8-8-generative-adversarial-networks.ipynb

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   "source": [
    "# Generative Adversarial Networks\n",
    "\n",
    "&copy; 李浩东 haodongli@zju.edu.cn\n",
    "\n",
    "- Review\n",
    "- Pokemon Generation\n",
    "- Style Transfer\n"
   ]
  },
  {
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   "id": "a267b45b",
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   "source": [
    "## Review (~2021)\n",
    "\n",
    "|Common GAN variants and extensions\t|Application area|\n",
    "|:-----:|:-----:|\n",
    "|DCGAN(Radford et al., 2016)\t|Image Generation|\n",
    "|cGAN(Mirza and Osindero, 2014)\t|Semi supervised conditional Image Generation|\n",
    "|InfoGAN(Chen et al., 2016)\t|Unsupervised learning of interpretable and disentangled representations|\n",
    "|StackGAN(Zhang et al., 2017)\t|Image generation based on text inputs|\n",
    "|Pix2Pix(Isola et al., 2017)\t|Image to image translation|\n",
    "|CycleGAN(Zhu et al., 2017)\t|Unpaired Image to image translation|\n",
    "|StyleGAN(Karras et al., 2020)\t|High resolution facial image generation that are diverse in nature|\n",
    "|RGAN, RCGAN(Esteban et al., 2017)\t|Synthetic medical time series data generation|\n",
    "|TimeGAN(Yoon et al., 2019)\t|Realistic time-series data generation|"
   ]
  },
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    "- A schematic view of variants of GAN. $c$ represents the conditional vector. In `CGAN` and `ACGAN`, $c$ is the discrete categorical code (e.g. one hot vector) that encodes class labels and in `InfoGAN` it can also be continuous code that encodes attributes. $x_g$ generally refers to the generated image but can also be internal representations as in `SGAN`.\n",
    "\n",
    "![gan_1](./images/8/gan_1.png)"
   ]
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    "- `cGAN` frameworks for image-to-image translation. `pix2pix` requires aligned training data whereas this constraint is relaxed in `CycleGAN` but usually suffers from performance loss. Note that in (a), we chose reconstruction loss as an example of target consistency. This supervision is task related and can take many other different forms. (c) It consists of two `VAEGAN`s with shared latent vector in the `VAE` part.\n",
    "\n",
    "![gan_2](./images/8/gan_2.png)"
   ]
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   "source": [
    "**Pix2pix:  [Project](https://phillipi.github.io/pix2pix/) |  [Paper](https://arxiv.org/pdf/1611.07004.pdf) |  [Torch](https://github.com/phillipi/pix2pix) |\n",
    "[Tensorflow Core Tutorial](https://www.tensorflow.org/tutorials/generative/pix2pix) | [PyTorch Colab](https://colab.research.google.com/github/junyanz/pytorch-CycleGAN-and-pix2pix/blob/master/pix2pix.ipynb)**\n",
    "\n",
    "![gan_3](./images/8/gan_3.png)"
   ]
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   "id": "b146dc5a",
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   "source": [
    "**CycleGAN: [Project](https://junyanz.github.io/CycleGAN/) |  [Paper](https://arxiv.org/pdf/1703.10593.pdf) |  [Torch](https://github.com/junyanz/CycleGAN) |\n",
    "[Tensorflow Core Tutorial](https://www.tensorflow.org/tutorials/generative/cyclegan) | [PyTorch Colab](https://colab.research.google.com/github/junyanz/pytorch-CycleGAN-and-pix2pix/blob/master/CycleGAN.ipynb)**\n",
    "\n",
    "![gan_3](./images/8/gan_4.png)\n",
    "\n",
    "![horse2zebra.gif](./images/8/horse2zebra.gif)"
   ]
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   "id": "e9f6aec8",
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   "source": [
    "![gan_3](./images/8/gan_5.png)\n",
    "\n",
    "- We propose an alternative generator architecture for\n",
    "generative adversarial networks, borrowing from style\n",
    "transfer literature. The new architecture leads to an automatically learned, unsupervised separation of **high-level\n",
    "attributes (e.g., pose and identity when trained on human\n",
    "faces) and stochastic variation in the generated images\n",
    "(e.g., freckles, hair)**, and it enables intuitive, scale-specific\n",
    "control of the synthesis. The new generator improves the\n",
    "state-of-the-art in terms of traditional distribution quality\n",
    "metrics, leads to **demonstrably better interpolation properties, and also better disentangles the latent factors of variation**. To quantify interpolation quality and disentanglement,\n",
    "we propose two new, automated methods that are applicable to any generator architecture. Finally, we introduce a\n",
    "new, highly varied and high-quality dataset of human faces.\n",
    "- Paper: https://arxiv.org/abs/1812.04948\n",
    "- Video: https://youtu.be/kSLJriaOumA\n",
    "- Code: https://github.com/NVlabs/stylegan\n",
    "- FFHQ: https://github.com/NVlabs/ffhq-dataset\n",
    "\n"
   ]
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    "- Copying a specified subset of styles from source B and taking the rest from source A. \n",
    "- Copying the styles corresponding to **coarse spatial resolutions** $(4^2– 8^2)$ brings high-level aspects such as **pose, general hair style, face shape, and yeglasses from source B** while all **colors (eyes, hair, lighting) and finer facial features resemble A** \n",
    "- If we instead copy the styles of **middle resolutions** $(16^2– 32^2)$ from B, we inherit **smaller scale facial features, hair style, eyes open/closed from B** while the **pose, general face shape, and eyeglasses from A** are preserved.\n",
    "- Finally, copying the **fine styles** $(64^2– 1024^2)$ from **B brings mainly the color scheme and microstructure**\n",
    "\n",
    "![gan_3](./images/8/gan_6.png)"
   ]
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   "source": [
    "## Pokemon Generation\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "7e98a181",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading ../data/pokemon.zip from http://d2l-data.s3-accelerate.amazonaws.com/pokemon.zip...\n"
     ]
    }
   ],
   "source": [
    "import warnings\n",
    "import torch\n",
    "import torchvision\n",
    "from torch import nn\n",
    "from d2l import torch as d2l\n",
    "\n",
    "#@save\n",
    "d2l.DATA_HUB['pokemon'] = (d2l.DATA_URL + 'pokemon.zip',\n",
    "                           'c065c0e2593b8b161a2d7873e42418bf6a21106c')\n",
    "\n",
    "data_dir = d2l.download_extract('pokemon')\n",
    "pokemon = torchvision.datasets.ImageFolder(data_dir)\n",
    "\n",
    "batch_size = 256\n",
    "transformer = torchvision.transforms.Compose([\n",
    "    torchvision.transforms.Resize((64, 64)),\n",
    "    torchvision.transforms.ToTensor(),\n",
    "    torchvision.transforms.Normalize(0.5, 0.5)\n",
    "])\n",
    "pokemon.transform = transformer\n",
    "data_iter = torch.utils.data.DataLoader(\n",
    "    pokemon, batch_size=batch_size,\n",
    "    shuffle=True, num_workers=d2l.get_dataloader_workers())"
   ]
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       "<Figure size 540x432 with 20 Axes>"
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   "source": [
    "warnings.filterwarnings('ignore')\n",
    "d2l.set_figsize((4, 4))\n",
    "for X, y in data_iter:\n",
    "    imgs = X[:20,:,:,:].permute(0, 2, 3, 1)/2+0.5\n",
    "    d2l.show_images(imgs, num_rows=4, num_cols=5)\n",
    "    break"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d0eb9de1",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### The Generator and Discriminator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "3d4e4abd",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "class G_block(nn.Module):\n",
    "    def __init__(self, out_channels, in_channels=3, kernel_size=4, strides=2,\n",
    "                 padding=1, **kwargs):\n",
    "        super(G_block, self).__init__(**kwargs)\n",
    "        self.conv2d_trans = nn.ConvTranspose2d(in_channels, out_channels,\n",
    "                                kernel_size, strides, padding, bias=False)\n",
    "        self.batch_norm = nn.BatchNorm2d(out_channels)\n",
    "        self.activation = nn.ReLU()\n",
    "\n",
    "    def forward(self, X):\n",
    "        return self.activation(self.batch_norm(self.conv2d_trans(X)))\n",
    "\n",
    "n_G = 64\n",
    "net_G = nn.Sequential(\n",
    "    G_block(in_channels=100, out_channels=n_G*8,\n",
    "            strides=1, padding=0),                  # Output: (64 * 8, 4, 4)\n",
    "    G_block(in_channels=n_G*8, out_channels=n_G*4), # Output: (64 * 4, 8, 8)\n",
    "    G_block(in_channels=n_G*4, out_channels=n_G*2), # Output: (64 * 2, 16, 16)\n",
    "    G_block(in_channels=n_G*2, out_channels=n_G),   # Output: (64, 32, 32)\n",
    "    nn.ConvTranspose2d(in_channels=n_G, out_channels=3,\n",
    "                       kernel_size=4, stride=2, padding=1, bias=False),\n",
    "    nn.Tanh())  # Output: (3, 64, 64)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "514d8395",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "class D_block(nn.Module):\n",
    "    def __init__(self, out_channels, in_channels=3, kernel_size=4, strides=2,\n",
    "                padding=1, alpha=0.2, **kwargs):\n",
    "        super(D_block, self).__init__(**kwargs)\n",
    "        self.conv2d = nn.Conv2d(in_channels, out_channels, kernel_size,\n",
    "                                strides, padding, bias=False)\n",
    "        self.batch_norm = nn.BatchNorm2d(out_channels)\n",
    "        self.activation = nn.LeakyReLU(alpha, inplace=True)\n",
    "\n",
    "    def forward(self, X):\n",
    "        return self.activation(self.batch_norm(self.conv2d(X)))\n",
    "    \n",
    "n_D = 64\n",
    "net_D = nn.Sequential(\n",
    "    D_block(n_D),  # Output: (64, 32, 32)\n",
    "    D_block(in_channels=n_D, out_channels=n_D*2),  # Output: (64 * 2, 16, 16)\n",
    "    D_block(in_channels=n_D*2, out_channels=n_D*4),  # Output: (64 * 4, 8, 8)\n",
    "    D_block(in_channels=n_D*4, out_channels=n_D*8),  # Output: (64 * 8, 4, 4)\n",
    "    nn.Conv2d(in_channels=n_D*8, out_channels=1,\n",
    "              kernel_size=4, bias=False))  # Output: (1, 1, 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb766c8e",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a24bb28c",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
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   },
   "outputs": [],
   "source": [
    "def train(net_D, net_G, data_iter, num_epochs, lr, latent_dim,\n",
    "          device=d2l.try_gpu()):\n",
    "    loss = nn.BCEWithLogitsLoss(reduction='sum')\n",
    "    for w in net_D.parameters():\n",
    "        nn.init.normal_(w, 0, 0.02)\n",
    "    for w in net_G.parameters():\n",
    "        nn.init.normal_(w, 0, 0.02)\n",
    "    net_D, net_G = net_D.to(device), net_G.to(device)\n",
    "    trainer_hp = {'lr': lr, 'betas': [0.5,0.999]}\n",
    "    trainer_D = torch.optim.Adam(net_D.parameters(), **trainer_hp)\n",
    "    trainer_G = torch.optim.Adam(net_G.parameters(), **trainer_hp)\n",
    "    animator = d2l.Animator(xlabel='epoch', ylabel='loss',\n",
    "                            xlim=[1, num_epochs], nrows=2, figsize=(5, 5),\n",
    "                            legend=['discriminator', 'generator'])\n",
    "    animator.fig.subplots_adjust(hspace=0.3)\n",
    "    for epoch in range(1, num_epochs + 1):\n",
    "        # Train one epoch\n",
    "        timer = d2l.Timer()\n",
    "        metric = d2l.Accumulator(3)  # loss_D, loss_G, num_examples\n",
    "        for X, _ in data_iter:\n",
    "            batch_size = X.shape[0]\n",
    "            Z = torch.normal(0, 1, size=(batch_size, latent_dim, 1, 1))\n",
    "            X, Z = X.to(device), Z.to(device)\n",
    "            metric.add(d2l.update_D(X, Z, net_D, net_G, loss, trainer_D),\n",
    "                       d2l.update_G(Z, net_D, net_G, loss, trainer_G),\n",
    "                       batch_size)\n",
    "        # Show generated examples\n",
    "        Z = torch.normal(0, 1, size=(21, latent_dim, 1, 1), device=device)\n",
    "        # Normalize the synthetic data to N(0, 1)\n",
    "        fake_x = net_G(Z).permute(0, 2, 3, 1) / 2 + 0.5\n",
    "        imgs = torch.cat(\n",
    "            [torch.cat([\n",
    "                fake_x[i * 7 + j].cpu().detach() for j in range(7)], dim=1)\n",
    "             for i in range(len(fake_x)//7)], dim=0)\n",
    "        animator.axes[1].cla()\n",
    "        animator.axes[1].imshow(imgs)\n",
    "        # Show the losses\n",
    "        loss_D, loss_G = metric[0] / metric[2], metric[1] / metric[2]\n",
    "        animator.add(epoch, (loss_D, loss_G))\n",
    "    print(f'loss_D {loss_D:.3f}, loss_G {loss_G:.3f}, '\n",
    "          f'{metric[2] / timer.stop():.1f} examples/sec on {str(device)}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "5176bde8",
   "metadata": {
    "slideshow": {
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   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss_D 0.041, loss_G 6.817, 1155.7 examples/sec on cuda:0\n"
     ]
    },
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      "text/plain": [
       "<Figure size 360x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
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     "output_type": "display_data"
    }
   ],
   "source": [
    "latent_dim, lr, num_epochs = 100, 0.005, 20\n",
    "train(net_D, net_G, data_iter, num_epochs, lr, latent_dim)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c7f5337",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Style Transfer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "783d158c",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-03-13 14:39:48.582924: I tensorflow/core/util/util.cc:169] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "import os\n",
    "import numpy as np\n",
    "import PIL.Image\n",
    "\n",
    "\n",
    "def tensor_to_image(tensor):\n",
    "    tensor = tensor * 255\n",
    "    tensor = np.array(tensor, dtype=np.uint8)\n",
    "    if np.ndim(tensor) > 3:\n",
    "        assert tensor.shape[0] == 1\n",
    "        tensor = tensor[0]\n",
    "    return PIL.Image.fromarray(tensor)\n",
    "\n",
    "\n",
    "def load_img(path_to_img, max_dim):\n",
    "    img = tf.io.read_file(path_to_img)\n",
    "    img = tf.image.decode_image(img, channels=3)\n",
    "    img = tf.image.convert_image_dtype(img, tf.float32)\n",
    "\n",
    "    shape = tf.cast(tf.shape(img)[:-1], tf.float32)\n",
    "    long_dim = max(shape)\n",
    "    scale = max_dim / long_dim\n",
    "\n",
    "    new_shape = tf.cast(shape * scale, tf.int32)\n",
    "\n",
    "    img = tf.image.resize(img, new_shape)\n",
    "    img = img[tf.newaxis, :]\n",
    "    return img"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c1e78d7c",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d142c5af",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "def vgg_layers(layer_names):\n",
    "    \"\"\"Creates a vgg model that returns a list of intermediate output values.\"\"\"\n",
    "    vgg = tf.keras.applications.VGG19(include_top=False, weights=\"imagenet\")\n",
    "    vgg.trainable = False\n",
    "\n",
    "    outputs = [vgg.get_layer(name).output for name in layer_names]\n",
    "\n",
    "    model = tf.keras.Model([vgg.input], outputs)\n",
    "    return model\n",
    "\n",
    "\n",
    "def gram_matrix(input_tensor):\n",
    "    result = tf.linalg.einsum(\"bijc,bijd->bcd\", input_tensor, input_tensor)\n",
    "    input_shape = tf.shape(input_tensor)\n",
    "    num_locations = tf.cast(input_shape[1] * input_shape[2], tf.float32)\n",
    "    return result / (num_locations)\n",
    "\n",
    "\n",
    "class StyleContentModel(tf.keras.models.Model):\n",
    "    def __init__(self, style_layers, content_layers):\n",
    "        super(StyleContentModel, self).__init__()\n",
    "        self.vgg = vgg_layers(style_layers + content_layers)\n",
    "        self.style_layers = style_layers\n",
    "        self.content_layers = content_layers\n",
    "        self.num_style_layers = len(style_layers)\n",
    "        self.vgg.trainable = False\n",
    "\n",
    "    def call(self, inputs):\n",
    "        \"Expects float input in [0,1]\"\n",
    "        inputs = inputs * 255.0\n",
    "        preprocessed_input = tf.keras.applications.vgg19.preprocess_input(inputs)\n",
    "        outputs = self.vgg(preprocessed_input)\n",
    "        style_outputs, content_outputs = (\n",
    "            outputs[: self.num_style_layers],\n",
    "            outputs[self.num_style_layers :],\n",
    "        )\n",
    "\n",
    "        style_outputs = [gram_matrix(style_output) for style_output in style_outputs]\n",
    "\n",
    "        content_dict = {\n",
    "            content_name: value\n",
    "            for content_name, value in zip(self.content_layers, content_outputs)\n",
    "        }\n",
    "\n",
    "        style_dict = {\n",
    "            style_name: value\n",
    "            for style_name, value in zip(self.style_layers, style_outputs)\n",
    "        }\n",
    "\n",
    "        return {\"content\": content_dict, \"style\": style_dict}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7be48121",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "abe81b3e",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "def style_transfer_image(content, style, epochs=10, steps_per_epoch=100, style_weight=1e-2,\n",
    "    content_weight=1e4, total_variation_weight=30, max_dim=1000, save_name=\"output.png\"):\n",
    "    content_img = load_img(content, max_dim=max_dim)\n",
    "    style_img = load_img(style, max_dim=max_dim)\n",
    "\n",
    "    content_layers = [\"block5_conv2\"]\n",
    "\n",
    "    style_layers = [\"block1_conv1\", \"block2_conv1\", \"block3_conv1\", \"block4_conv1\", \"block5_conv1\"]\n",
    "\n",
    "    num_content_layers = len(content_layers)\n",
    "    num_style_layers = len(style_layers)\n",
    "\n",
    "    extractor = StyleContentModel(style_layers, content_layers)\n",
    "\n",
    "    style_targets = extractor(style_img)[\"style\"]\n",
    "    content_targets = extractor(content_img)[\"content\"]\n",
    "\n",
    "    image = tf.Variable(content_img)\n",
    "\n",
    "    def clip_0_1(image):\n",
    "        return tf.clip_by_value(image, clip_value_min=0.0, clip_value_max=1.0)\n",
    "\n",
    "    opt = tf.optimizers.Adam(learning_rate=0.02, beta_1=0.99, epsilon=1e-1)\n",
    "\n",
    "    def style_content_loss(outputs):\n",
    "        style_outputs = outputs[\"style\"]\n",
    "        content_outputs = outputs[\"content\"]\n",
    "        style_loss = tf.add_n(\n",
    "            [\n",
    "                tf.reduce_mean((style_outputs[name] - style_targets[name]) ** 2)\n",
    "                for name in style_outputs.keys()\n",
    "            ]\n",
    "        )\n",
    "        style_loss *= style_weight / num_style_layers\n",
    "\n",
    "        content_loss = tf.add_n(\n",
    "            [\n",
    "                tf.reduce_mean((content_outputs[name] - content_targets[name]) ** 2)\n",
    "                for name in content_outputs.keys()\n",
    "            ]\n",
    "        )\n",
    "        content_loss *= content_weight / num_content_layers\n",
    "        loss = style_loss + content_loss\n",
    "        return loss\n",
    "\n",
    "    @tf.function()\n",
    "    def train_step(image):\n",
    "        with tf.GradientTape() as tape:\n",
    "            outputs = extractor(image)\n",
    "            loss = style_content_loss(outputs)\n",
    "            loss += total_variation_weight * tf.image.total_variation(image)\n",
    "\n",
    "        grad = tape.gradient(loss, image)\n",
    "        opt.apply_gradients([(grad, image)])\n",
    "        image.assign(clip_0_1(image))\n",
    "        return loss\n",
    "\n",
    "    for n in range(epochs):\n",
    "        for m in range(steps_per_epoch):\n",
    "            print(f\"Epoch: {n+1}/{epochs}, \\t\\t\\tStep:\", m + 1, \" of \", steps_per_epoch, end=\"\\r\")\n",
    "            train_step(image)\n",
    "        img = tensor_to_image(image)\n",
    "        img.save(f\"{save_name}_{n}.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "550ec94e",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training for Content -> zju_2.jpg  and Style -> art.jpg\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-03-13 14:39:52.251510: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2023-03-13 14:39:52.274229: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2023-03-13 14:39:52.274368: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2023-03-13 14:39:52.275460: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 AVX512F AVX512_VNNI FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2023-03-13 14:39:52.276666: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2023-03-13 14:39:52.276784: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2023-03-13 14:39:52.276859: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2023-03-13 14:39:52.610927: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2023-03-13 14:39:52.611055: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2023-03-13 14:39:52.611138: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2023-03-13 14:39:52.611217: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1532] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 4129 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 3060 Laptop GPU, pci bus id: 0000:01:00.0, compute capability: 8.6\n",
      "2023-03-13 14:39:53.581183: I tensorflow/stream_executor/cuda/cuda_dnn.cc:384] Loaded cuDNN version 8401\n",
      "2023-03-13 14:39:55.505816: W tensorflow/core/common_runtime/bfc_allocator.cc:290] Allocator (GPU_0_bfc) ran out of memory trying to allocate 3.55GiB with freed_by_count=0. The caller indicates that this is not a failure, but this may mean that there could be performance gains if more memory were available.\n",
      "2023-03-13 14:39:55.505846: W tensorflow/core/common_runtime/bfc_allocator.cc:290] Allocator (GPU_0_bfc) ran out of memory trying to allocate 3.55GiB with freed_by_count=0. The caller indicates that this is not a failure, but this may mean that there could be performance gains if more memory were available.\n",
      "2023-03-13 14:39:56.159746: I tensorflow/stream_executor/cuda/cuda_blas.cc:1786] TensorFloat-32 will be used for the matrix multiplication. This will only be logged once.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 1/10, \t\t\tStep: 1  of  100\r"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-03-13 14:39:59.091552: W tensorflow/core/common_runtime/bfc_allocator.cc:290] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.93GiB with freed_by_count=0. The caller indicates that this is not a failure, but this may mean that there could be performance gains if more memory were available.\n",
      "2023-03-13 14:39:59.091582: W tensorflow/core/common_runtime/bfc_allocator.cc:290] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.93GiB with freed_by_count=0. The caller indicates that this is not a failure, but this may mean that there could be performance gains if more memory were available.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training for Content -> zju_2.jpg  and Style -> greatwave.jpg\n",
      "Training for Content -> zju_1.png  and Style -> art.jpg\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-03-13 14:45:38.347957: W tensorflow/core/common_runtime/bfc_allocator.cc:290] Allocator (GPU_0_bfc) ran out of memory trying to allocate 3.98GiB with freed_by_count=0. The caller indicates that this is not a failure, but this may mean that there could be performance gains if more memory were available.\n",
      "2023-03-13 14:45:38.347995: W tensorflow/core/common_runtime/bfc_allocator.cc:290] Allocator (GPU_0_bfc) ran out of memory trying to allocate 3.98GiB with freed_by_count=0. The caller indicates that this is not a failure, but this may mean that there could be performance gains if more memory were available.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 1/10, \t\t\tStep: 1  of  100\r"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-03-13 14:45:40.893936: W tensorflow/core/common_runtime/bfc_allocator.cc:290] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.72GiB with freed_by_count=0. The caller indicates that this is not a failure, but this may mean that there could be performance gains if more memory were available.\n",
      "2023-03-13 14:45:40.893992: W tensorflow/core/common_runtime/bfc_allocator.cc:290] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.72GiB with freed_by_count=0. The caller indicates that this is not a failure, but this may mean that there could be performance gains if more memory were available.\n",
      "2023-03-13 14:45:41.290411: W tensorflow/core/common_runtime/bfc_allocator.cc:290] Allocator (GPU_0_bfc) ran out of memory trying to allocate 3.98GiB with freed_by_count=0. The caller indicates that this is not a failure, but this may mean that there could be performance gains if more memory were available.\n",
      "2023-03-13 14:45:41.290447: W tensorflow/core/common_runtime/bfc_allocator.cc:290] Allocator (GPU_0_bfc) ran out of memory trying to allocate 3.98GiB with freed_by_count=0. The caller indicates that this is not a failure, but this may mean that there could be performance gains if more memory were available.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training for Content -> zju_1.png  and Style -> greatwave.jpg\n",
      "Epoch: 10/10, \t\t\tStep: 100  of  100\r"
     ]
    }
   ],
   "source": [
    "path = \"./8-8-generative-adversarial-networks\"\n",
    "contents = os.listdir(os.path.join(path, \"content\"))\n",
    "styles = os.listdir(os.path.join(path, \"style\"))\n",
    "os.mkdir(os.path.join(path, \"save\")) if os.path.exists(os.path.join(path, \"save\")) is False else None\n",
    "for C in contents:\n",
    "    for S in styles:\n",
    "        print(\"Training for Content ->\", C, \" and Style ->\", S)\n",
    "        style_transfer_image(os.path.join(os.path.join(path, \"content\"), C), \n",
    "                             os.path.join(os.path.join(path, \"style\"), S), \n",
    "                             save_name=os.path.join(os.path.join(path, \"save\"), \n",
    "                                                    C.split('.')[0] + '_' + S.split('.')[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c0cdf0c8",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "<table>\n",
    "  <tr>\n",
    "    <td><center><b>Content</b></center></td>\n",
    "     <td><center><b>Style</b></center></td>\n",
    "     <td><center><b>Output</b></center></td>\n",
    "  </tr>\n",
    "  <tr>\n",
    "    <td><img src=\"./8-8-generative-adversarial-networks/content/zju_2.jpg\" width=400 ></td>\n",
    "    <td><img src=\"./8-8-generative-adversarial-networks/style/art.jpg\" width=400 ></td>\n",
    "    <td><img src=\"./8-8-generative-adversarial-networks/save/zju_2_art_9.png\" width=400 ></td>\n",
    "  </tr>\n",
    " </table>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a3337b7b",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "<table>\n",
    "  <tr>\n",
    "    <td><center><b>Content</b></center></td>\n",
    "     <td><center><b>Style</b></center></td>\n",
    "     <td><center><b>Output</b></center></td>\n",
    "  </tr>\n",
    "  <tr>\n",
    "    <td><img src=\"./8-8-generative-adversarial-networks/content/zju_2.jpg\" width=400 ></td>\n",
    "    <td><img src=\"./8-8-generative-adversarial-networks/style/greatwave.jpg\" width=400 ></td>\n",
    "    <td><img src=\"./8-8-generative-adversarial-networks/save/zju_2_greatwave_9.png\" width=400 ></td>\n",
    "  </tr>\n",
    " </table>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "957828fc",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "<table>\n",
    "  <tr>\n",
    "    <td><center><b>Content</b></center></td>\n",
    "     <td><center><b>Style</b></center></td>\n",
    "     <td><center><b>Output</b></center></td>\n",
    "  </tr>\n",
    "  <tr>\n",
    "    <td><img src=\"./8-8-generative-adversarial-networks/content/zju_1.png\" width=400 ></td>\n",
    "    <td><img src=\"./8-8-generative-adversarial-networks/style/art.jpg\" width=400 ></td>\n",
    "    <td><img src=\"./8-8-generative-adversarial-networks/save/zju_1_art_9.png\" width=400 ></td>\n",
    "  </tr>\n",
    " </table>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "208618ec",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "<table>\n",
    "  <tr>\n",
    "    <td><center><b>Content</b></center></td>\n",
    "     <td><center><b>Style</b></center></td>\n",
    "     <td><center><b>Output</b></center></td>\n",
    "  </tr>\n",
    "  <tr>\n",
    "    <td><img src=\"./8-8-generative-adversarial-networks/content/zju_1.png\" width=400 ></td>\n",
    "    <td><img src=\"./8-8-generative-adversarial-networks/style/greatwave.jpg\" width=400 ></td>\n",
    "    <td><img src=\"./8-8-generative-adversarial-networks/save/zju_1_greatwave_9.png\" width=400 ></td>\n",
    "  </tr>\n",
    " </table>"
   ]
  }
 ],
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