{"cells":[{"metadata":{},"cell_type":"markdown","source":"# Introduction and Setup\n\nThis notebook utilizes a CycleGAN architecture to add Monet-style to photos. For this tutorial, we will be using the TFRecord dataset. Import the following packages and change the accelerator to TPU.\n\nFor more information, check out [TensorFlow](https://www.tensorflow.org/tutorials/generative/cyclegan) and [Keras](https://keras.io/examples/generative/cyclegan/) CycleGAN documentation pages."},{"metadata":{"_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","trusted":true},"cell_type":"code","source":"import tensorflow as tf\nfrom tensorflow import keras\nfrom tensorflow.keras import layers\nimport tensorflow_addons as tfa\n\nfrom kaggle_datasets import KaggleDatasets\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom PIL import Image\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__)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"import os \ninputFolder = '../input/' \nfor root, directories, filenames in os.walk(inputFolder): \n    for filename in filenames: print(os.path.join(root,filename))","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"# Load in the data\n\nWe want to keep our photo dataset and our Monet dataset separate. First, load in the filenames of the TFRecords."},{"metadata":{"trusted":true},"cell_type":"code","source":"GCS_PATH = KaggleDatasets().get_gcs_path()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"MONET_FILENAMES = tf.io.gfile.glob(str(GCS_PATH + '/monet_tfrec/*.tfrec'))\n\nprint('Monet TFRecord Files:', len(MONET_FILENAMES))\n\nPHOTO_FILENAMES = tf.io.gfile.glob(str(GCS_PATH + '/photo_tfrec/*.tfrec'))\nprint('Photo TFRecord Files:', len(PHOTO_FILENAMES))","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"All the images for the competition are already sized to 256x256. As these images are RGB images, set the channel to 3. Additionally, we need to scale the images to a [-1, 1] scale. Because we are building a generative model, we don't need the labels or the image id so we'll only return the image from the TFRecord."},{"metadata":{"trusted":true},"cell_type":"code","source":"IMAGE_SIZE = [256, 256]\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","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"Define the function to extract the image from the files."},{"metadata":{"trusted":true},"cell_type":"code","source":"def 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","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"# Data Augmentation\n\nIn this section, we will try to enhance the diversity of images and paintings."},{"metadata":{"trusted":true},"cell_type":"code","source":"# IMAGE_SIZE = [256, 256]\nImage_height = 256\nImage_width = 256\nChannels_number = 3 \n\ndef Aug_Rotate(image):\n    p_rotate = tf.random.uniform([], 0, 1.0, dtype=tf.float32)\n    p_spatial = tf.random.uniform([], 0, 1.0, dtype=tf.float32)\n    p_crop = tf.random.uniform([], 0, 1.0, dtype=tf.float32)\n    \n    \n    if p_rotate > .8:\n        image = tf.image.rot90(image, k=3) # rotate 270º\n    elif p_rotate > .6:\n        image = tf.image.rot90(image, k=2) # rotate 180º\n    elif p_rotate > .4:\n        image = tf.image.rot90(image, k=1) # rotate 90º\n            \n    return image\n\ndef Aug_Color(image):\n    image = tf.image.random_flip_left_right(image)\n    image = (image + 1) / 2\n    image = tf.image.random_saturation(image, 0.7, 1.2)\n    image = tf.clip_by_value(image, 0, 1) \n    image = (image - 0.5) * 2    \n\n    return image\n\ndef Aug_Crop(image):\n    p_rotate = tf.random.uniform([], 0, 1.0, dtype=tf.float32)\n    p_spatial = tf.random.uniform([], 0, 1.0, dtype=tf.float32)\n    p_crop = tf.random.uniform([], 0, 1.0, dtype=tf.float32)\n    \n    \n    if p_crop > .5:\n        image = tf.image.resize(image, [286, 286])\n        image = tf.image.random_crop(image, size=[Image_height, Image_width, Channels_number])\n        if p_crop > .9:\n            image = tf.image.resize(image, [300, 300])\n            image = tf.image.random_crop(image, size=[Image_height, Image_width, Channels_number])\n            \n    return image\n\ndef Aug_Flip(image):\n    p_spatial = tf.random.uniform([], 0, 1.0, dtype=tf.float32)\n    \n    if p_spatial > .6:\n        image = tf.image.random_flip_left_right(image)\n        image = tf.image.random_flip_up_down(image)\n        if p_spatial > .9:\n            image = tf.image.transpose(image)\n            \n    return image\n\ndef augment_dataset(filenames, Aug = False, repeat = True, \n                    shuffle = False, label = True, batch_size = 1):\n    data_set_ = load_dataset(filenames, labeled = label)\n    \n    if Aug:\n        data_set_ = data_set_.map(Aug_Crop, num_parallel_calls=AUTOTUNE)\n        data_set_ = data_set_.concatenate(load_dataset(filenames))\n        data_set_ = data_set_.map(Aug_Flip, num_parallel_calls=AUTOTUNE)\n        data_set_ = data_set_.concatenate(load_dataset(filenames))\n        data_set_ = data_set_.map(Aug_Color, num_parallel_calls=AUTOTUNE)\n        data_set_ = data_set_.concatenate(load_dataset(filenames))\n        data_set_ = data_set_.map(Aug_Rotate, num_parallel_calls=AUTOTUNE)\n        data_set_ = data_set_.concatenate(load_dataset(filenames))\n    if repeat:\n        data_set_ = data_set_.repeat(count=1)\n    \n    if shuffle:\n        data_set_ = data_set_.shuffle(43)\n    \n    data_set_ = data_set_.batch(batch_size,drop_remainder=True)\n    data_set_ = data_set_.cache()\n    data_set_ = data_set_.prefetch(AUTOTUNE)\n    \n    return data_set_","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"Let's load in our datasets."},{"metadata":{"trusted":true},"cell_type":"code","source":"# monet_ds = load_dataset(MONET_FILENAMES, labeled=True).batch(1)\n# photo_ds = load_dataset(PHOTO_FILENAMES, labeled=True).batch(1)\n# load dataset\nBatch_size = 16\nmonet_ds = augment_dataset(MONET_FILENAMES, Aug = True, shuffle = False, batch_size = Batch_size)\nphoto_ds = augment_dataset(PHOTO_FILENAMES, Aug = True, shuffle = False, batch_size = Batch_size)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"example_monet = next(iter(monet_ds))\nexample_photo = next(iter(photo_ds))","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"Let's  visualize a photo example and a Monet example."},{"metadata":{"trusted":true},"cell_type":"code","source":"plt.subplot(121)\nplt.title('Photo')\nplt.imshow(example_photo[0] * 0.5 + 0.5)\n\nplt.subplot(122)\nplt.title('Monet')\nplt.imshow(example_monet[0] * 0.5 + 0.5)","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"# Build the generator\n\nWe'll be using a UNET architecture for our CycleGAN. To build our generator, let's first define our `downsample` and `upsample` methods.\n\nThe `downsample`, as the name suggests, reduces the 2D dimensions, the width and height, of the image by the stride. The stride is the length of the step the filter takes. Since the stride is 2, the filter is applied to every other pixel, hence reducing the weight and height by 2.\n\nWe'll be using an instance normalization instead of batch normalization. As the instance normalization is not standard in the TensorFlow API, we'll use the layer from TensorFlow Add-ons."},{"metadata":{"trusted":true},"cell_type":"code","source":"OUTPUT_CHANNELS = 3\n\ndef downsample(filters, size, apply_instancenorm=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_instancenorm:\n        result.add(tfa.layers.InstanceNormalization(gamma_initializer=gamma_init))\n\n    result.add(layers.LeakyReLU())\n\n    return result","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"`Upsample` does the opposite of downsample and increases the dimensions of the of the image. `Conv2DTranspose` does basically the opposite of a `Conv2D` layer."},{"metadata":{"trusted":true},"cell_type":"code","source":"def 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\n    if apply_dropout:\n        result.add(layers.Dropout(0.5))\n\n    result.add(layers.ReLU())\n\n    return result","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"Let's build our generator!\n\nThe generator first downsamples the input image and then upsample while establishing long skip connections. Skip connections are a way to help bypass the vanishing gradient problem by concatenating the output of a layer to multiple layers instead of only one. Here we concatenate the output of the downsample layer to the upsample layer in a symmetrical fashion."},{"metadata":{"trusted":true},"cell_type":"code","source":"def 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(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)","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"# 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."},{"metadata":{"trusted":true},"cell_type":"code","source":"def 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\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)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"with 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","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"Since our generators are not trained yet, the generated Monet-esque photo does not show what is expected at this point."},{"metadata":{"trusted":true},"cell_type":"code","source":"to_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()","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"# Build the CycleGAN model\n\nWe will subclass a `tf.keras.Model` so that we can run `fit()` later to train our model. During the training step, the model transforms a photo to a Monet painting and then back to a photo. The difference between the original photo and the twice-transformed photo is the cycle-consistency loss. We want the original photo and the twice-transformed photo to be similar to one another.\n\nThe losses are defined in the next section."},{"metadata":{"trusted":true},"cell_type":"code","source":"class CycleGan(keras.Model):\n    def __init__(\n        self,\n        monet_generator,\n        photo_generator,\n        monet_discriminator,\n        photo_discriminator,\n        lambda_cycle=tf.Variable(15, dtype=tf.float32, trainable=False),\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    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        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        }","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"# Define loss functions\n\nThe discriminator loss function below compares real images to a matrix of 1s and fake images to a matrix of 0s. The perfect discriminator will output all 1s for real images and all 0s for fake images. The discriminator loss outputs the average of the real and generated loss."},{"metadata":{"trusted":true},"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\n        generated_loss = tf.keras.losses.BinaryCrossentropy(from_logits=True, reduction=tf.keras.losses.Reduction.NONE)(tf.zeros_like(generated), generated)\n\n        total_disc_loss = real_loss + generated_loss\n\n        return total_disc_loss * 0.5","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"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."},{"metadata":{"trusted":true},"cell_type":"code","source":"with 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)","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"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."},{"metadata":{"trusted":true},"cell_type":"code","source":"with strategy.scope():\n    def calc_cycle_loss(real_image, cycled_image, LAMBDA):\n        sigma_2 = 625\n        loss1 = tf.reduce_mean(1 - tf.exp(- (real_image - cycled_image) ** 2 / sigma_2)\n\n)\n#         print(np.shape(cycled_image))\n#         tf.print(cycled_image) \n       \n\n        return LAMBDA * loss1","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"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."},{"metadata":{"trusted":true},"cell_type":"code","source":"with 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","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"# 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":{"trusted":true},"cell_type":"code","source":"with 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)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"with 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    )","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"# Changing Lambda per Epoch"},{"metadata":{"trusted":true},"cell_type":"code","source":"class CustomLambdaScheduler(keras.callbacks.Callback):\n    \"\"\"Lambda scheduler which sets the value of lambda according to epoch.\n\n    Arguments:\n      schedule: a function that takes an epoch index\n          (integer, indexed from 0) and current learning rate\n          as inputs and returns a new lambda as output (float32).\n    \"\"\"\n\n    def __init__(self, schedule):\n        super(CustomLambdaScheduler, self).__init__()\n        self.schedule = schedule\n\n    def on_epoch_begin(self, epoch, logs=None):  \n        lc = float(tf.keras.backend.get_value(self.model.lambda_cycle))\n        # Call schedule function to get the scheduled learning rate.\n        scheduled_lc = self.schedule(epoch, lc)\n        # Set the value back to the optimizer before this epoch starts\n        tf.keras.backend.set_value(self.model.lambda_cycle, scheduled_lc)\n        print(\"\\nEpoch %05d: Lambda is %6.4f.\" % (epoch, scheduled_lc))\n\n\nLC_SCHEDULE = [\n    # (epoch to start, lambda) tuples\n    (1, 15),\n    (10, 16),\n    (13, 17),\n    (15, 18),\n    (17, 19),\n]\n\n\ndef lc_schedule(epoch, lc):\n    \"\"\"Helper function to retrieve the scheduled lambda based on epoch.\"\"\"\n    if epoch < LC_SCHEDULE[0][0] or epoch > LC_SCHEDULE[-1][0]:\n        return lc\n    for i in range(len(LC_SCHEDULE)):\n        if epoch == LC_SCHEDULE[i][0]:\n            return LC_SCHEDULE[i][1]\n    return lc\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# import os\n\n# checkpoint_path = 'training_1/cp.ckpt'\n# checkpoint_dir = os.path.dirname(checkpoint_path)\n\n# create checkpoint callback\n# cp_callback = tf.keras.callbacks.ModelCheckpoint(checkpoint_path,\n#                                                 save_weights_only=True,\n#                                                 verbose=1)\n\n# cycle_gan_model.load_weights(checkpoint_path)\n\ncycle_gan_model.fit(\n    tf.data.Dataset.zip((monet_ds, photo_ds)),\n    epochs=18,\n    callbacks = [CustomLambdaScheduler(lc_schedule)]\n#     ,callbacks = [cp_callback] # pass callback to training\n)","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"# Visualize our Monet-esque photos"},{"metadata":{"trusted":true},"cell_type":"code","source":"_, ax = plt.subplots(5, 2, figsize=(12, 12))\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[i, 0].imshow(img)\n    ax[i, 1].imshow(prediction)\n    ax[i, 0].set_title(\"Input Photo\")\n    ax[i, 1].set_title(\"Monet-esque\")\n    ax[i, 0].axis(\"off\")\n    ax[i, 1].axis(\"off\")\nplt.show()","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"# Create submission file"},{"metadata":{"trusted":true},"cell_type":"code","source":"import PIL\n! mkdir ../images","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# add this snippet to come back the main dataset to submit with less RAM \nphoto_ds = load_dataset(PHOTO_FILENAMES, labeled=True).batch(1)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"i = 1\nfor 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","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"import shutil\nshutil.make_archive(\"/kaggle/working/images\", 'zip', \"/kaggle/images\")","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"cell_type":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load\n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# Input data files are available in the read-only \"../input/\" directory\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n    for filename in filenames:\n        print(os.path.join(dirname, filename))\n\n# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session","execution_count":null,"outputs":[]}],"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":4,"nbformat_minor":4}