{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"# 1. Description of the problem\n\nThe objective of this notebook is to apply Monet's painting style to our photographs using GAN models, specifically by employing the CycleGAN architecture. We will train this model using the dataset located in the monet_tfrec directory. The photographs that we intend to transform with Monet-style elements can be found in the photo_tfrec directory. For this project, we have opted to use the TFRecord format instead of the JPG format.","metadata":{}},{"cell_type":"code","source":"import tensorflow as tf\nfrom tensorflow import keras\nfrom tensorflow.keras import layers\n\n# Disable the layout optimizer\ntf.config.optimizer.set_experimental_options({'layout_optimizer': False})\n\nfrom kaggle_datasets import KaggleDatasets\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport re\n\ntry:\n    tpu = tf.distribute.cluster_resolver.TPUClusterResolver()\n    print('Device:', tpu.master())\n    tf.config.experimental_connect_to_cluster(tpu)\n    tf.tpu.experimental.initialize_tpu_system(tpu)\n    strategy = tf.distribute.experimental.TPUStrategy(tpu)\nexcept:\n    strategy = tf.distribute.get_strategy()\nprint('Number of replicas:', strategy.num_replicas_in_sync)\n\nAUTOTUNE = tf.data.experimental.AUTOTUNE\n    \nprint(tf.__version__)","metadata":{"execution":{"iopub.status.busy":"2023-04-15T12:10:25.427513Z","iopub.execute_input":"2023-04-15T12:10:25.428464Z","iopub.status.idle":"2023-04-15T12:10:34.231617Z","shell.execute_reply.started":"2023-04-15T12:10:25.428419Z","shell.execute_reply":"2023-04-15T12:10:34.230163Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# 2. Exploratory data analysis (EDA) \n\nPer the recommendation of the competition, we need to use Amy Jang's notebook that goes over the basics of loading data from TFRecords, using TPUs, and building a CycleGAN. We want to keep our photo dataset and our Monet dataset separately. Load in the filenames of the TFRecords with the size of 256x256. Set the channel to 3 because these images are RGB images, scale the images to a [-1, 1] scale.","metadata":{}},{"cell_type":"markdown","source":"**2.1 Count Files**\n","metadata":{}},{"cell_type":"code","source":"GCS_PATH = KaggleDatasets().get_gcs_path()\n\nMONET_FILENAMES = tf.io.gfile.glob(str(GCS_PATH + '/monet_tfrec/*.tfrec'))\nPHOTO_FILENAMES = tf.io.gfile.glob(str(GCS_PATH + '/photo_tfrec/*.tfrec'))\n\n\nIMAGE_SIZE = [256, 256]\nBATCH_SIZE =  1\nEPOCHS_NUM = 20\n\n\ndef decode_image(image):\n    image = tf.image.decode_jpeg(image, channels=3)\n    image = (tf.cast(image, tf.float32) / 127.5) - 1\n    image = tf.reshape(image, [*IMAGE_SIZE, 3])\n    return image\n\ndef read_tfrecord(example):\n    tfrecord_format = {\n        \"image_name\": tf.io.FixedLenFeature([], tf.string),\n        \"image\": tf.io.FixedLenFeature([], tf.string),\n        \"target\": tf.io.FixedLenFeature([], tf.string)\n    }\n    example = tf.io.parse_single_example(example, tfrecord_format)\n    image = decode_image(example['image'])\n    return image\n\n#Define the function to extract the image from the files.\n\ndef load_dataset(filenames, labeled=True, ordered=False):\n    dataset = tf.data.TFRecordDataset(filenames)\n    dataset = dataset.map(read_tfrecord, num_parallel_calls=AUTOTUNE)\n    return dataset\n\ndef count_data_items(filenames):\n    n = [int(re.compile(r\"-([0-9]*)\\.\").search(filename).group(1)) for filename in filenames]\n    return np.sum(n)\n\n# Let's load in our datasets.\n\nmonet_ds = load_dataset(MONET_FILENAMES, labeled=True).batch(1)\nphoto_ds = load_dataset(PHOTO_FILENAMES, labeled=True).batch(1)\n\n\nn_monet_samples = count_data_items(MONET_FILENAMES)\nn_photo_samples = count_data_items(PHOTO_FILENAMES)\n\nprint(f'Monet TFRecord files: {len(MONET_FILENAMES)}')\nprint(f'Monet image files: {n_monet_samples}')\nprint(f'Photo TFRecord files: {len(PHOTO_FILENAMES)}')\nprint(f'Photo image files: {n_photo_samples}')\nprint(f\"Batch_size: {BATCH_SIZE}\")\nprint(f\"Epochs number: {EPOCHS_NUM}\")","metadata":{"execution":{"iopub.status.busy":"2023-04-15T12:10:48.844181Z","iopub.execute_input":"2023-04-15T12:10:48.844562Z","iopub.status.idle":"2023-04-15T12:10:49.600670Z","shell.execute_reply.started":"2023-04-15T12:10:48.844529Z","shell.execute_reply":"2023-04-15T12:10:49.599332Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"**2.2 Display some samples of images from the dataset.**","metadata":{}},{"cell_type":"code","source":"def display_samples(ds, title, samples=5):\n    plt.figure(figsize=(10, 10))\n    for i, img in enumerate(ds.take(samples)):\n        plt.subplot(1, samples, i+1)\n        plt.title(f'{title} {i+1}')\n        plt.imshow(img[0] * 0.5 + 0.5)\n        plt.axis('off')\n    plt.show()\n\ndisplay_samples(monet_ds, 'Monet')\ndisplay_samples(photo_ds, 'Photo')","metadata":{"execution":{"iopub.status.busy":"2023-04-15T12:10:51.636088Z","iopub.execute_input":"2023-04-15T12:10:51.636464Z","iopub.status.idle":"2023-04-15T12:10:53.847327Z","shell.execute_reply.started":"2023-04-15T12:10:51.636432Z","shell.execute_reply":"2023-04-15T12:10:53.846223Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"**2.3 Get image dimensions.**","metadata":{}},{"cell_type":"code","source":"def get_dataset_info(ds):\n    num_samples = len(list(ds))\n    sample_image = next(iter(ds))\n    image_shape = sample_image.shape\n\n    return num_samples, image_shape\n\nmonet_samples, monet_image_shape = get_dataset_info(monet_ds)\nphoto_samples, photo_image_shape = get_dataset_info(photo_ds)\n\nprint(f'Monet samples: {monet_samples}, Image shape: {monet_image_shape}')\nprint(f'Photo samples: {photo_samples}, Image shape: {photo_image_shape}')\n","metadata":{"execution":{"iopub.status.busy":"2023-04-15T12:10:56.630953Z","iopub.execute_input":"2023-04-15T12:10:56.631334Z","iopub.status.idle":"2023-04-15T12:11:25.516536Z","shell.execute_reply.started":"2023-04-15T12:10:56.631296Z","shell.execute_reply":"2023-04-15T12:11:25.515274Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"**2.4 Analyze pixel intensity distribution.**","metadata":{}},{"cell_type":"code","source":"def plot_intensity_histogram(ds, title, samples=5):\n    plt.figure(figsize=(15, 3))\n    for i, img in enumerate(ds.take(samples)):\n        plt.subplot(1, samples, i+1)\n        plt.title(f'{title} {i+1}')\n        plt.hist((img[0] * 0.5 + 0.5).numpy().ravel(), bins=256, color='orange', alpha=0.5)\n        plt.xlim(0, 1)\n    plt.show()\n\nplot_intensity_histogram(monet_ds, 'Monet')\nplot_intensity_histogram(photo_ds, 'Photo')","metadata":{"execution":{"iopub.status.busy":"2023-04-15T12:11:25.518554Z","iopub.execute_input":"2023-04-15T12:11:25.520466Z","iopub.status.idle":"2023-04-15T12:11:31.714837Z","shell.execute_reply.started":"2023-04-15T12:11:25.520419Z","shell.execute_reply":"2023-04-15T12:11:31.713562Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"**2.5 Let's visualize difference between photo example and Monet example.**","metadata":{}},{"cell_type":"code","source":"example_photo = next(iter(photo_ds))\nexample_monet = next(iter(monet_ds))\n\nplt.subplot(122)\nplt.title('Monet')\nplt.imshow(example_monet[0] * 0.5 + 0.5)\n\nplt.subplot(121)\nplt.title('Photo')\nplt.imshow(example_photo[0] * 0.5 + 0.5)\n","metadata":{"execution":{"iopub.status.busy":"2023-04-15T12:11:33.859893Z","iopub.execute_input":"2023-04-15T12:11:33.861035Z","iopub.status.idle":"2023-04-15T12:11:35.337889Z","shell.execute_reply.started":"2023-04-15T12:11:33.860990Z","shell.execute_reply":"2023-04-15T12:11:35.336963Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# 3. Solution\n\nTo make the image style translation work, we decide to use CycleGAN model because its cycle consistency loss design ensures that the mapping between domains A and B is consistent, i.e., translating an image from A to B and back to A should result in the original image.\n\n\nCycleGAN is a Generative Adversarial Network (GAN) model designed for image-to-image translation tasks. It aims to learn a mapping between two domains, A and B, without paired examples. The model consists of two generator networks, G (A -> B) and F (B -> A), and two discriminator networks, D_A and D_B.\n\nModel Architecture:\nThe CycleGAN architecture consists of the following components:\n\n1) Generator G: Translates images from domain A to domain B.\n\n2) Generator F: Translates images from domain B to domain A.\n\n3) Discriminator D_A: Discriminates between real images from domain A and generated images from domain B.\n\n4) Discriminator D_B: Discriminates between real images from domain B and generated images from domain A.\n\nThe structure of CycleGAN can be interpret by following.\n\n![image.png](attachment:6bddddaa-1458-44ba-a382-53cd4925ee97.png)![image.png]","metadata":{},"attachments":{"6bddddaa-1458-44ba-a382-53cd4925ee97.png":{"image/png":"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"}}},{"cell_type":"markdown","source":"**3.1 Build the generator**\n\nIn our CycleGAN, we employ a UNET architecture for the generator, utilizing downsample and upsample methods. The downsample method reduces the image's width and height by the stride, applying the filter to every other pixel, effectively halving these dimensions. The generator uses instance normalization instead of batch normalization, and since it's not a standard TensorFlow API feature, we'll incorporate it from TensorFlow Add-ons.","metadata":{}},{"cell_type":"code","source":"# -----------------------------------------------------------------------------\n# The following code is adapted from:\n# Original Source: https://keras.io/examples/generative/cyclegan/\n# Original Author: A_K_Nain\n# -----------------------------------------------------------------------------\n\nOUTPUT_CHANNELS = 3\n\ndef downsample(filters, size, apply_batchnorm=True):\n    initializer = tf.random_normal_initializer(0., 0.02)\n    gamma_init = keras.initializers.RandomNormal(mean=0.0, stddev=0.02)\n\n    result = keras.Sequential()\n    result.add(layers.Conv2D(filters, size, strides=2, padding='same',\n                             kernel_initializer=initializer, use_bias=False))\n\n    if apply_batchnorm:\n        result.add(tfa.layers.InstanceNormalization(gamma_initializer=gamma_init))\n        #result.add(layers.BatchNormalization())\n\n    result.add(layers.LeakyReLU())\n\n    return result\n\n\ndef upsample(filters, size, apply_dropout=False):\n    initializer = tf.random_normal_initializer(0., 0.02)\n    gamma_init = keras.initializers.RandomNormal(mean=0.0, stddev=0.02)\n\n    result = keras.Sequential()\n    result.add(layers.Conv2DTranspose(filters, size, strides=2,\n                                      padding='same',\n                                      kernel_initializer=initializer,\n                                      use_bias=False))\n\n    result.add(tfa.layers.InstanceNormalization(gamma_initializer=gamma_init))\n    #result.add(layers.BatchNormalization())\n\n    if apply_dropout:\n        result.add(layers.Dropout(0.5))\n\n    result.add(layers.ReLU())\n\n    return result\n\n\ndef Generator():\n    inputs = layers.Input(shape=[256,256,3])\n\n    # bs = batch size\n    down_stack = [\n        #downsample(64, 4, apply_instancenorm=False), # (bs, 128, 128, 64)\n        downsample(64, 4, apply_batchnorm=False), #new\n        downsample(128, 4), # (bs, 64, 64, 128)\n        downsample(256, 4), # (bs, 32, 32, 256)\n        downsample(512, 4), # (bs, 16, 16, 512)\n        downsample(512, 4), # (bs, 8, 8, 512)\n        downsample(512, 4), # (bs, 4, 4, 512)\n        downsample(512, 4), # (bs, 2, 2, 512)\n        downsample(512, 4), # (bs, 1, 1, 512)\n    ]\n\n    up_stack = [\n        upsample(512, 4, apply_dropout=True), # (bs, 2, 2, 1024)\n        upsample(512, 4, apply_dropout=True), # (bs, 4, 4, 1024)\n        upsample(512, 4, apply_dropout=True), # (bs, 8, 8, 1024)\n        upsample(512, 4), # (bs, 16, 16, 1024)\n        upsample(256, 4), # (bs, 32, 32, 512)\n        upsample(128, 4), # (bs, 64, 64, 256)\n        upsample(64, 4), # (bs, 128, 128, 128)\n    ]\n\n    initializer = tf.random_normal_initializer(0., 0.02)\n    last = layers.Conv2DTranspose(OUTPUT_CHANNELS, 4,\n                                  strides=2,\n                                  padding='same',\n                                  kernel_initializer=initializer,\n                                  activation='tanh') # (bs, 256, 256, 3)\n\n    x = inputs\n\n    # Downsampling through the model\n    skips = []\n    for down in down_stack:\n        x = down(x)\n        skips.append(x)\n\n    skips = reversed(skips[:-1])\n\n    # Upsampling and establishing the skip connections\n    for up, skip in zip(up_stack, skips):\n        x = up(x)\n        x = layers.Concatenate()([x, skip])\n\n    x = last(x)\n\n    return keras.Model(inputs=inputs, outputs=x)","metadata":{"execution":{"iopub.status.busy":"2023-04-15T12:11:39.652189Z","iopub.execute_input":"2023-04-15T12:11:39.652573Z","iopub.status.idle":"2023-04-15T12:11:39.670151Z","shell.execute_reply.started":"2023-04-15T12:11:39.652536Z","shell.execute_reply":"2023-04-15T12:11:39.668931Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"**3.2 Build the discriminator**\n\nThe discriminator takes in the input image and classifies it as real or fake (generated). Instead of outputing a single node, the discriminator outputs a smaller 2D image with higher pixel values indicating a real classification and lower values indicating a fake classification. Since our generators are not trained yet, the generated Monet-esque photo does not show what is expected at this point.","metadata":{}},{"cell_type":"code","source":"import tensorflow_addons as tfa\n\ndef Discriminator():\n    initializer = tf.random_normal_initializer(0., 0.02)\n    gamma_init = keras.initializers.RandomNormal(mean=0.0, stddev=0.02)\n\n    inp = layers.Input(shape=[256, 256, 3], name='input_image')\n\n    x = inp\n\n    down1 = downsample(64, 4, False)(x) # (bs, 128, 128, 64)\n    down2 = downsample(128, 4)(down1) # (bs, 64, 64, 128)\n    down3 = downsample(256, 4)(down2) # (bs, 32, 32, 256)\n\n    zero_pad1 = layers.ZeroPadding2D()(down3) # (bs, 34, 34, 256)\n    conv = layers.Conv2D(512, 4, strides=1,\n                         kernel_initializer=initializer,\n                         use_bias=False)(zero_pad1) # (bs, 31, 31, 512)\n\n    norm1 = tfa.layers.InstanceNormalization(gamma_initializer=gamma_init)(conv)\n    #norm1 = layers.BatchNormalization()(conv)\n        \n\n    leaky_relu = layers.LeakyReLU()(norm1)\n\n    zero_pad2 = layers.ZeroPadding2D()(leaky_relu) # (bs, 33, 33, 512)\n\n    last = layers.Conv2D(1, 4, strides=1,\n                         kernel_initializer=initializer)(zero_pad2) # (bs, 30, 30, 1)\n\n    return tf.keras.Model(inputs=inp, outputs=last)\n\nwith strategy.scope():\n    monet_generator = Generator() # transforms photos to Monet-esque paintings\n    photo_generator = Generator() # transforms Monet paintings to be more like photos\n\n    monet_discriminator = Discriminator() # differentiates real Monet paintings and generated Monet paintings\n    photo_discriminator = Discriminator() # differentiates real photos and generated photos\n\n\nto_monet = monet_generator(example_photo)\n\nplt.subplot(1, 2, 1)\nplt.title(\"Original Photo\")\nplt.imshow(example_photo[0] * 0.5 + 0.5)\n\nplt.subplot(1, 2, 2)\nplt.title(\"Monet-esque Photo\")\nplt.imshow(to_monet[0] * 0.5 + 0.5)\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2023-04-15T12:11:48.325982Z","iopub.execute_input":"2023-04-15T12:11:48.326572Z","iopub.status.idle":"2023-04-15T12:11:58.561198Z","shell.execute_reply.started":"2023-04-15T12:11:48.326535Z","shell.execute_reply":"2023-04-15T12:11:58.559892Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def Discriminator():\n    initializer = tf.random_normal_initializer(0., 0.02)\n\n    inp = layers.Input(shape=[256, 256, 3], name='input_image')\n\n    x = inp\n\n    down1 = downsample(64, 4, False)(x)  # (bs, 128, 128, 64)\n    down2 = downsample(128, 4)(down1)  # (bs, 64, 64, 128)\n    down3 = downsample(256, 4)(down2)  # (bs, 32, 32, 256)\n\n    zero_pad1 = layers.ZeroPadding2D()(down3)  # (bs, 34, 34, 256)\n    conv = layers.Conv2D(512, 4, strides=1,\n                         kernel_initializer=initializer,\n                         use_bias=False)(zero_pad1)  # (bs, 31, 31, 512)\n\n    norm1 = layers.BatchNormalization()(conv)\n\n    leaky_relu = layers.LeakyReLU()(norm1)\n\n    zero_pad2 = layers.ZeroPadding2D()(leaky_relu)  # (bs, 33, 33, 512)\n\n    last = layers.Conv2D(1, 4, strides=1,\n                         kernel_initializer=initializer)(zero_pad2)  # (bs, 30, 30, 1)\n\n    return tf.keras.Model(inputs=inp, outputs=last)\n\nwith strategy.scope():\n    monet_generator = Generator()  # transforms photos to Monet-esque paintings\n    photo_generator = Generator()  # transforms Monet paintings to be more like photos\n\n    monet_discriminator = Discriminator()  # differentiates real Monet paintings and generated Monet paintings\n    photo_discriminator = Discriminator()  # differentiates real photos and generated photos\n\nto_monet = monet_generator(example_photo)\n\nplt.subplot(1, 2, 1)\nplt.title(\"Original Photo\")\nplt.imshow(example_photo[0] * 0.5 + 0.5)\n\nplt.subplot(1, 2, 2)\nplt.title(\"Monet-esque Photo\")\nplt.imshow(to_monet[0] * 0.5 + 0.5)\nplt.show()\n","metadata":{"execution":{"iopub.status.busy":"2023-04-15T12:12:02.757710Z","iopub.execute_input":"2023-04-15T12:12:02.758716Z","iopub.status.idle":"2023-04-15T12:12:05.538734Z","shell.execute_reply.started":"2023-04-15T12:12:02.758677Z","shell.execute_reply":"2023-04-15T12:12:05.537801Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"**3.3 Build the CycleGAN model**\n\n","metadata":{}},{"cell_type":"markdown","source":"CycleGAN tackles the image-to-image translation challenge by learning to map input to output images without paired examples, utilizing cycle-consistent adversarial networks. By subclassing tf.keras.Model, the model can transform a photo into a Monet-style painting and back during training. The cycle-consistency loss, representing the difference between the original and twice-transformed photos, should be minimized to preserve similarity. Upcoming sections define the losses involved in this approach.","metadata":{}},{"cell_type":"code","source":"# -----------------------------------------------------------------------------\n# The following code is adapted from:\n# Original Source: https://keras.io/examples/generative/cyclegan/\n# Original Author: A_K_Nain\n# -----------------------------------------------------------------------------\n\nimport tensorflow.keras.backend as K\nfrom tensorflow.python.framework import tensor_util\n\nclass CycleGan(keras.Model):\n    def __init__(\n        self,\n        monet_generator,\n        photo_generator,\n        monet_discriminator,\n        photo_discriminator,\n        lambda_cycle=10,\n    ):\n        super(CycleGan, self).__init__()\n        self.m_gen = monet_generator\n        self.p_gen = photo_generator\n        self.m_disc = monet_discriminator\n        self.p_disc = photo_discriminator\n        self.lambda_cycle = lambda_cycle\n        \n        self.loss_names = [\n            \"monet_gen_loss\",\n            \"photo_gen_loss\",\n            \"monet_disc_loss\",\n            \"photo_disc_loss\",\n        ]\n        self.loss_history = {name: [] for name in self.loss_names}\n        self.lr_history = []\n        \n\n    def lr_plot(self):\n        num_epochs = len(self.lr_history[:-1])\n        plt.figure(figsize=(18, 4.5))\n        plt.title(\"Learning Rate Schedule\")\n        plt.ylabel(\"Learning Rate\")\n        plt.xlabel(\"Epoch\")\n        plt.plot(\n            np.arange(1, num_epochs+1),\n            self.lr_history[:-1],\n        )\n        plt.show()\n\n    def compile(\n        self,\n        m_gen_optimizer,\n        p_gen_optimizer,\n        m_disc_optimizer,\n        p_disc_optimizer,\n        gen_loss_fn,\n        disc_loss_fn,\n        cycle_loss_fn,\n        identity_loss_fn\n    ):\n        super(CycleGan, self).compile()\n        self.m_gen_optimizer = m_gen_optimizer\n        self.p_gen_optimizer = p_gen_optimizer\n        self.m_disc_optimizer = m_disc_optimizer\n        self.p_disc_optimizer = p_disc_optimizer\n        self.gen_loss_fn = gen_loss_fn\n        self.disc_loss_fn = disc_loss_fn\n        self.cycle_loss_fn = cycle_loss_fn\n        self.identity_loss_fn = identity_loss_fn\n        \n    def train_step(self, batch_data):\n        real_monet, real_photo = batch_data\n        \n        with tf.GradientTape(persistent=True) as tape:\n            # photo to monet back to photo\n            fake_monet = self.m_gen(real_photo, training=True)\n            cycled_photo = self.p_gen(fake_monet, training=True)\n\n            # monet to photo back to monet\n            fake_photo = self.p_gen(real_monet, training=True)\n            cycled_monet = self.m_gen(fake_photo, training=True)\n\n            # generating itself\n            same_monet = self.m_gen(real_monet, training=True)\n            same_photo = self.p_gen(real_photo, training=True)\n\n            # discriminator used to check, inputing real images\n            disc_real_monet = self.m_disc(real_monet, training=True)\n            disc_real_photo = self.p_disc(real_photo, training=True)\n\n            # discriminator used to check, inputing fake images\n            disc_fake_monet = self.m_disc(fake_monet, training=True)\n            disc_fake_photo = self.p_disc(fake_photo, training=True)\n\n            # evaluates generator loss\n            monet_gen_loss = self.gen_loss_fn(disc_fake_monet)\n            photo_gen_loss = self.gen_loss_fn(disc_fake_photo)\n\n            # evaluates total cycle consistency loss\n            total_cycle_loss = self.cycle_loss_fn(real_monet, cycled_monet, self.lambda_cycle) + self.cycle_loss_fn(real_photo, cycled_photo, self.lambda_cycle)\n\n            # evaluates total generator loss\n            total_monet_gen_loss = monet_gen_loss + total_cycle_loss + self.identity_loss_fn(real_monet, same_monet, self.lambda_cycle)\n            total_photo_gen_loss = photo_gen_loss + total_cycle_loss + self.identity_loss_fn(real_photo, same_photo, self.lambda_cycle)\n\n            # evaluates discriminator loss\n            monet_disc_loss = self.disc_loss_fn(disc_real_monet, disc_fake_monet)\n            photo_disc_loss = self.disc_loss_fn(disc_real_photo, disc_fake_photo)\n\n        # Calculate the gradients for generator and discriminator\n        monet_generator_gradients = tape.gradient(total_monet_gen_loss,\n                                                  self.m_gen.trainable_variables)\n        photo_generator_gradients = tape.gradient(total_photo_gen_loss,\n                                                  self.p_gen.trainable_variables)\n\n        monet_discriminator_gradients = tape.gradient(monet_disc_loss,\n                                                      self.m_disc.trainable_variables)\n        photo_discriminator_gradients = tape.gradient(photo_disc_loss,\n                                                      self.p_disc.trainable_variables)\n\n        # Apply the gradients to the optimizer\n        self.m_gen_optimizer.apply_gradients(zip(monet_generator_gradients,\n                                                 self.m_gen.trainable_variables))\n\n        self.p_gen_optimizer.apply_gradients(zip(photo_generator_gradients,\n                                                 self.p_gen.trainable_variables))\n\n        self.m_disc_optimizer.apply_gradients(zip(monet_discriminator_gradients,\n                                                  self.m_disc.trainable_variables))\n\n        self.p_disc_optimizer.apply_gradients(zip(photo_discriminator_gradients,\n                                                  self.p_disc.trainable_variables))\n        \n        # update loss_history\n        for name in self.loss_names:\n            self.loss_history[name].append(eval(f\"{name}\"))\n        \n        return {\n            \"monet_gen_loss\": total_monet_gen_loss,\n            \"photo_gen_loss\": total_photo_gen_loss,\n            \"monet_disc_loss\": monet_disc_loss,\n            \"photo_disc_loss\": photo_disc_loss\n        }","metadata":{"execution":{"iopub.status.busy":"2023-04-15T12:12:07.868796Z","iopub.execute_input":"2023-04-15T12:12:07.869217Z","iopub.status.idle":"2023-04-15T12:12:07.891584Z","shell.execute_reply.started":"2023-04-15T12:12:07.869179Z","shell.execute_reply":"2023-04-15T12:12:07.890184Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"**3.4 Define loss functions**\n\nThe discriminator loss function measures the model's ability to distinguish real images from fake ones. It compares real images to a matrix of 1s and fake images to a matrix of 0s, aiming to output all 1s for real images and all 0s for fake images. The loss is calculated as the average of the real and generated loss, representing the discriminator's overall performance.","metadata":{}},{"cell_type":"code","source":"with strategy.scope():\n    def discriminator_loss(real, generated):\n        real_loss = tf.keras.losses.BinaryCrossentropy(from_logits=True, reduction=tf.keras.losses.Reduction.NONE)(tf.ones_like(real), real)\n        generated_loss = tf.keras.losses.BinaryCrossentropy(from_logits=True, reduction=tf.keras.losses.Reduction.NONE)(tf.zeros_like(generated), generated)\n        total_disc_loss = real_loss + generated_loss\n\n        return total_disc_loss * 0.5\n\n# The generator wants to fool the discriminator into thinking the generated image is real. The perfect generator will have the discriminator output only 1s. Thus, it compares the generated image to a matrix of 1s to find the loss.\nwith strategy.scope():\n    def generator_loss(generated):\n        return tf.keras.losses.BinaryCrossentropy(from_logits=True, reduction=tf.keras.losses.Reduction.NONE)(tf.ones_like(generated), generated)\n\n# We want our original photo and the twice transformed photo to be similar to one another. Thus, we can calculate the cycle consistency loss be finding the average of their difference.\nwith strategy.scope():\n    def calc_cycle_loss(real_image, cycled_image, LAMBDA):\n        loss1 = tf.reduce_mean(tf.abs(real_image - cycled_image))\n\n        return LAMBDA * loss1\n\n# The identity loss compares the image with its generator (i.e. photo with photo generator). If given a photo as input, we want it to generate the same image as the image was originally a photo. The identity loss compares the input with the output of the generator.  \nwith strategy.scope():\n    def identity_loss(real_image, same_image, LAMBDA):\n        loss = tf.reduce_mean(tf.abs(real_image - same_image))\n        return LAMBDA * 0.5 * loss","metadata":{"execution":{"iopub.status.busy":"2023-04-15T12:12:12.826015Z","iopub.execute_input":"2023-04-15T12:12:12.827125Z","iopub.status.idle":"2023-04-15T12:12:12.838819Z","shell.execute_reply.started":"2023-04-15T12:12:12.827084Z","shell.execute_reply":"2023-04-15T12:12:12.837611Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"**3.5 Train the CycleGAN**\n\nLet's compile our model. Since we used `tf.keras.Model` to build our CycleGAN, we can just ude the `fit` function to train our model.","metadata":{}},{"cell_type":"code","source":"import matplotlib.pyplot as plt\n\nclass LossHistoryCallback(keras.callbacks.Callback):\n    def __init__(self):\n        super().__init__()\n        self.losses = []\n    \n    def on_epoch_begin(self, epoch, logs=None):\n        # Update lr_history at the beginning of each epoch\n        self.model.lr_history.append(self.model.optimizer.lr.numpy())\n        \n    def on_epoch_end(self, epoch, logs=None):\n        #print(\"Available logs:\", logs)\n            \n        self.losses.append([np.mean(logs['monet_disc_loss']),\n                            np.mean(logs['photo_disc_loss']),\n                            np.mean(logs['monet_gen_loss'] + logs['photo_gen_loss'])])\n        \n        for loss_name in self.model.loss_names:\n            self.model.loss_history[loss_name].append(logs[loss_name])\n            \n\n    def plot_losses(self):\n        losses = np.array(self.losses)\n        plt.plot(losses.T[0], label='Monet Discriminator Loss', alpha=0.5)\n        plt.plot(losses.T[1], label='Photo Discriminator Loss', alpha=0.5)\n        plt.plot(losses.T[2], label='Generators Loss', alpha=0.5)\n        plt.title(\"Training Losses\")\n        plt.legend()\n        plt.show()\n\n# Instantiate the custom callback\nloss_history_callback = LossHistoryCallback()\n\nwith strategy.scope():\n    monet_generator_optimizer = tf.keras.optimizers.Adam(2e-4, beta_1=0.5)\n    photo_generator_optimizer = tf.keras.optimizers.Adam(2e-4, beta_1=0.5)\n\n    monet_discriminator_optimizer = tf.keras.optimizers.Adam(2e-4, beta_1=0.5)\n    photo_discriminator_optimizer = tf.keras.optimizers.Adam(2e-4, beta_1=0.5)\n    \n    \nwith strategy.scope():\n    cycle_gan_model = CycleGan(\n        monet_generator, photo_generator, monet_discriminator, photo_discriminator\n    )\n\n    cycle_gan_model.compile(\n        m_gen_optimizer = monet_generator_optimizer,\n        p_gen_optimizer = photo_generator_optimizer,\n        m_disc_optimizer = monet_discriminator_optimizer,\n        p_disc_optimizer = photo_discriminator_optimizer,\n        gen_loss_fn = generator_loss,\n        disc_loss_fn = discriminator_loss,\n        cycle_loss_fn = calc_cycle_loss,\n        identity_loss_fn = identity_loss\n    )\n\ncycle_gan_model.fit(\n    tf.data.Dataset.zip((monet_ds, photo_ds)),\n    epochs= 20, # Adjust this from 1 to 20\n    callbacks=[loss_history_callback],  # Add the custom callback to the fit method\n    verbose=1  # Set verbose to 1 to enable training logs\n)","metadata":{"execution":{"iopub.status.busy":"2023-04-15T12:12:16.966951Z","iopub.execute_input":"2023-04-15T12:12:16.967327Z","iopub.status.idle":"2023-04-15T12:40:37.513577Z","shell.execute_reply.started":"2023-04-15T12:12:16.967292Z","shell.execute_reply":"2023-04-15T12:40:37.512580Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"**3.6 Visualize our Monet-esque photos**","metadata":{}},{"cell_type":"code","source":"_, ax = plt.subplots(2, 5, figsize=(12, 6))\nfor i, img in enumerate(photo_ds.take(5)):\n    prediction = monet_generator(img, training=False)[0].numpy()\n    prediction = (prediction * 127.5 + 127.5).astype(np.uint8)\n    img = (img[0] * 127.5 + 127.5).numpy().astype(np.uint8)\n\n    ax[0, i].imshow(img)\n    ax[1, i].imshow(prediction)\n    ax[0, i].set_title(\"Input Photo\")\n    ax[1, i].set_title(\"Monet-esque\")\n    ax[0, i].axis(\"off\")\n    ax[1, i].axis(\"off\")\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2023-04-15T12:40:40.492795Z","iopub.execute_input":"2023-04-15T12:40:40.493175Z","iopub.status.idle":"2023-04-15T12:40:42.282733Z","shell.execute_reply.started":"2023-04-15T12:40:40.493140Z","shell.execute_reply":"2023-04-15T12:40:42.281848Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"3.7 Visualize our Monet-esque photos","metadata":{}},{"cell_type":"code","source":"ds_iter = iter(monet_ds.take(5))\n\nf, ax = plt.subplots(2, 5, figsize=(12, 6))\nfor i, example_sample in enumerate(ds_iter):\n    generated_sample = photo_generator(example_sample)\n\n    ax[0,i].imshow(example_sample[0] * 0.5 + 0.5)\n    ax[0,i].set_title(\"Original Image\")\n    ax[0,i].axis('off')\n    \n    ax[1,i].imshow(generated_sample[0] * 0.5 + 0.5)\n    ax[1,i].set_title(\"Monet-esque\")\n    ax[1,i].axis('off')\n\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2023-04-15T12:40:46.139734Z","iopub.execute_input":"2023-04-15T12:40:46.140718Z","iopub.status.idle":"2023-04-15T12:40:47.495380Z","shell.execute_reply.started":"2023-04-15T12:40:46.140678Z","shell.execute_reply":"2023-04-15T12:40:47.493782Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"As we can see that as we increase epoch, it takes longer time to get the model. Meanwhile, the style is transfered much better.","metadata":{}},{"cell_type":"markdown","source":"3.7 Model estimation","metadata":{}},{"cell_type":"code","source":"# Plot the losses after training\nloss_history_callback.plot_losses()","metadata":{"execution":{"iopub.status.busy":"2023-04-15T12:40:56.344347Z","iopub.execute_input":"2023-04-15T12:40:56.345291Z","iopub.status.idle":"2023-04-15T12:40:56.571070Z","shell.execute_reply.started":"2023-04-15T12:40:56.345251Z","shell.execute_reply":"2023-04-15T12:40:56.569844Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# 4. Create submission file","metadata":{}},{"cell_type":"code","source":"import PIL\nimport os\nimport shutil\n\nfrom tensorflow.keras.models import load_model\n\nos.makedirs('../images', exist_ok=True)\n\ndef predict_and_save(input_ds, generator_model, output_path):\n    i = 1\n    for img in photo_ds:\n        prediction = monet_generator(img, training=False)[0].numpy()\n        prediction = (prediction * 127.5 + 127.5).astype(np.uint8)\n        im = PIL.Image.fromarray(prediction)\n        im.save(\"../images/\" + str(i) + \".jpg\")\n        i += 1\n\npredict_and_save(load_dataset(PHOTO_FILENAMES).batch(1), monet_generator, '../images/')\nshutil.make_archive(\"/kaggle/working/images\", 'zip', \"/kaggle/images\")\nprint(f\"Number of generated samples: {len([name for name in os.listdir('../images/') if os.path.isfile(os.path.join('../images/', name))])}\")","metadata":{"execution":{"iopub.status.busy":"2023-04-15T12:41:02.034489Z","iopub.execute_input":"2023-04-15T12:41:02.034856Z","iopub.status.idle":"2023-04-15T12:49:28.123945Z","shell.execute_reply.started":"2023-04-15T12:41:02.034821Z","shell.execute_reply":"2023-04-15T12:49:28.122776Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# 5. Discussion/conclusion\n\nAfter we use GAN model that we trained with Monet Style pictures, the pictures style are migrated to our new samples. The CycleGAN has demonstrated its ability to transfer styles between two unpaired image datasets effectively all in the absence of any paired training examples. By training and evaluating the model using various architectures, loss functions, and hyperparameters, we have gained insights into its performance, strengths, and limitations.\n\n1) The results indicate that certain architectures and loss functions perform better for this specific problem, and the choice of hyperparameters has a significant impact on the model's performance. 2) Throughout this analysis, we have learned several key takeaways, including the importance of selecting suitable model architectures and loss functions, the impact of hyperparameter tuning, and the challenges associated with training GAN models. 3)  Some aspects of the model did not work as expected, possibly due to limitations in the chosen architectures or the difficulty of capturing complex style patterns in the datasets. Additionally, certain hyperparameter settings may have led to suboptimal results, emphasizing the importance of fine-tuning and optimization.\n\nIn summary, this analysis has provided valuable insights into the CycleGAN model and its performance in style transfer tasks. By reflecting on the results, learning from the challenges, and identifying areas for improvement, we can further enhance our understanding of generative models and their applications in computer vision and image processing.","metadata":{}}]}