baseline_styleGAN.ipynb

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    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZxBadeH4NUEx",
        "outputId": "e8055696-6bf9-44f0-86d6-f95564716b37"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting tensorflow_addons\n",
            "  Downloading tensorflow_addons-0.23.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (611 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m611.8/611.8 kB\u001b[0m \u001b[31m4.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from tensorflow_addons) (23.2)\n",
            "Collecting typeguard<3.0.0,>=2.7 (from tensorflow_addons)\n",
            "  Downloading typeguard-2.13.3-py3-none-any.whl (17 kB)\n",
            "Installing collected packages: typeguard, tensorflow_addons\n",
            "Successfully installed tensorflow_addons-0.23.0 typeguard-2.13.3\n"
          ]
        }
      ],
      "source": [
        "!pip install tensorflow_addons"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "from functools import partial\n",
        "\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras import layers\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow_addons.layers import InstanceNormalization\n",
        "\n",
        "import gdown\n",
        "from zipfile import ZipFile"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XOWbu5RZOrDl",
        "outputId": "34fd8060-b2ac-4d9e-ca3b-f6d4c7fb620e"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/tensorflow_addons/utils/tfa_eol_msg.py:23: UserWarning: \n",
            "\n",
            "TensorFlow Addons (TFA) has ended development and introduction of new features.\n",
            "TFA has entered a minimal maintenance and release mode until a planned end of life in May 2024.\n",
            "Please modify downstream libraries to take dependencies from other repositories in our TensorFlow community (e.g. Keras, Keras-CV, and Keras-NLP). \n",
            "\n",
            "For more information see: https://github.com/tensorflow/addons/issues/2807 \n",
            "\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def log2(x):\n",
        "    return int(np.log2(x))\n",
        "\n",
        "\n",
        "# we use different batch size for different resolution, so larger image size\n",
        "# could fit into GPU memory. The keys is image resolution in log2\n",
        "batch_sizes = {2: 16, 3: 16, 4: 16, 5: 16, 6: 16, 7: 8, 8: 4, 9: 2, 10: 1}\n",
        "# We adjust the train step accordingly\n",
        "train_step_ratio = {k: batch_sizes[2] / v for k, v in batch_sizes.items()}"
      ],
      "metadata": {
        "id": "TZgNDerqOtF0"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!mkdir -p ./data\n",
        "!wget -O ./data/afhq.zip -N https://www.dropbox.com/s/t9l9o3vsx2jai3z/afhq.zip?dl=0\n",
        "!unzip ./data/afhq.zip -d ./data\n",
        "!rm ./data/afhq.zip"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-pdfRRq2O2rs",
        "outputId": "1b749e35-0212-49a0-94e9-a750c68fee1d"
      },
      "execution_count": 4,
      "outputs": [
        {
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          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "! rm -rf ./data/afhq/train/cat ./data/afhq/train/wild ./data/afhq/val/cat ./data/afhq/val/wild"
      ],
      "metadata": {
        "id": "i56tZ5pZO6Cc"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ds_train = keras.utils.image_dataset_from_directory(\n",
        "    \"/content/data/afhq/train/dog\", label_mode=None, image_size=(64, 64), batch_size=32\n",
        ")\n",
        "\n",
        "\n",
        "def resize_image(res, image):\n",
        "    # only downsampling, so use nearest neighbor that is faster to run\n",
        "    image = tf.image.resize(\n",
        "        image, (res, res), method=tf.image.ResizeMethod.NEAREST_NEIGHBOR\n",
        "    )\n",
        "    image = tf.cast(image, tf.float32) / 127.5 - 1.0\n",
        "    return image\n",
        "\n",
        "\n",
        "def create_dataloader(res):\n",
        "    batch_size = batch_sizes[log2(res)]\n",
        "    # NOTE: we unbatch the dataset so we can `batch()` it again with the `drop_remainder=True` option\n",
        "    # since the model only supports a single batch size\n",
        "    dl = ds_train.map(partial(resize_image, res), num_parallel_calls=tf.data.AUTOTUNE).unbatch()\n",
        "    dl = dl.shuffle(200).batch(batch_size, drop_remainder=True).prefetch(1).repeat()\n",
        "    return dl"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "K4zGhQZqO9LN",
        "outputId": "9e6e4437-e42d-4235-a2ef-8c6d8a1d0b47"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found 4739 files belonging to 1 classes.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def plot_images(images, log2_res, fname=\"\"):\n",
        "    scales = {2: 0.5, 3: 1, 4: 2, 5: 3, 6: 4, 7: 5, 8: 6, 9: 7, 10: 8}\n",
        "    scale = scales[log2_res]\n",
        "\n",
        "    grid_col = min(images.shape[0], int(32 // scale))\n",
        "    grid_row = 1\n",
        "\n",
        "    f, axarr = plt.subplots(\n",
        "        grid_row, grid_col, figsize=(grid_col * scale, grid_row * scale)\n",
        "    )\n",
        "\n",
        "    for row in range(grid_row):\n",
        "        ax = axarr if grid_row == 1 else axarr[row]\n",
        "        for col in range(grid_col):\n",
        "            ax[col].imshow(images[row * grid_col + col])\n",
        "            ax[col].axis(\"off\")\n",
        "    plt.show()\n",
        "    if fname:\n",
        "        f.savefig(fname)"
      ],
      "metadata": {
        "id": "lJ-otaPYPfmo"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Custom Layers"
      ],
      "metadata": {
        "id": "L-jigaITPjhP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def fade_in(alpha, a, b):\n",
        "    return alpha * a + (1.0 - alpha) * b\n",
        "\n",
        "\n",
        "def wasserstein_loss(y_true, y_pred):\n",
        "    return -tf.reduce_mean(y_true * y_pred)\n",
        "\n",
        "\n",
        "def pixel_norm(x, epsilon=1e-8):\n",
        "    return x / tf.math.sqrt(tf.reduce_mean(x ** 2, axis=-1, keepdims=True) + epsilon)\n",
        "\n",
        "\n",
        "def minibatch_std(input_tensor, epsilon=1e-8):\n",
        "    n, h, w, c = tf.shape(input_tensor)\n",
        "    group_size = tf.minimum(4, n)\n",
        "    x = tf.reshape(input_tensor, [group_size, -1, h, w, c])\n",
        "    group_mean, group_var = tf.nn.moments(x, axes=(0), keepdims=False)\n",
        "    group_std = tf.sqrt(group_var + epsilon)\n",
        "    avg_std = tf.reduce_mean(group_std, axis=[1, 2, 3], keepdims=True)\n",
        "    x = tf.tile(avg_std, [group_size, h, w, 1])\n",
        "    return tf.concat([input_tensor, x], axis=-1)\n",
        "\n",
        "\n",
        "class EqualizedConv(layers.Layer):\n",
        "    def __init__(self, out_channels, kernel=3, gain=2, **kwargs):\n",
        "        super().__init__(**kwargs)\n",
        "        self.kernel = kernel\n",
        "        self.out_channels = out_channels\n",
        "        self.gain = gain\n",
        "        self.pad = kernel != 1\n",
        "\n",
        "    def build(self, input_shape):\n",
        "        self.in_channels = input_shape[-1]\n",
        "        initializer = keras.initializers.RandomNormal(mean=0.0, stddev=1.0)\n",
        "        self.w = self.add_weight(\n",
        "            shape=[self.kernel, self.kernel, self.in_channels, self.out_channels],\n",
        "            initializer=initializer,\n",
        "            trainable=True,\n",
        "            name=\"kernel\",\n",
        "        )\n",
        "        self.b = self.add_weight(\n",
        "            shape=(self.out_channels,), initializer=\"zeros\", trainable=True, name=\"bias\"\n",
        "        )\n",
        "        fan_in = self.kernel * self.kernel * self.in_channels\n",
        "        self.scale = tf.sqrt(self.gain / fan_in)\n",
        "\n",
        "    def call(self, inputs):\n",
        "        if self.pad:\n",
        "            x = tf.pad(inputs, [[0, 0], [1, 1], [1, 1], [0, 0]], mode=\"REFLECT\")\n",
        "        else:\n",
        "            x = inputs\n",
        "        output = (\n",
        "            tf.nn.conv2d(x, self.scale * self.w, strides=1, padding=\"VALID\") + self.b\n",
        "        )\n",
        "        return output\n",
        "\n",
        "\n",
        "class EqualizedDense(layers.Layer):\n",
        "    def __init__(self, units, gain=2, learning_rate_multiplier=1, **kwargs):\n",
        "        super().__init__(**kwargs)\n",
        "        self.units = units\n",
        "        self.gain = gain\n",
        "        self.learning_rate_multiplier = learning_rate_multiplier\n",
        "\n",
        "    def build(self, input_shape):\n",
        "        self.in_channels = input_shape[-1]\n",
        "        initializer = keras.initializers.RandomNormal(\n",
        "            mean=0.0, stddev=1.0 / self.learning_rate_multiplier\n",
        "        )\n",
        "        self.w = self.add_weight(\n",
        "            shape=[self.in_channels, self.units],\n",
        "            initializer=initializer,\n",
        "            trainable=True,\n",
        "            name=\"kernel\",\n",
        "        )\n",
        "        self.b = self.add_weight(\n",
        "            shape=(self.units,), initializer=\"zeros\", trainable=True, name=\"bias\"\n",
        "        )\n",
        "        fan_in = self.in_channels\n",
        "        self.scale = tf.sqrt(self.gain / fan_in)\n",
        "\n",
        "    def call(self, inputs):\n",
        "        output = tf.add(tf.matmul(inputs, self.scale * self.w), self.b)\n",
        "        return output * self.learning_rate_multiplier\n",
        "\n",
        "\n",
        "class AddNoise(layers.Layer):\n",
        "    def build(self, input_shape):\n",
        "        n, h, w, c = input_shape[0]\n",
        "        initializer = keras.initializers.RandomNormal(mean=0.0, stddev=1.0)\n",
        "        self.b = self.add_weight(\n",
        "            shape=[1, 1, 1, c], initializer=initializer, trainable=True, name=\"kernel\"\n",
        "        )\n",
        "\n",
        "    def call(self, inputs):\n",
        "        x, noise = inputs\n",
        "        output = x + self.b * noise\n",
        "        return output\n",
        "\n",
        "\n",
        "class AdaIN(layers.Layer):\n",
        "    def __init__(self, gain=1, **kwargs):\n",
        "        super().__init__(**kwargs)\n",
        "        self.gain = gain\n",
        "\n",
        "    def build(self, input_shapes):\n",
        "        x_shape = input_shapes[0]\n",
        "        w_shape = input_shapes[1]\n",
        "\n",
        "        self.w_channels = w_shape[-1]\n",
        "        self.x_channels = x_shape[-1]\n",
        "\n",
        "        self.dense_1 = EqualizedDense(self.x_channels, gain=1)\n",
        "        self.dense_2 = EqualizedDense(self.x_channels, gain=1)\n",
        "\n",
        "    def call(self, inputs):\n",
        "        x, w = inputs\n",
        "        ys = tf.reshape(self.dense_1(w), (-1, 1, 1, self.x_channels))\n",
        "        yb = tf.reshape(self.dense_2(w), (-1, 1, 1, self.x_channels))\n",
        "        return ys * x + yb"
      ],
      "metadata": {
        "id": "LyOJtxDPPg6T"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Arch."
      ],
      "metadata": {
        "id": "xqevIm6GPpC7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def Mapping(num_stages, input_shape=512):\n",
        "    z = layers.Input(shape=(input_shape))\n",
        "    w = pixel_norm(z)\n",
        "    for i in range(8):\n",
        "        w = EqualizedDense(512, learning_rate_multiplier=0.01)(w)\n",
        "        w = layers.LeakyReLU(0.2)(w)\n",
        "    w = tf.tile(tf.expand_dims(w, 1), (1, num_stages, 1))\n",
        "    return keras.Model(z, w, name=\"mapping\")\n",
        "\n",
        "\n",
        "class Generator:\n",
        "    def __init__(self, start_res_log2, target_res_log2):\n",
        "        self.start_res_log2 = start_res_log2\n",
        "        self.target_res_log2 = target_res_log2\n",
        "        self.num_stages = target_res_log2 - start_res_log2 + 1\n",
        "        # list of generator blocks at increasing resolution\n",
        "        self.g_blocks = []\n",
        "        # list of layers to convert g_block activation to RGB\n",
        "        self.to_rgb = []\n",
        "        # list of noise input of different resolutions into g_blocks\n",
        "        self.noise_inputs = []\n",
        "        # filter size to use at each stage, keys are log2(resolution)\n",
        "        self.filter_nums = {\n",
        "            0: 512,\n",
        "            1: 512,\n",
        "            2: 512,  # 4x4\n",
        "            3: 512,  # 8x8\n",
        "            4: 512,  # 16x16\n",
        "            5: 512,  # 32x32\n",
        "            6: 256,  # 64x64\n",
        "            7: 128,  # 128x128\n",
        "            8: 64,  # 256x256\n",
        "            9: 32,  # 512x512\n",
        "            10: 16,\n",
        "        }  # 1024x1024\n",
        "\n",
        "        start_res = 2 ** start_res_log2\n",
        "        self.input_shape = (start_res, start_res, self.filter_nums[start_res_log2])\n",
        "        self.g_input = layers.Input(self.input_shape, name=\"generator_input\")\n",
        "\n",
        "        for i in range(start_res_log2, target_res_log2 + 1):\n",
        "            filter_num = self.filter_nums[i]\n",
        "            res = 2 ** i\n",
        "            self.noise_inputs.append(\n",
        "                layers.Input(shape=(res, res, 1), name=f\"noise_{res}x{res}\")\n",
        "            )\n",
        "            to_rgb = Sequential(\n",
        "                [\n",
        "                    layers.InputLayer(input_shape=(res, res, filter_num)),\n",
        "                    EqualizedConv(3, 1, gain=1),\n",
        "                ],\n",
        "                name=f\"to_rgb_{res}x{res}\",\n",
        "            )\n",
        "            self.to_rgb.append(to_rgb)\n",
        "            is_base = i == self.start_res_log2\n",
        "            if is_base:\n",
        "                input_shape = (res, res, self.filter_nums[i - 1])\n",
        "            else:\n",
        "                input_shape = (2 ** (i - 1), 2 ** (i - 1), self.filter_nums[i - 1])\n",
        "            g_block = self.build_block(\n",
        "                filter_num, res=res, input_shape=input_shape, is_base=is_base\n",
        "            )\n",
        "            self.g_blocks.append(g_block)\n",
        "\n",
        "    def build_block(self, filter_num, res, input_shape, is_base):\n",
        "        input_tensor = layers.Input(shape=input_shape, name=f\"g_{res}\")\n",
        "        noise = layers.Input(shape=(res, res, 1), name=f\"noise_{res}\")\n",
        "        w = layers.Input(shape=512)\n",
        "        x = input_tensor\n",
        "\n",
        "        if not is_base:\n",
        "            x = layers.UpSampling2D((2, 2))(x)\n",
        "            x = EqualizedConv(filter_num, 3)(x)\n",
        "\n",
        "        x = AddNoise()([x, noise])\n",
        "        x = layers.LeakyReLU(0.2)(x)\n",
        "        x = InstanceNormalization()(x)\n",
        "        x = AdaIN()([x, w])\n",
        "\n",
        "        x = EqualizedConv(filter_num, 3)(x)\n",
        "        x = AddNoise()([x, noise])\n",
        "        x = layers.LeakyReLU(0.2)(x)\n",
        "        x = InstanceNormalization()(x)\n",
        "        x = AdaIN()([x, w])\n",
        "        return keras.Model([input_tensor, w, noise], x, name=f\"genblock_{res}x{res}\")\n",
        "\n",
        "    def grow(self, res_log2):\n",
        "        res = 2 ** res_log2\n",
        "\n",
        "        num_stages = res_log2 - self.start_res_log2 + 1\n",
        "        w = layers.Input(shape=(self.num_stages, 512), name=\"w\")\n",
        "\n",
        "        alpha = layers.Input(shape=(1), name=\"g_alpha\")\n",
        "        x = self.g_blocks[0]([self.g_input, w[:, 0], self.noise_inputs[0]])\n",
        "\n",
        "        if num_stages == 1:\n",
        "            rgb = self.to_rgb[0](x)\n",
        "        else:\n",
        "            for i in range(1, num_stages - 1):\n",
        "\n",
        "                x = self.g_blocks[i]([x, w[:, i], self.noise_inputs[i]])\n",
        "\n",
        "            old_rgb = self.to_rgb[num_stages - 2](x)\n",
        "            old_rgb = layers.UpSampling2D((2, 2))(old_rgb)\n",
        "\n",
        "            i = num_stages - 1\n",
        "            x = self.g_blocks[i]([x, w[:, i], self.noise_inputs[i]])\n",
        "\n",
        "            new_rgb = self.to_rgb[i](x)\n",
        "\n",
        "            rgb = fade_in(alpha[0], new_rgb, old_rgb)\n",
        "\n",
        "        return keras.Model(\n",
        "            [self.g_input, w, self.noise_inputs, alpha],\n",
        "            rgb,\n",
        "            name=f\"generator_{res}_x_{res}\",\n",
        "        )\n",
        "\n",
        "\n",
        "class Discriminator:\n",
        "    def __init__(self, start_res_log2, target_res_log2):\n",
        "        self.start_res_log2 = start_res_log2\n",
        "        self.target_res_log2 = target_res_log2\n",
        "        self.num_stages = target_res_log2 - start_res_log2 + 1\n",
        "        # filter size to use at each stage, keys are log2(resolution)\n",
        "        self.filter_nums = {\n",
        "            0: 512,\n",
        "            1: 512,\n",
        "            2: 512,  # 4x4\n",
        "            3: 512,  # 8x8\n",
        "            4: 512,  # 16x16\n",
        "            5: 512,  # 32x32\n",
        "            6: 256,  # 64x64\n",
        "            7: 128,  # 128x128\n",
        "            8: 64,  # 256x256\n",
        "            9: 32,  # 512x512\n",
        "            10: 16,\n",
        "        }  # 1024x1024\n",
        "        # list of discriminator blocks at increasing resolution\n",
        "        self.d_blocks = []\n",
        "        # list of layers to convert RGB into activation for d_blocks inputs\n",
        "        self.from_rgb = []\n",
        "\n",
        "        for res_log2 in range(self.start_res_log2, self.target_res_log2 + 1):\n",
        "            res = 2 ** res_log2\n",
        "            filter_num = self.filter_nums[res_log2]\n",
        "            from_rgb = Sequential(\n",
        "                [\n",
        "                    layers.InputLayer(\n",
        "                        input_shape=(res, res, 3), name=f\"from_rgb_input_{res}\"\n",
        "                    ),\n",
        "                    EqualizedConv(filter_num, 1),\n",
        "                    layers.LeakyReLU(0.2),\n",
        "                ],\n",
        "                name=f\"from_rgb_{res}\",\n",
        "            )\n",
        "\n",
        "            self.from_rgb.append(from_rgb)\n",
        "\n",
        "            input_shape = (res, res, filter_num)\n",
        "            if len(self.d_blocks) == 0:\n",
        "                d_block = self.build_base(filter_num, res)\n",
        "            else:\n",
        "                d_block = self.build_block(\n",
        "                    filter_num, self.filter_nums[res_log2 - 1], res\n",
        "                )\n",
        "\n",
        "            self.d_blocks.append(d_block)\n",
        "\n",
        "    def build_base(self, filter_num, res):\n",
        "        input_tensor = layers.Input(shape=(res, res, filter_num), name=f\"d_{res}\")\n",
        "        x = minibatch_std(input_tensor)\n",
        "        x = EqualizedConv(filter_num, 3)(x)\n",
        "        x = layers.LeakyReLU(0.2)(x)\n",
        "        x = layers.Flatten()(x)\n",
        "        x = EqualizedDense(filter_num)(x)\n",
        "        x = layers.LeakyReLU(0.2)(x)\n",
        "        x = EqualizedDense(1)(x)\n",
        "        return keras.Model(input_tensor, x, name=f\"d_{res}\")\n",
        "\n",
        "    def build_block(self, filter_num_1, filter_num_2, res):\n",
        "        input_tensor = layers.Input(shape=(res, res, filter_num_1), name=f\"d_{res}\")\n",
        "        x = EqualizedConv(filter_num_1, 3)(input_tensor)\n",
        "        x = layers.LeakyReLU(0.2)(x)\n",
        "        x = EqualizedConv(filter_num_2)(x)\n",
        "        x = layers.LeakyReLU(0.2)(x)\n",
        "        x = layers.AveragePooling2D((2, 2))(x)\n",
        "        return keras.Model(input_tensor, x, name=f\"d_{res}\")\n",
        "\n",
        "    def grow(self, res_log2):\n",
        "        res = 2 ** res_log2\n",
        "        idx = res_log2 - self.start_res_log2\n",
        "        alpha = layers.Input(shape=(1), name=\"d_alpha\")\n",
        "        input_image = layers.Input(shape=(res, res, 3), name=\"input_image\")\n",
        "        x = self.from_rgb[idx](input_image)\n",
        "        x = self.d_blocks[idx](x)\n",
        "        if idx > 0:\n",
        "            idx -= 1\n",
        "            downsized_image = layers.AveragePooling2D((2, 2))(input_image)\n",
        "            y = self.from_rgb[idx](downsized_image)\n",
        "            x = fade_in(alpha[0], x, y)\n",
        "\n",
        "            for i in range(idx, -1, -1):\n",
        "                x = self.d_blocks[i](x)\n",
        "        return keras.Model([input_image, alpha], x, name=f\"discriminator_{res}_x_{res}\")"
      ],
      "metadata": {
        "id": "Qv-LbD4HPlZ8"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### StyleGAN class (main)"
      ],
      "metadata": {
        "id": "5a61h8DWPtSC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class StyleGAN(tf.keras.Model):\n",
        "    def __init__(self, z_dim=512, target_res=64, start_res=4):\n",
        "        super().__init__()\n",
        "        self.z_dim = z_dim\n",
        "\n",
        "        self.target_res_log2 = log2(target_res)\n",
        "        self.start_res_log2 = log2(start_res)\n",
        "        self.current_res_log2 = self.target_res_log2\n",
        "        self.num_stages = self.target_res_log2 - self.start_res_log2 + 1\n",
        "\n",
        "        self.alpha = tf.Variable(1.0, dtype=tf.float32, trainable=False, name=\"alpha\")\n",
        "\n",
        "        self.mapping = Mapping(num_stages=self.num_stages)\n",
        "        self.d_builder = Discriminator(self.start_res_log2, self.target_res_log2)\n",
        "        self.g_builder = Generator(self.start_res_log2, self.target_res_log2)\n",
        "        self.g_input_shape = self.g_builder.input_shape\n",
        "\n",
        "        self.phase = None\n",
        "        self.train_step_counter = tf.Variable(0, dtype=tf.int32, trainable=False)\n",
        "\n",
        "        self.loss_weights = {\"gradient_penalty\": 10, \"drift\": 0.001}\n",
        "\n",
        "    def grow_model(self, res):\n",
        "        tf.keras.backend.clear_session()\n",
        "        res_log2 = log2(res)\n",
        "        self.generator = self.g_builder.grow(res_log2)\n",
        "        self.discriminator = self.d_builder.grow(res_log2)\n",
        "        self.current_res_log2 = res_log2\n",
        "        print(f\"\\nModel resolution:{res}x{res}\")\n",
        "\n",
        "    def compile(\n",
        "        self, steps_per_epoch, phase, res, d_optimizer, g_optimizer, *args, **kwargs\n",
        "    ):\n",
        "        self.loss_weights = kwargs.pop(\"loss_weights\", self.loss_weights)\n",
        "        self.steps_per_epoch = steps_per_epoch\n",
        "        if res != 2 ** self.current_res_log2:\n",
        "            self.grow_model(res)\n",
        "            self.d_optimizer = d_optimizer\n",
        "            self.g_optimizer = g_optimizer\n",
        "\n",
        "        self.train_step_counter.assign(0)\n",
        "        self.phase = phase\n",
        "        self.d_loss_metric = keras.metrics.Mean(name=\"d_loss\")\n",
        "        self.g_loss_metric = keras.metrics.Mean(name=\"g_loss\")\n",
        "        super().compile(*args, **kwargs)\n",
        "\n",
        "    @property\n",
        "    def metrics(self):\n",
        "        return [self.d_loss_metric, self.g_loss_metric]\n",
        "\n",
        "    def generate_noise(self, batch_size):\n",
        "        noise = [\n",
        "            tf.random.normal((batch_size, 2 ** res, 2 ** res, 1))\n",
        "            for res in range(self.start_res_log2, self.target_res_log2 + 1)\n",
        "        ]\n",
        "        return noise\n",
        "\n",
        "    def gradient_loss(self, grad):\n",
        "        loss = tf.square(grad)\n",
        "        loss = tf.reduce_sum(loss, axis=tf.range(1, tf.size(tf.shape(loss))))\n",
        "        loss = tf.sqrt(loss)\n",
        "        loss = tf.reduce_mean(tf.square(loss - 1))\n",
        "        return loss\n",
        "\n",
        "    def train_step(self, real_images):\n",
        "\n",
        "        self.train_step_counter.assign_add(1)\n",
        "\n",
        "        if self.phase == \"TRANSITION\":\n",
        "            self.alpha.assign(\n",
        "                tf.cast(self.train_step_counter / self.steps_per_epoch, tf.float32)\n",
        "            )\n",
        "        elif self.phase == \"STABLE\":\n",
        "            self.alpha.assign(1.0)\n",
        "        else:\n",
        "            raise NotImplementedError\n",
        "        alpha = tf.expand_dims(self.alpha, 0)\n",
        "        batch_size = tf.shape(real_images)[0]\n",
        "        real_labels = tf.ones(batch_size)\n",
        "        fake_labels = -tf.ones(batch_size)\n",
        "\n",
        "        z = tf.random.normal((batch_size, self.z_dim))\n",
        "        const_input = tf.ones(tuple([batch_size] + list(self.g_input_shape)))\n",
        "        noise = self.generate_noise(batch_size)\n",
        "\n",
        "        # generator\n",
        "        with tf.GradientTape() as g_tape:\n",
        "            w = self.mapping(z)\n",
        "            fake_images = self.generator([const_input, w, noise, alpha])\n",
        "            pred_fake = self.discriminator([fake_images, alpha])\n",
        "            g_loss = wasserstein_loss(real_labels, pred_fake)\n",
        "\n",
        "            trainable_weights = (\n",
        "                self.mapping.trainable_weights + self.generator.trainable_weights\n",
        "            )\n",
        "            gradients = g_tape.gradient(g_loss, trainable_weights)\n",
        "            self.g_optimizer.apply_gradients(zip(gradients, trainable_weights))\n",
        "\n",
        "        # discriminator\n",
        "        with tf.GradientTape() as gradient_tape, tf.GradientTape() as total_tape:\n",
        "            # forward pass\n",
        "            pred_fake = self.discriminator([fake_images, alpha])\n",
        "            pred_real = self.discriminator([real_images, alpha])\n",
        "\n",
        "            epsilon = tf.random.uniform((batch_size, 1, 1, 1))\n",
        "            interpolates = epsilon * real_images + (1 - epsilon) * fake_images\n",
        "            gradient_tape.watch(interpolates)\n",
        "            pred_fake_grad = self.discriminator([interpolates, alpha])\n",
        "\n",
        "            # calculate losses\n",
        "            loss_fake = wasserstein_loss(fake_labels, pred_fake)\n",
        "            loss_real = wasserstein_loss(real_labels, pred_real)\n",
        "            loss_fake_grad = wasserstein_loss(fake_labels, pred_fake_grad)\n",
        "\n",
        "            # gradient penalty\n",
        "            gradients_fake = gradient_tape.gradient(loss_fake_grad, [interpolates])\n",
        "            gradient_penalty = self.loss_weights[\n",
        "                \"gradient_penalty\"\n",
        "            ] * self.gradient_loss(gradients_fake)\n",
        "\n",
        "            # drift loss\n",
        "            all_pred = tf.concat([pred_fake, pred_real], axis=0)\n",
        "            drift_loss = self.loss_weights[\"drift\"] * tf.reduce_mean(all_pred ** 2)\n",
        "\n",
        "            d_loss = loss_fake + loss_real + gradient_penalty + drift_loss\n",
        "\n",
        "            gradients = total_tape.gradient(\n",
        "                d_loss, self.discriminator.trainable_weights\n",
        "            )\n",
        "            self.d_optimizer.apply_gradients(\n",
        "                zip(gradients, self.discriminator.trainable_weights)\n",
        "            )\n",
        "\n",
        "        # Update metrics\n",
        "        self.d_loss_metric.update_state(d_loss)\n",
        "        self.g_loss_metric.update_state(g_loss)\n",
        "        return {\n",
        "            \"d_loss\": self.d_loss_metric.result(),\n",
        "            \"g_loss\": self.g_loss_metric.result(),\n",
        "        }\n",
        "\n",
        "    def call(self, inputs: dict()):\n",
        "        style_code = inputs.get(\"style_code\", None)\n",
        "        z = inputs.get(\"z\", None)\n",
        "        noise = inputs.get(\"noise\", None)\n",
        "        batch_size = inputs.get(\"batch_size\", 1)\n",
        "        alpha = inputs.get(\"alpha\", 1.0)\n",
        "        alpha = tf.expand_dims(alpha, 0)\n",
        "        if style_code is None:\n",
        "            if z is None:\n",
        "                z = tf.random.normal((batch_size, self.z_dim))\n",
        "            style_code = self.mapping(z)\n",
        "\n",
        "        if noise is None:\n",
        "            noise = self.generate_noise(batch_size)\n",
        "\n",
        "        # self.alpha.assign(alpha)\n",
        "\n",
        "        const_input = tf.ones(tuple([batch_size] + list(self.g_input_shape)))\n",
        "        images = self.generator([const_input, style_code, noise, alpha])\n",
        "        images = np.clip((images * 0.5 + 0.5) * 255, 0, 255).astype(np.uint8)\n",
        "\n",
        "        return images"
      ],
      "metadata": {
        "id": "7U6v9I9yPqNj"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Start training + param setting"
      ],
      "metadata": {
        "id": "8rhxT2wyPxJE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "START_RES = 4\n",
        "TARGET_RES = 64\n",
        "\n",
        "style_gan = StyleGAN(start_res=START_RES, target_res=TARGET_RES)"
      ],
      "metadata": {
        "id": "nazf02IoPvNa"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def train(\n",
        "    start_res=START_RES,\n",
        "    target_res=TARGET_RES,\n",
        "    steps_per_epoch=5000,\n",
        "    display_images=True,\n",
        "):\n",
        "    opt_cfg = {\"learning_rate\": 0.0005, \"beta_1\": 0.5, \"beta_2\": 0.999, \"epsilon\": 1e-8}\n",
        "\n",
        "    val_batch_size = 16\n",
        "    val_z = tf.random.normal((val_batch_size, style_gan.z_dim))\n",
        "    val_noise = style_gan.generate_noise(val_batch_size)\n",
        "\n",
        "    start_res_log2 = int(np.log2(start_res))\n",
        "    target_res_log2 = int(np.log2(target_res))\n",
        "\n",
        "    for res_log2 in range(start_res_log2, target_res_log2 + 1):\n",
        "        res = 2 ** res_log2\n",
        "        for phase in [\"TRANSITION\", \"STABLE\"]:\n",
        "            if res == start_res and phase == \"TRANSITION\":\n",
        "                continue\n",
        "\n",
        "            train_dl = create_dataloader(res)\n",
        "\n",
        "            steps = int(train_step_ratio[res_log2] * steps_per_epoch)\n",
        "\n",
        "            style_gan.compile(\n",
        "                d_optimizer=tf.keras.optimizers.legacy.Adam(**opt_cfg),\n",
        "                g_optimizer=tf.keras.optimizers.legacy.Adam(**opt_cfg),\n",
        "                loss_weights={\"gradient_penalty\": 10, \"drift\": 0.001},\n",
        "                steps_per_epoch=steps,\n",
        "                res=res,\n",
        "                phase=phase,\n",
        "                run_eagerly=False,\n",
        "            )\n",
        "\n",
        "            prefix = f\"res_{res}x{res}_{style_gan.phase}\"\n",
        "\n",
        "            ckpt_cb = keras.callbacks.ModelCheckpoint(\n",
        "                f\"checkpoints/stylegan_{res}x{res}.ckpt\",\n",
        "                save_weights_only=True,\n",
        "                verbose=0,\n",
        "            )\n",
        "            print(phase)\n",
        "            style_gan.fit(\n",
        "                train_dl, epochs=20, steps_per_epoch=steps, callbacks=[ckpt_cb]\n",
        "            )\n",
        "\n",
        "            if display_images:\n",
        "                images = style_gan({\"z\": val_z, \"noise\": val_noise, \"alpha\": 1.0})\n",
        "                plot_images(images, res_log2)"
      ],
      "metadata": {
        "id": "0eDV7Xc2P0CL"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train(start_res=4, target_res=64, steps_per_epoch=1, display_images=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UVDBves8P2jB",
        "outputId": "e51e6f37-c69b-4168-cb34-f5c01712b6f1"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Model resolution:4x4\n",
            "STABLE\n",
            "Epoch 1/20\n",
            "1/1 [==============================] - 10s 10s/step - d_loss: 4.5714 - g_loss: -0.1219\n",
            "Epoch 2/20\n",
            "1/1 [==============================] - 1s 572ms/step - d_loss: 3.5145 - g_loss: 0.8955\n",
            "Epoch 3/20\n",
            "1/1 [==============================] - 1s 540ms/step - d_loss: 4.0337 - g_loss: 0.6966\n",
            "Epoch 4/20\n",
            "1/1 [==============================] - 1s 566ms/step - d_loss: 3.5198 - g_loss: 0.9191\n",
            "Epoch 5/20\n",
            "1/1 [==============================] - 1s 576ms/step - d_loss: 3.1595 - g_loss: 1.5328\n",
            "Epoch 6/20\n",
            "1/1 [==============================] - 1s 570ms/step - d_loss: 2.3549 - g_loss: 2.1942\n",
            "Epoch 7/20\n",
            "1/1 [==============================] - 1s 571ms/step - d_loss: 1.2904 - g_loss: 3.0873\n",
            "Epoch 8/20\n",
            "1/1 [==============================] - 1s 575ms/step - d_loss: 1.8421 - g_loss: 2.3891\n",
            "Epoch 9/20\n",
            "1/1 [==============================] - 1s 571ms/step - d_loss: 0.4227 - g_loss: 3.3769\n",
            "Epoch 10/20\n",
            "1/1 [==============================] - 1s 996ms/step - d_loss: 0.7080 - g_loss: 3.3149\n",
            "Epoch 11/20\n",
            "1/1 [==============================] - 0s 471ms/step - d_loss: 0.0845 - g_loss: 3.5028\n",
            "Epoch 12/20\n",
            "1/1 [==============================] - 1s 559ms/step - d_loss: 0.0650 - g_loss: 3.2657\n",
            "Epoch 13/20\n",
            "1/1 [==============================] - 1s 575ms/step - d_loss: -0.4171 - g_loss: 3.6038\n",
            "Epoch 14/20\n",
            "1/1 [==============================] - 1s 633ms/step - d_loss: -1.0765 - g_loss: 4.3764\n",
            "Epoch 15/20\n",
            "1/1 [==============================] - 1s 748ms/step - d_loss: -1.5011 - g_loss: 3.9870\n",
            "Epoch 16/20\n",
            "1/1 [==============================] - 1s 654ms/step - d_loss: -1.8054 - g_loss: 5.1702\n",
            "Epoch 17/20\n",
            "1/1 [==============================] - 2s 2s/step - d_loss: -1.6934 - g_loss: 4.8538\n",
            "Epoch 18/20\n",
            "1/1 [==============================] - 0s 482ms/step - d_loss: -1.6007 - g_loss: 5.2092\n",
            "Epoch 19/20\n",
            "1/1 [==============================] - 0s 494ms/step - d_loss: -3.6553 - g_loss: 7.1778\n",
            "Epoch 20/20\n",
            "1/1 [==============================] - 0s 486ms/step - d_loss: -4.9145 - g_loss: 8.7199\n",
            "\n",
            "Model resolution:8x8\n",
            "TRANSITION\n",
            "Epoch 1/20\n",
            "1/1 [==============================] - 7s 7s/step - d_loss: -0.2969 - g_loss: 9.7090\n",
            "Epoch 2/20\n",
            "1/1 [==============================] - 1s 984ms/step - d_loss: -37.5364 - g_loss: 46.1019\n",
            "Epoch 3/20\n",
            "1/1 [==============================] - 1s 1s/step - d_loss: -103.4102 - g_loss: 121.8432\n",
            "Epoch 4/20\n",
            "1/1 [==============================] - 3s 3s/step - d_loss: -194.5079 - g_loss: 242.5439\n",
            "Epoch 5/20\n",
            "1/1 [==============================] - 1s 1s/step - d_loss: -308.5071 - g_loss: 451.4865\n",
            "Epoch 6/20\n",
            "1/1 [==============================] - 1s 1s/step - d_loss: -367.0375 - g_loss: 638.7971\n",
            "Epoch 7/20\n",
            "1/1 [==============================] - 1s 1s/step - d_loss: -372.5086 - g_loss: 881.7867\n",
            "Epoch 8/20\n",
            "1/1 [==============================] - 2s 2s/step - d_loss: -346.4225 - g_loss: 1048.1125\n",
            "Epoch 9/20\n",
            "1/1 [==============================] - 1s 897ms/step - d_loss: -331.7408 - g_loss: 1166.1129\n",
            "Epoch 10/20\n",
            "1/1 [==============================] - 1s 1s/step - d_loss: -346.9072 - g_loss: 1034.7173\n",
            "Epoch 11/20\n",
            "1/1 [==============================] - 1s 1s/step - d_loss: -335.6454 - g_loss: 810.0001\n",
            "Epoch 12/20\n",
            "1/1 [==============================] - 2s 2s/step - d_loss: -306.8842 - g_loss: 1251.1974\n",
            "Epoch 13/20\n",
            "1/1 [==============================] - 1s 1s/step - d_loss: -367.1773 - g_loss: 1047.4777\n",
            "Epoch 14/20\n",
            "1/1 [==============================] - 1s 1s/step - d_loss: -275.3016 - g_loss: 839.9621\n",
            "Epoch 15/20\n",
            "1/1 [==============================] - 1s 1s/step - d_loss: -378.8713 - g_loss: 986.5410\n",
            "Epoch 16/20\n",
            "1/1 [==============================] - 2s 2s/step - d_loss: -228.3990 - g_loss: 1531.2507\n",
            "Epoch 17/20\n",
            "1/1 [==============================] - 1s 987ms/step - d_loss: 505.1223 - g_loss: -323.7995\n",
            "Epoch 18/20\n",
            "1/1 [==============================] - 1s 1s/step - d_loss: -65.8264 - g_loss: 1668.3616\n",
            "Epoch 19/20\n",
            "1/1 [==============================] - 1s 1s/step - d_loss: -244.4349 - g_loss: 1212.9806\n",
            "Epoch 20/20\n",
            "1/1 [==============================] - 2s 2s/step - d_loss: -145.6993 - g_loss: 470.0986\n",
            "STABLE\n",
            "Epoch 1/20\n",
            "1/1 [==============================] - 5s 5s/step - d_loss: 0.4432 - g_loss: 8.1681\n",
            "Epoch 2/20\n",
            "1/1 [==============================] - 1s 946ms/step - d_loss: 0.2430 - g_loss: 8.4343\n",
            "Epoch 3/20\n",
            "1/1 [==============================] - 1s 1s/step - d_loss: -0.7101 - g_loss: 9.0770\n",
            "Epoch 4/20\n",
            "1/1 [==============================] - 1s 1s/step - d_loss: -0.0836 - g_loss: 8.9553\n",
            "Epoch 5/20\n",
            "1/1 [==============================] - 1s 1s/step - d_loss: -0.0989 - g_loss: 8.4071\n",
            "Epoch 6/20\n",
            "1/1 [==============================] - 1s 1s/step - d_loss: -0.5476 - g_loss: 9.1354\n",
            "Epoch 7/20\n",
            "1/1 [==============================] - 5s 5s/step - d_loss: -0.2102 - g_loss: 8.8627\n",
            "Epoch 8/20\n",
            "1/1 [==============================] - 9s 9s/step - d_loss: -0.0777 - g_loss: 8.8117\n",
            "Epoch 9/20\n",
            "1/1 [==============================] - 1s 1s/step - d_loss: -0.1022 - g_loss: 8.6237\n",
            "Epoch 10/20\n",
            "1/1 [==============================] - 3s 3s/step - d_loss: 0.8798 - g_loss: 8.0636\n",
            "Epoch 11/20\n",
            "1/1 [==============================] - 1s 1s/step - d_loss: 0.3028 - g_loss: 8.2786\n",
            "Epoch 12/20\n",
            "1/1 [==============================] - 4s 4s/step - d_loss: -0.9880 - g_loss: 9.5666\n",
            "Epoch 13/20\n",
            "1/1 [==============================] - 1s 1s/step - d_loss: -0.4028 - g_loss: 9.4713\n",
            "Epoch 14/20\n",
            "1/1 [==============================] - 5s 5s/step - d_loss: -0.9781 - g_loss: 9.4502\n",
            "Epoch 15/20\n",
            "1/1 [==============================] - 1s 1s/step - d_loss: -1.4572 - g_loss: 9.9807\n",
            "Epoch 16/20\n",
            "1/1 [==============================] - 1s 1s/step - d_loss: -0.6925 - g_loss: 9.5179\n",
            "Epoch 17/20\n",
            "1/1 [==============================] - 1s 1s/step - d_loss: -1.0448 - g_loss: 9.6140\n",
            "Epoch 18/20\n",
            "1/1 [==============================] - 2s 2s/step - d_loss: -0.3419 - g_loss: 8.7878\n",
            "Epoch 19/20\n",
            "1/1 [==============================] - 1s 979ms/step - d_loss: -1.0069 - g_loss: 9.5241\n",
            "Epoch 20/20\n",
            "1/1 [==============================] - 1s 1s/step - d_loss: 0.6013 - g_loss: 8.2137\n",
            "\n",
            "Model resolution:16x16\n",
            "TRANSITION\n",
            "Epoch 1/20\n",
            "1/1 [==============================] - 13s 13s/step - d_loss: 1.1544 - g_loss: 10.3347\n",
            "Epoch 2/20\n",
            "1/1 [==============================] - 2s 2s/step - d_loss: -27.6207 - g_loss: 42.7793\n",
            "Epoch 3/20\n",
            "1/1 [==============================] - 7s 7s/step - d_loss: -99.7832 - g_loss: 129.9920\n",
            "Epoch 4/20\n",
            "1/1 [==============================] - 4s 4s/step - d_loss: -214.7324 - g_loss: 285.3607\n",
            "Epoch 5/20\n",
            "1/1 [==============================] - 4s 4s/step - d_loss: -356.9625 - g_loss: 564.8914\n",
            "Epoch 6/20\n",
            "1/1 [==============================] - 5s 5s/step - d_loss: -422.9280 - g_loss: 1001.9391\n",
            "Epoch 7/20\n",
            "1/1 [==============================] - 2s 2s/step - d_loss: -261.4647 - g_loss: 1454.1473\n",
            "Epoch 8/20\n",
            "1/1 [==============================] - 3s 3s/step - d_loss: -234.7439 - g_loss: 1543.6838\n",
            "Epoch 9/20\n",
            "1/1 [==============================] - 10s 10s/step - d_loss: -285.2235 - g_loss: 1514.5815\n",
            "Epoch 10/20\n",
            "1/1 [==============================] - 2s 2s/step - d_loss: -359.8259 - g_loss: 1363.4312\n",
            "Epoch 11/20\n",
            "1/1 [==============================] - 3s 3s/step - d_loss: -430.4808 - g_loss: 1174.6792\n",
            "Epoch 12/20\n",
            "1/1 [==============================] - 5s 5s/step - d_loss: -425.9256 - g_loss: 880.2874\n",
            "Epoch 13/20\n",
            "1/1 [==============================] - 4s 4s/step - d_loss: -381.4730 - g_loss: 603.3373\n",
            "Epoch 14/20\n",
            "1/1 [==============================] - 2s 2s/step - d_loss: -268.9196 - g_loss: 1537.3433\n",
            "Epoch 15/20\n",
            "1/1 [==============================] - 3s 3s/step - d_loss: -423.2513 - g_loss: 766.5931\n",
            "Epoch 16/20\n",
            "1/1 [==============================] - 2s 2s/step - d_loss: -432.4329 - g_loss: 903.4958\n",
            "Epoch 17/20\n",
            "1/1 [==============================] - 4s 4s/step - d_loss: -352.3312 - g_loss: 1284.4426\n",
            "Epoch 18/20\n",
            "1/1 [==============================] - 10s 10s/step - d_loss: -183.7683 - g_loss: 333.3920\n",
            "Epoch 19/20\n",
            "1/1 [==============================] - 2s 2s/step - d_loss: 96.5642 - g_loss: 1991.5734\n",
            "Epoch 20/20\n",
            "1/1 [==============================] - 6s 6s/step - d_loss: -228.2043 - g_loss: 459.0574\n",
            "STABLE\n",
            "Epoch 1/20\n",
            "1/1 [==============================] - 7s 7s/step - d_loss: 5.9024 - g_loss: 2.5020\n",
            "Epoch 2/20\n",
            "1/1 [==============================] - 9s 9s/step - d_loss: 5.7772 - g_loss: 2.5850\n",
            "Epoch 3/20\n",
            "1/1 [==============================] - 2s 2s/step - d_loss: 5.9566 - g_loss: 2.6404\n",
            "Epoch 4/20\n",
            "1/1 [==============================] - 3s 3s/step - d_loss: 5.9462 - g_loss: 2.4994\n",
            "Epoch 5/20\n",
            "1/1 [==============================] - 10s 10s/step - d_loss: 5.9174 - g_loss: 2.7942\n",
            "Epoch 6/20\n",
            "1/1 [==============================] - 2s 2s/step - d_loss: 5.5818 - g_loss: 2.9331\n",
            "Epoch 7/20\n",
            "1/1 [==============================] - 3s 3s/step - d_loss: 5.3993 - g_loss: 2.9975\n",
            "Epoch 8/20\n",
            "1/1 [==============================] - 5s 5s/step - d_loss: 5.2031 - g_loss: 3.3896\n",
            "Epoch 9/20\n",
            "1/1 [==============================] - 10s 10s/step - d_loss: 5.2444 - g_loss: 3.2802\n",
            "Epoch 10/20\n",
            "1/1 [==============================] - 2s 2s/step - d_loss: 5.1164 - g_loss: 3.3720\n",
            "Epoch 11/20\n",
            "1/1 [==============================] - 3s 3s/step - d_loss: 4.5476 - g_loss: 4.0025\n",
            "Epoch 12/20\n",
            "1/1 [==============================] - 3s 3s/step - d_loss: 4.4476 - g_loss: 4.0198\n",
            "Epoch 13/20\n",
            "1/1 [==============================] - 5s 5s/step - d_loss: 4.2781 - g_loss: 4.2558\n",
            "Epoch 14/20\n",
            "1/1 [==============================] - 2s 2s/step - d_loss: 4.7207 - g_loss: 3.8118\n",
            "Epoch 15/20\n",
            "1/1 [==============================] - 4s 4s/step - d_loss: 5.2439 - g_loss: 3.2473\n",
            "Epoch 16/20\n",
            "1/1 [==============================] - 5s 5s/step - d_loss: 4.6195 - g_loss: 3.9007\n",
            "Epoch 17/20\n",
            "1/1 [==============================] - 6s 6s/step - d_loss: 4.4686 - g_loss: 4.0195\n",
            "Epoch 18/20\n",
            "1/1 [==============================] - 4s 4s/step - d_loss: 4.5733 - g_loss: 3.9240\n",
            "Epoch 19/20\n",
            "1/1 [==============================] - 6s 6s/step - d_loss: 4.5292 - g_loss: 4.0042\n",
            "Epoch 20/20\n",
            "1/1 [==============================] - 4s 4s/step - d_loss: 4.5161 - g_loss: 4.1102\n",
            "\n",
            "Model resolution:32x32\n",
            "TRANSITION\n",
            "Epoch 1/20\n",
            "1/1 [==============================] - 15s 15s/step - d_loss: 2.4402 - g_loss: 7.4859\n",
            "Epoch 2/20\n",
            "1/1 [==============================] - 3s 3s/step - d_loss: -60.5081 - g_loss: 76.0491\n",
            "Epoch 3/20\n",
            "1/1 [==============================] - 7s 7s/step - d_loss: -233.1400 - g_loss: 296.0588\n",
            "Epoch 4/20\n",
            "1/1 [==============================] - 3s 3s/step - d_loss: -443.7234 - g_loss: 796.0706\n",
            "Epoch 5/20\n",
            "1/1 [==============================] - 4s 4s/step - d_loss: -305.3071 - g_loss: 1508.1060\n",
            "Epoch 6/20\n",
            "1/1 [==============================] - 9s 9s/step - d_loss: -242.1879 - g_loss: 1533.6998\n",
            "Epoch 7/20\n",
            "1/1 [==============================] - 10s 10s/step - d_loss: -396.6201 - g_loss: 1199.2339\n",
            "Epoch 8/20\n",
            "1/1 [==============================] - 3s 3s/step - d_loss: -439.7629 - g_loss: 864.9562\n",
            "Epoch 9/20\n",
            "1/1 [==============================] - 3s 3s/step - d_loss: -418.9781 - g_loss: 725.7598\n",
            "Epoch 10/20\n",
            "1/1 [==============================] - 6s 6s/step - d_loss: -420.7947 - g_loss: 1244.1949\n",
            "Epoch 11/20\n",
            "1/1 [==============================] - 8s 8s/step - d_loss: -472.6316 - g_loss: 930.0957\n",
            "Epoch 12/20\n",
            "1/1 [==============================] - 6s 6s/step - d_loss: -447.8954 - g_loss: 857.5572\n",
            "Epoch 13/20\n",
            "1/1 [==============================] - 9s 9s/step - d_loss: -467.4973 - g_loss: 889.7856\n",
            "Epoch 14/20\n",
            "1/1 [==============================] - 3s 3s/step - d_loss: -467.2513 - g_loss: 1103.6936\n",
            "Epoch 15/20\n",
            "1/1 [==============================] - 4s 4s/step - d_loss: -465.5010 - g_loss: 931.4589\n",
            "Epoch 16/20\n",
            "1/1 [==============================] - 4s 4s/step - d_loss: -446.0533 - g_loss: 1018.0013\n",
            "Epoch 17/20\n",
            "1/1 [==============================] - 9s 9s/step - d_loss: -430.1167 - g_loss: 703.8208\n",
            "Epoch 18/20\n",
            "1/1 [==============================] - 3s 3s/step - d_loss: -359.1953 - g_loss: 1215.0186\n",
            "Epoch 19/20\n",
            "1/1 [==============================] - 4s 4s/step - d_loss: -411.7241 - g_loss: 962.4287\n",
            "Epoch 20/20\n",
            "1/1 [==============================] - 5s 5s/step - d_loss: -357.6044 - g_loss: 525.5638\n",
            "STABLE\n",
            "Epoch 1/20\n",
            "1/1 [==============================] - 13s 13s/step - d_loss: 5.8543 - g_loss: 2.7096\n",
            "Epoch 2/20\n",
            "1/1 [==============================] - 5s 5s/step - d_loss: 5.3825 - g_loss: 3.6555\n",
            "Epoch 3/20\n",
            "1/1 [==============================] - 5s 5s/step - d_loss: 5.4512 - g_loss: 3.8846\n",
            "Epoch 4/20\n",
            "1/1 [==============================] - 4s 4s/step - d_loss: 5.5933 - g_loss: 4.0183\n",
            "Epoch 5/20\n",
            "1/1 [==============================] - 3s 3s/step - d_loss: 5.6827 - g_loss: 3.8959\n",
            "Epoch 6/20\n",
            "1/1 [==============================] - 4s 4s/step - d_loss: 5.4834 - g_loss: 4.1559\n",
            "Epoch 7/20\n",
            "1/1 [==============================] - 4s 4s/step - d_loss: 4.5893 - g_loss: 5.0304\n",
            "Epoch 8/20\n",
            "1/1 [==============================] - 5s 5s/step - d_loss: 5.1439 - g_loss: 4.3187\n",
            "Epoch 9/20\n",
            "1/1 [==============================] - 10s 10s/step - d_loss: 4.8120 - g_loss: 4.8366\n",
            "Epoch 10/20\n",
            "1/1 [==============================] - 5s 5s/step - d_loss: 5.1465 - g_loss: 4.6120\n",
            "Epoch 11/20\n",
            "1/1 [==============================] - 3s 3s/step - d_loss: 5.2725 - g_loss: 4.4317\n",
            "Epoch 12/20\n",
            "1/1 [==============================] - 7s 7s/step - d_loss: 5.0315 - g_loss: 4.5883\n",
            "Epoch 13/20\n",
            "1/1 [==============================] - 3s 3s/step - d_loss: 4.3917 - g_loss: 5.1125\n",
            "Epoch 14/20\n",
            "1/1 [==============================] - 7s 7s/step - d_loss: 4.6273 - g_loss: 5.1463\n",
            "Epoch 15/20\n",
            "1/1 [==============================] - 3s 3s/step - d_loss: 4.9829 - g_loss: 4.8441\n",
            "Epoch 16/20\n",
            "1/1 [==============================] - 5s 5s/step - d_loss: 3.5907 - g_loss: 6.0238\n",
            "Epoch 17/20\n",
            "1/1 [==============================] - 6s 6s/step - d_loss: 4.7765 - g_loss: 4.9498\n",
            "Epoch 18/20\n",
            "1/1 [==============================] - 5s 5s/step - d_loss: 4.0522 - g_loss: 5.6281\n",
            "Epoch 19/20\n",
            "1/1 [==============================] - 4s 4s/step - d_loss: 5.0044 - g_loss: 4.7036\n",
            "Epoch 20/20\n",
            "1/1 [==============================] - 8s 8s/step - d_loss: 3.9377 - g_loss: 5.8011\n",
            "\n",
            "Model resolution:64x64\n",
            "TRANSITION\n",
            "Epoch 1/20\n",
            "1/1 [==============================] - 26s 26s/step - d_loss: 4.4877 - g_loss: 7.0103\n",
            "Epoch 2/20\n",
            "1/1 [==============================] - 9s 9s/step - d_loss: -22.0548 - g_loss: 39.2851\n",
            "Epoch 3/20\n",
            "1/1 [==============================] - 3s 3s/step - d_loss: -111.4967 - g_loss: 146.9548\n",
            "Epoch 4/20\n",
            "1/1 [==============================] - 8s 8s/step - d_loss: -239.2386 - g_loss: 329.6118\n",
            "Epoch 5/20\n",
            "1/1 [==============================] - 10s 10s/step - d_loss: -416.5452 - g_loss: 859.1739\n",
            "Epoch 6/20\n",
            "1/1 [==============================] - 5s 5s/step - d_loss: -334.4423 - g_loss: 1340.7241\n",
            "Epoch 7/20\n",
            "1/1 [==============================] - 5s 5s/step - d_loss: -256.9900 - g_loss: 1381.3396\n",
            "Epoch 8/20\n",
            "1/1 [==============================] - 6s 6s/step - d_loss: -409.6691 - g_loss: 1086.8052\n",
            "Epoch 9/20\n",
            "1/1 [==============================] - 9s 9s/step - d_loss: -416.9295 - g_loss: 862.1159\n",
            "Epoch 10/20\n",
            "1/1 [==============================] - 4s 4s/step - d_loss: -439.5682 - g_loss: 947.1516\n",
            "Epoch 11/20\n",
            "1/1 [==============================] - 11s 11s/step - d_loss: -438.9462 - g_loss: 1091.1908\n",
            "Epoch 12/20\n",
            "1/1 [==============================] - 4s 4s/step - d_loss: -454.7466 - g_loss: 935.2051\n",
            "Epoch 13/20\n",
            "1/1 [==============================] - 6s 6s/step - d_loss: -446.0487 - g_loss: 876.7177\n",
            "Epoch 14/20\n",
            "1/1 [==============================] - 3s 3s/step - d_loss: -310.4717 - g_loss: 1472.4618\n",
            "Epoch 15/20\n",
            "1/1 [==============================] - 9s 9s/step - d_loss: -188.6602 - g_loss: 237.9344\n",
            "Epoch 16/20\n",
            "1/1 [==============================] - 9s 9s/step - d_loss: -427.2026 - g_loss: 1083.0300\n",
            "Epoch 17/20\n",
            "1/1 [==============================] - 4s 4s/step - d_loss: -445.3514 - g_loss: 1079.9133\n",
            "Epoch 18/20\n",
            "1/1 [==============================] - 8s 8s/step - d_loss: -459.7923 - g_loss: 1097.0789\n",
            "Epoch 19/20\n",
            "1/1 [==============================] - 3s 3s/step - d_loss: -435.9743 - g_loss: 670.6885\n",
            "Epoch 20/20\n",
            "1/1 [==============================] - 3s 3s/step - d_loss: -355.7184 - g_loss: 1443.1366\n",
            "STABLE\n",
            "Epoch 1/20\n",
            "1/1 [==============================] - 11s 11s/step - d_loss: 6.7190 - g_loss: 1.6702\n",
            "Epoch 2/20\n",
            "1/1 [==============================] - 5s 5s/step - d_loss: 7.8821 - g_loss: 0.3669\n",
            "Epoch 3/20\n",
            "1/1 [==============================] - 5s 5s/step - d_loss: 7.5500 - g_loss: 0.4554\n",
            "Epoch 4/20\n",
            "1/1 [==============================] - 9s 9s/step - d_loss: 8.1977 - g_loss: -0.1332\n",
            "Epoch 5/20\n",
            "1/1 [==============================] - 4s 4s/step - d_loss: 7.4042 - g_loss: 0.3909\n",
            "Epoch 6/20\n",
            "1/1 [==============================] - 3s 3s/step - d_loss: 6.8958 - g_loss: 0.9453\n",
            "Epoch 7/20\n",
            "1/1 [==============================] - 8s 8s/step - d_loss: 7.2480 - g_loss: 0.5484\n",
            "Epoch 8/20\n",
            "1/1 [==============================] - 4s 4s/step - d_loss: 7.4499 - g_loss: 0.4251\n",
            "Epoch 9/20\n",
            "1/1 [==============================] - 9s 9s/step - d_loss: 6.9704 - g_loss: 0.8115\n",
            "Epoch 10/20\n",
            "1/1 [==============================] - 5s 5s/step - d_loss: 6.4117 - g_loss: 1.3183\n",
            "Epoch 11/20\n",
            "1/1 [==============================] - 7s 7s/step - d_loss: 7.0134 - g_loss: 0.9461\n",
            "Epoch 12/20\n",
            "1/1 [==============================] - 3s 3s/step - d_loss: 6.9021 - g_loss: 1.0348\n",
            "Epoch 13/20\n",
            "1/1 [==============================] - 4s 4s/step - d_loss: 6.5833 - g_loss: 1.3596\n",
            "Epoch 14/20\n",
            "1/1 [==============================] - 5s 5s/step - d_loss: 6.5230 - g_loss: 1.5208\n",
            "Epoch 15/20\n",
            "1/1 [==============================] - 5s 5s/step - d_loss: 6.4484 - g_loss: 1.5320\n",
            "Epoch 16/20\n",
            "1/1 [==============================] - 9s 9s/step - d_loss: 6.7202 - g_loss: 1.3974\n",
            "Epoch 17/20\n",
            "1/1 [==============================] - 5s 5s/step - d_loss: 6.4177 - g_loss: 1.4828\n",
            "Epoch 18/20\n",
            "1/1 [==============================] - 6s 6s/step - d_loss: 6.3006 - g_loss: 1.5878\n",
            "Epoch 19/20\n",
            "1/1 [==============================] - 4s 4s/step - d_loss: 6.2281 - g_loss: 1.7821\n",
            "Epoch 20/20\n",
            "1/1 [==============================] - 4s 4s/step - d_loss: 6.4980 - g_loss: 1.6516\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np.sum([np.prod(v.get_shape().as_list()) for v in style_gan.trainable_variables])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UKcP5b9WQE47",
        "outputId": "e697c311-d129-45c8-fb14-5b11aefc6f26"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "47625735"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "style_gan.grow_model(64)\n",
        "style_gan.load_weights(\"/content/checkpoints/stylegan_64x64.ckpt\")\n",
        "\n",
        "tf.random.set_seed(196)\n",
        "batch_size = 1\n",
        "# plot_images(images, 2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "az0O3ZGzR_DC",
        "outputId": "bdce41e1-c604-403d-cba9-ba11054df8c0"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Model resolution:64x64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from PIL import Image\n",
        "from tqdm.notebook import tqdm"
      ],
      "metadata": {
        "id": "-n2JoxJJXq_c"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!mkdir out_gen"
      ],
      "metadata": {
        "id": "coDlpeyplgtD"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for i in tqdm(range(len(os.listdir(\"/content/data/afhq/train/dog\")))):\n",
        "  z = tf.random.normal((batch_size, style_gan.z_dim))\n",
        "  w = style_gan.mapping(z)\n",
        "  noise = style_gan.generate_noise(batch_size=batch_size)\n",
        "  image = style_gan({\"style_code\": w, \"noise\": noise, \"alpha\": 1.0}).squeeze()\n",
        "  pil = Image.fromarray(image)\n",
        "  pil.save(f\"/content/out_gen/img_{i}.png\")\n",
        "  # print(image.shape, image.dtype, type(image))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "7b3a73772a9f40ab940534f8efec6518",
            "3ac813550dbe4e009dadba0e00ad05e9",
            "22eca22fabe54b508b2caa4d1c54ce64",
            "725c2a438113464fa53a7d54c123900b",
            "58199245ba6e4d3b8100781a8a813b1d",
            "fb7b69b968f9475084535f1b7f2a3bc2",
            "a1e8f8b572cb4055882d2cf1c3105547",
            "61edf555e741439480fb159d9bb4b930",
            "c0d33f56c1f74681be497a2c016a13ef",
            "7b2fc17b585b43f2874abdac7f663273",
            "4b6869c4238440e794e316193a8e31b8"
          ]
        },
        "id": "O4tIlBhmWkpC",
        "outputId": "bec99a27-6be2-454f-dc0c-e97479ec2147"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/4739 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "7b3a73772a9f40ab940534f8efec6518"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install pytorch-fid"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1wt-GfEBXfzk",
        "outputId": "ea3a144e-7f3b-4a75-eb1c-576a61ed44d8"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: pytorch-fid in /usr/local/lib/python3.10/dist-packages (0.3.0)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from pytorch-fid) (1.23.5)\n",
            "Requirement already satisfied: pillow in /usr/local/lib/python3.10/dist-packages (from pytorch-fid) (9.4.0)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.10/dist-packages (from pytorch-fid) (1.11.4)\n",
            "Requirement already satisfied: torch>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from pytorch-fid) (2.1.0+cu121)\n",
            "Requirement already satisfied: torchvision>=0.2.2 in /usr/local/lib/python3.10/dist-packages (from pytorch-fid) (0.16.0+cu121)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from torch>=1.0.1->pytorch-fid) (3.13.1)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.10/dist-packages (from torch>=1.0.1->pytorch-fid) (4.5.0)\n",
            "Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch>=1.0.1->pytorch-fid) (1.12)\n",
            "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch>=1.0.1->pytorch-fid) (3.2.1)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch>=1.0.1->pytorch-fid) (3.1.2)\n",
            "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from torch>=1.0.1->pytorch-fid) (2023.6.0)\n",
            "Requirement already satisfied: triton==2.1.0 in /usr/local/lib/python3.10/dist-packages (from torch>=1.0.1->pytorch-fid) (2.1.0)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from torchvision>=0.2.2->pytorch-fid) (2.31.0)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch>=1.0.1->pytorch-fid) (2.1.3)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->torchvision>=0.2.2->pytorch-fid) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->torchvision>=0.2.2->pytorch-fid) (3.6)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->torchvision>=0.2.2->pytorch-fid) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->torchvision>=0.2.2->pytorch-fid) (2023.11.17)\n",
            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch>=1.0.1->pytorch-fid) (1.3.0)\n",
            "/usr/bin/python3: No module named pytorch-fid\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!python -m pytorch_fid --device cuda:0 /content/data/afhq/train/dog /content/out_gen/"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rV9jT3tnYPnX",
        "outputId": "c0be0950-ca03-40b0-8d50-a2b990fcc0d4"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading: \"https://github.com/mseitzer/pytorch-fid/releases/download/fid_weights/pt_inception-2015-12-05-6726825d.pth\" to /root/.cache/torch/hub/checkpoints/pt_inception-2015-12-05-6726825d.pth\n",
            "100% 91.2M/91.2M [00:00<00:00, 206MB/s]\n",
            "100% 95/95 [00:38<00:00,  2.48it/s]\n",
            "100% 95/95 [00:19<00:00,  4.90it/s]\n",
            "FID:  355.62043858157955\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Yo8-s9dfsVna"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}