MMGeneration_demo.ipynb

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      "source": [
        "GitHub<br>\n",
        "https://github.com/open-mmlab/mmgeneration<br>\n",
        "<br>\n",
        "<a href=\"https://colab.research.google.com/github/kaz12tech/ai_demos/blob/master/MMGeneration_demo.ipynb\" target=\"_blank\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "894c0QeEFKit"
      },
      "source": [
        "# 環境セットアップ"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iWBmluQQx-MK"
      },
      "source": [
        "## GPU確認"
      ]
    },
    {
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        "outputId": "3ac1a3d0-0965-4331-8350-59180a225d03"
      },
      "outputs": [],
      "source": [
        "!nvidia-smi"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TmL3LoMMGcNF"
      },
      "source": [
        "## Githubからソースコード取得"
      ]
    },
    {
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        "outputId": "72afe412-7813-46ad-be2d-5eea1e820dec"
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      "outputs": [],
      "source": [
        "%cd /content\n",
        "\n",
        "!git clone https://github.com/open-mmlab/mmgeneration.git"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2-2Fh1sOHdnn"
      },
      "source": [
        "## ライブラリのインストール"
      ]
    },
    {
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      "outputs": [],
      "source": [
        "%cd /content/mmgeneration\n",
        "\n",
        "!pip install moviepy\n",
        "!pip3 install openmim\n",
        "!mim install mmcv-full\n",
        "\n",
        "!pip install -e .[all]"
      ]
    },
    {
      "cell_type": "markdown",
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        "id": "XD205dUu6KAQ"
      },
      "source": [
        "# synthesized images with unconditional GAN"
      ]
    },
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      "outputs": [],
      "source": [
        "import mmcv\n",
        "from mmgen.apis import init_model, sample_unconditional_model\n",
        "\n",
        "# Specify the path to model config and checkpoint file\n",
        "config_file = 'configs/styleganv2/stylegan2_c2_ffhq_1024_b4x8.py'\n",
        "# you can download this checkpoint in advance and use a local file path.\n",
        "checkpoint_file = 'https://download.openmmlab.com/mmgen/stylegan2/stylegan2_c2_ffhq_1024_b4x8_20210407_150045-618c9024.pth'\n",
        "\n",
        "device = 'cuda:0'\n",
        "# init a generatvie\n",
        "model = init_model(config_file, checkpoint_file, device=device)\n",
        "# sample images\n",
        "fake_imgs = sample_unconditional_model(model, 4)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
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          "base_uri": "https://localhost:8080/",
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        },
        "id": "oew7yPXE61Bz",
        "outputId": "6cb85659-b1b1-45dd-fc81-5042d4f70400"
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      "outputs": [],
      "source": [
        "from torchvision import utils\n",
        "import matplotlib.pyplot as plt\n",
        "import os\n",
        "from IPython.display import Image, display\n",
        "\n",
        "results = (fake_imgs[:, [2, 1, 0]] + 1.) / 2.\n",
        "save_path = './outputs/unconditional/results.jpg'\n",
        "\n",
        "# save images\n",
        "mmcv.mkdir_or_exist(os.path.dirname(save_path))\n",
        "utils.save_image(\n",
        "    results, save_path, ncol=1, padding=0)\n",
        "\n",
        "# display\n",
        "display(Image(save_path))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g_d6kvkd-etX"
      },
      "source": [
        "# synthesized images with conditional GAN"
      ]
    },
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      "source": [
        "from mmgen.apis import init_model, sample_conditional_model\n",
        "\n",
        "# Specify the path to model config and checkpoint file\n",
        "config_file = 'configs/sagan/sagan_128_woReLUinplace_noaug_bigGAN_Glr-1e-4_Dlr-4e-4_ndisc1_imagenet1k_b32x8.py'\n",
        "# you can download this checkpoint in advance and use a local file path.\n",
        "checkpoint_file = 'https://download.openmmlab.com/mmgen/sagan/sagan_128_woReLUinplace_noaug_bigGAN_imagenet1k_b32x8_Glr1e-4_Dlr-4e-4_ndisc1_20210818_210232-3f5686af.pth'\n",
        "\n",
        "device = 'cuda:0'\n",
        "# init a generatvie\n",
        "model = init_model(config_file, checkpoint_file, device=device)\n",
        "\n",
        "# sample images with specific labels\n",
        "# label https://gist.github.com/yrevar/942d3a0ac09ec9e5eb3a\n",
        "fake_imgs = sample_conditional_model(model, 4, label=[0, 1, 2, 3])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145
        },
        "id": "d8xpnPDa-mNU",
        "outputId": "ac1d47e0-7aba-4eb1-b3dd-36b1023d70fb"
      },
      "outputs": [],
      "source": [
        "results = (fake_imgs[:, [2, 1, 0]] + 1.) / 2.\n",
        "save_path = './outputs/conditional/results.jpg'\n",
        "\n",
        "# save images\n",
        "mmcv.mkdir_or_exist(os.path.dirname(save_path))\n",
        "utils.save_image(\n",
        "    results, save_path, ncol=1, padding=0)\n",
        "\n",
        "# display\n",
        "display(Image(save_path))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5uJ_xZJj_f9q"
      },
      "source": [
        "# image translation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hO0mLRtz_cdP",
        "outputId": "d3f9e9e1-6ca3-4dc8-81f3-2522d73a0186"
      },
      "outputs": [],
      "source": [
        "from mmgen.apis import init_model, sample_img2img_model\n",
        "\n",
        "config_file = 'configs/pix2pix/pix2pix_vanilla_unet_bn_wo_jitter_flip_edges2shoes_b1x4_190k.py'\n",
        "checkpoint_file = 'https://download.openmmlab.com/mmgen/pix2pix/refactor/pix2pix_vanilla_unet_bn_wo_jitter_flip_1x4_186840_edges2shoes_convert-bgr_20210902_170902-0c828552.pth'\n",
        "\n",
        "# Specify the path to image you want to translate\n",
        "image_path = 'tests/data/paired/test/33_AB.jpg'\n",
        "device = 'cuda:0'\n",
        "# init a generatvie\n",
        "model = init_model(config_file, checkpoint_file, device=device)\n",
        "# translate a single image\n",
        "translated_image = sample_img2img_model(model, image_path, target_domain='photo')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 273
        },
        "id": "L-QEV5oS_qGN",
        "outputId": "08da327e-3dcf-43d5-b610-7e95efc294b7"
      },
      "outputs": [],
      "source": [
        "results = (translated_image[:, [2, 1, 0]] + 1.) / 2.\n",
        "save_path = './outputs/translation/results.jpg'\n",
        "\n",
        "# save images\n",
        "mmcv.mkdir_or_exist(os.path.dirname(save_path))\n",
        "utils.save_image(\n",
        "    results, save_path, ncol=1, padding=0)\n",
        "\n",
        "# display\n",
        "display(Image(save_path))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FkBckvBdA5QF"
      },
      "source": [
        "# Interpolation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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        "colab": {
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        "id": "wBDIxv79IxV8",
        "outputId": "5ebf0600-5b57-4e88-eeef-faecdc019c38"
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      "outputs": [],
      "source": [
        "!python apps/interpolate_sample.py \\\n",
        "  configs/styleganv2/stylegan2_c2_ffhq_256_b4x8_800k.py \\\n",
        "  https://download.openmmlab.com/mmgen/stylegan2/stylegan2_c2_ffhq_256_b4x8_20210407_160709-7890ae1f.pth \\\n",
        "  --export-video \\\n",
        "  --samples-path outputs/interpolation \\\n",
        "  --endpoint 6 \\\n",
        "  --interval 60 \\\n",
        "  --space z \\\n",
        "  --seed 12 \\\n",
        "  --sample-cfg truncation=0.8"
      ]
    },
    {
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        },
        "id": "QIHnYUyJREPV",
        "outputId": "3989350d-a643-43f4-d9e2-7d33f685c094"
      },
      "outputs": [],
      "source": [
        "from moviepy.video.fx.resize import resize\n",
        "from moviepy.editor import VideoFileClip\n",
        "\n",
        "# display\n",
        "clip = VideoFileClip('/content/mmgeneration/outputs/interpolation/lerp.mp4')\n",
        "clip = resize(clip, height=420)\n",
        "clip.ipython_display()"
      ]
    }
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