Computer_Vision/Chapter13/Customizing_StyleGAN2.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "view-in-github"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/PacktPublishing/Hands-On-Computer-Vision-with-PyTorch/blob/master/Chapter13/Customizing_StyleGAN2.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272
        },
        "id": "oAmCSXY6dv5W",
        "outputId": "608aacb0-6c33-4f2e-aa12-1147bbf34279"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Cloning into 'pytorch_stylegan_encoder'...\n",
            "remote: Enumerating objects: 71, done.\u001b[K\n",
            "remote: Counting objects: 100% (71/71), done.\u001b[K\n",
            "remote: Compressing objects: 100% (64/64), done.\u001b[K\n",
            "remote: Total 358 (delta 40), reused 15 (delta 6), pack-reused 287\u001b[K\n",
            "Receiving objects: 100% (358/358), 56.89 MiB | 19.93 MiB/s, done.\n",
            "Resolving deltas: 100% (179/179), done.\n",
            "/content/pytorch_stylegan_encoder\n",
            "Submodule 'InterFaceGAN' (git://github.com/ShenYujun/InterFaceGAN.git) registered for path 'InterFaceGAN'\n",
            "Cloning into '/content/pytorch_stylegan_encoder/InterFaceGAN'...\n",
            "Submodule path 'InterFaceGAN': checked out 'b707e942187f464251f855c92f7009b8cf13bf03'\n",
            "\u001b[K     |████████████████████████████████| 36.7MB 82kB/s \n",
            "\u001b[K     |████████████████████████████████| 61kB 9.6MB/s \n",
            "\u001b[K     |████████████████████████████████| 102kB 13.9MB/s \n",
            "\u001b[?25h  Building wheel for contextvars (setup.py) ... \u001b[?25l\u001b[?25hdone\n"
          ]
        }
      ],
      "source": [
        "import os\n",
        "if not os.path.exists('pytorch_stylegan_encoder'):\n",
        "    !git clone https://github.com/jacobhallberg/pytorch_stylegan_encoder.git\n",
        "    %cd pytorch_stylegan_encoder\n",
        "    !git submodule update --init --recursive\n",
        "    !wget -q https://github.com/jacobhallberg/pytorch_stylegan_encoder/releases/download/v1.0/trained_models.zip\n",
        "    !unzip -q trained_models.zip\n",
        "    !rm trained_models.zip\n",
        "    !pip install -qU torch_snippets\n",
        "    !mv trained_models/stylegan_ffhq.pth InterFaceGAN/models/pretrain\n",
        "else:\n",
        "    %cd pytorch_stylegan_encoder\n",
        "    \n",
        "from torch_snippets import *"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 170
        },
        "id": "PhzYGPEId3F-",
        "outputId": "f42c9b58-4ebb-4f22-8fd5-77965c62f9ca"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[2020-10-13 05:39:11,237][INFO] Loading pytorch model from `InterFaceGAN/models/pretrain/stylegan_ffhq.pth`.\n",
            "[2020-10-13 05:39:11,514][INFO] Successfully loaded!\n",
            "[2020-10-13 05:39:11,516][INFO]   `lod` of the loaded model is 0.0.\n",
            "[2020-10-13 05:39:21,174][INFO] Loading pytorch model from `InterFaceGAN/models/pretrain/stylegan_ffhq.pth`.\n",
            "[2020-10-13 05:39:21,438][INFO] Successfully loaded!\n",
            "[2020-10-13 05:39:21,439][INFO]   `lod` of the loaded model is 0.0.\n",
            "[2020-10-13 05:39:21,693][INFO] Loading pytorch model from `InterFaceGAN/models/pretrain/stylegan_ffhq.pth`.\n",
            "[2020-10-13 05:39:21,926][INFO] Successfully loaded!\n",
            "[2020-10-13 05:39:21,927][INFO]   `lod` of the loaded model is 0.0.\n"
          ]
        }
      ],
      "source": [
        "from InterFaceGAN.models.stylegan_generator import StyleGANGenerator\n",
        "from models.latent_optimizer import PostSynthesisProcessing\n",
        "\n",
        "synthesizer = StyleGANGenerator(\"stylegan_ffhq\").model.synthesis\n",
        "mapper = StyleGANGenerator(\"stylegan_ffhq\").model.mapping\n",
        "trunc = StyleGANGenerator(\"stylegan_ffhq\").model.truncation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ULK2-BQId4sz"
      },
      "outputs": [],
      "source": [
        "post_processing = PostSynthesisProcessing()\n",
        "post_process = lambda image: post_processing(image).detach().cpu().numpy().astype(np.uint8)[0]\n",
        "\n",
        "def latent2image(latent):\n",
        "    img = post_process(synthesizer(latent))\n",
        "    img = img.transpose(1,2,0)\n",
        "    return img"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 303
        },
        "id": "wRq8ykHtd6Tg",
        "outputId": "dcf3adac-11a0-46e5-8e00-fad80b05ae3f"
      },
      "outputs": [
        {
          "data": {
            "image/png": "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
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light",
            "tags": []
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "rand_latents = torch.randn(1,512).cuda()\n",
        "show(latent2image(trunc(mapper(rand_latents))), sz=5)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        },
        "id": "7dl5EIlieKvH",
        "outputId": "f62b295f-4162-4b8b-99ab-0205a41277a7"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Cloning into 'stylegan-encoder'...\n",
            "remote: Enumerating objects: 105, done.\u001b[K\n",
            "remote: Total 105 (delta 0), reused 0 (delta 0), pack-reused 105\u001b[K\n",
            "Receiving objects: 100% (105/105), 10.39 MiB | 10.51 MiB/s, done.\n",
            "Resolving deltas: 100% (27/27), done.\n"
          ]
        }
      ],
      "source": [
        "!wget -q https://www.dropbox.com/s/lpw10qawsc5ipbn/MyImage.JPG -O MyImage.jpg\n",
        "!git clone https://github.com/sizhky/stylegan-encoder.git\n",
        "!mkdir -p stylegan-encoder/raw_images\n",
        "!mkdir -p stylegan-encoder/aligned_images\n",
        "!mv MyImage.jpg stylegan-encoder/raw_images"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 88
        },
        "id": "8y0FhdnqenUn",
        "outputId": "dfba1af1-8cba-4025-b457-cb6057cd7cf9"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "2020-10-13 05:42:04.958242: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1\n",
            "Downloading data from http://dlib.net/files/shape_predictor_68_face_landmarks.dat.bz2\n",
            "64045056/64040097 [==============================] - 38s 1us/step\n"
          ]
        }
      ],
      "source": [
        "!python stylegan-encoder/align_images.py stylegan-encoder/raw_images/ stylegan-encoder/aligned_images/\n",
        "!mv stylegan-encoder/aligned_images/* ./MyImage.jpg"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 596
        },
        "id": "lpfu5rONerWG",
        "outputId": "8da553b8-6691-4ce6-d8d9-20914818da54"
      },
      "outputs": [
        {
          "data": {
            "image/png": "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
            "text/plain": [
              "<Figure size 288x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light",
            "tags": []
          },
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Optimizing Latents.\n",
            "[2020-10-13 05:54:26,513][INFO] Loading pytorch model from `InterFaceGAN/models/pretrain/stylegan_ffhq.pth`.\n",
            "[2020-10-13 05:54:26,773][INFO] Successfully loaded!\n",
            "[2020-10-13 05:54:26,773][INFO]   `lod` of the loaded model is 0.0.\n",
            "Step: 2999, Loss: 1.732578992843628: 100% 3000/3000 [11:01<00:00,  4.53it/s]\n"
          ]
        },
        {
          "data": {
            "image/png": "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
            "text/plain": [
              "<Figure size 288x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light",
            "tags": []
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "from PIL import Image\n",
        "img = Image.open('MyImage.jpg')\n",
        "show(np.array(img), sz=4, title='original')\n",
        "\n",
        "!python encode_image.py ./MyImage.jpg\\\n",
        "    pred_dlatents_myImage.npy\\\n",
        "    --use_latent_finder true\\\n",
        "    --image_to_latent_path ./trained_models/image_to_latent.pt\n",
        "\n",
        "pred_dlatents = np.load('pred_dlatents_myImage.npy')\n",
        "pred_dlatent = torch.from_numpy(pred_dlatents).float().cuda()\n",
        "pred_image = latent2image(pred_dlatent)\n",
        "show(pred_image, sz=4, title='synthesized')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 579
        },
        "id": "hbXD0Ud4e41X",
        "outputId": "0283e6a4-e252-47f8-acc4-cf2fbef8f60a"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "2020-10-13 06:13:42.436 | INFO     | torch_snippets.loader:subplots:359 - plotting 4 images in a grid of 2x2 @ (7, 8)\n"
          ]
        },
        {
          "data": {
            "image/png": "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
            "text/plain": [
              "<Figure size 504x576 with 4 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light",
            "tags": []
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "idxs_to_swap = slice(0,3)\n",
        "my_latents = torch.Tensor(np.load('pred_dlatents_myImage.npy', allow_pickle=True))\n",
        "\n",
        "A, B = latent2image(my_latents.cuda()), latent2image(trunc(mapper(rand_latents)))\n",
        "generated_image_latents = trunc(mapper(rand_latents))\n",
        "\n",
        "x = my_latents.clone()\n",
        "x[:,idxs_to_swap] = generated_image_latents[:,idxs_to_swap]\n",
        "a = latent2image(x.float().cuda())\n",
        "\n",
        "x = generated_image_latents.clone()\n",
        "x[:,idxs_to_swap] = my_latents[:,idxs_to_swap]\n",
        "b = latent2image(x.float().cuda())\n",
        "\n",
        "subplots([A,a,B,b], figsize=(7,8), nc=2, suptitle='Transfer high level features')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "id": "SDB-69Idf53_",
        "outputId": "9e2495c3-3518-4577-9476-c20d25540f16"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "slice(0, 2, None)"
            ]
          },
          "execution_count": 37,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        }
      ],
      "source": [
        "idxs_to_swap"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 579
        },
        "id": "QmK_fDY7gAP7",
        "outputId": "642c805c-0135-412b-d9d5-ab03be8b692d"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "2020-10-13 06:13:52.663 | INFO     | torch_snippets.loader:subplots:359 - plotting 4 images in a grid of 2x2 @ (7, 8)\n"
          ]
        },
        {
          "data": {
            "image/png": "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
            "text/plain": [
              "<Figure size 504x576 with 4 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light",
            "tags": []
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "idxs_to_swap = slice(4,15)\n",
        "my_latents = torch.Tensor(np.load('pred_dlatents_myImage.npy', allow_pickle=True))\n",
        "\n",
        "A, B = latent2image(my_latents.cuda()), latent2image(trunc(mapper(rand_latents)))\n",
        "generated_image_latents = trunc(mapper(rand_latents))\n",
        "\n",
        "x = my_latents.clone()\n",
        "x[:,idxs_to_swap] = generated_image_latents[:,idxs_to_swap]\n",
        "a = latent2image(x.float().cuda())\n",
        "\n",
        "x = generated_image_latents.clone()\n",
        "x[:,idxs_to_swap] = my_latents[:,idxs_to_swap]\n",
        "b = latent2image(x.float().cuda())\n",
        "\n",
        "subplots([A,a,B,b], figsize=(7,8), nc=2, suptitle='Transfer granular features')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 579
        },
        "id": "sm1_33A_kNtw",
        "outputId": "0d914a49-b26a-4111-f37a-8776797aff2e"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "2020-10-13 06:14:10.776 | INFO     | torch_snippets.loader:subplots:359 - plotting 4 images in a grid of 2x2 @ (7, 8)\n"
          ]
        },
        {
          "data": {
            "image/png": "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
            "text/plain": [
              "<Figure size 504x576 with 4 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light",
            "tags": []
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "idxs_to_swap = slice(16,18)\n",
        "my_latents = torch.Tensor(np.load('pred_dlatents_myImage.npy', allow_pickle=True))\n",
        "\n",
        "A, B = latent2image(my_latents.cuda()), latent2image(trunc(mapper(rand_latents)))\n",
        "generated_image_latents = trunc(mapper(rand_latents))\n",
        "\n",
        "x = my_latents.clone()\n",
        "x[:,idxs_to_swap] = generated_image_latents[:,idxs_to_swap]\n",
        "a = latent2image(x.float().cuda())\n",
        "\n",
        "x = generated_image_latents.clone()\n",
        "x[:,idxs_to_swap] = my_latents[:,idxs_to_swap]\n",
        "b = latent2image(x.float().cuda())\n",
        "\n",
        "subplots([A,a,B,b], figsize=(7,8), nc=2, suptitle='Transfer general image features')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 854
        },
        "id": "-O4eN-l1kSml",
        "outputId": "09361307-670a-453d-8b50-95a37c36980d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[2020-10-13 06:17:20,651][INFO] Initializing generator.\n",
            "[2020-10-13 06:17:20,887][INFO] Loading pytorch model from `InterFaceGAN/models/pretrain/stylegan_ffhq.pth`.\n",
            "[2020-10-13 06:17:21,161][INFO] Successfully loaded!\n",
            "[2020-10-13 06:17:21,161][INFO]   `lod` of the loaded model is 0.0.\n",
            "[2020-10-13 06:17:24,246][INFO] Preparing boundary.\n",
            "[2020-10-13 06:17:24,250][INFO] Preparing latent codes.\n",
            "[2020-10-13 06:17:24,250][INFO]   Load latent codes from `pred_dlatents_myImage.npy`.\n",
            "[2020-10-13 06:17:24,251][INFO] Editing 1 samples.\n",
            "[2020-10-13 06:17:26,461][INFO] Successfully edited 1 samples.\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "2020-10-13 06:17:26.979 | INFO     | torch_snippets.loader:subplots:359 - plotting 20 images in a grid of 4x5 @ (10, 10)\n"
          ]
        },
        {
          "data": {
            "image/png": "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
            "text/plain": [
              "<Figure size 720x720 with 20 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light",
            "tags": []
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "!python InterFaceGAN/edit.py\\\n",
        " -m stylegan_ffhq\\\n",
        " -o results_new_smile\\\n",
        " -b InterFaceGAN/boundaries/stylegan_ffhq_smile_w_boundary.npy\\\n",
        " -i pred_dlatents_myImage.npy\\\n",
        " -s WP\\\n",
        " --steps 20\n",
        "\n",
        "generated_faces = glob.glob('results_new_smile/*.jpg')\n",
        "\n",
        "subplots([read(im,1) for im in sorted(generated_faces)],figsize=(10,10))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "pvLkri7TmvsM"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "include_colab_link": true,
      "name": "Customizing StyleGAN2.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}