "colab_type": "code",
-        "colab": {}
-      },
-      "source": [
-        "img = plt.imread('./results/facades_label2photo_pretrained/test_latest/images/100_real_A.png')\n",
-        "plt.imshow(img)"
-      ],
-      "execution_count": 0,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "ErK5OC1j1LH4",
-        "colab_type": "code",
-        "colab": {}
-      },
-      "source": [
-        "img = plt.imread('./results/facades_label2photo_pretrained/test_latest/images/100_real_B.png')\n",
-        "plt.imshow(img)"
-      ],
-      "execution_count": 0,
-      "outputs": []
-    }
-  ]
-}