Source code for simplegan.gan.sagan

from ..layers import (
    SpectralNormalization,
    GenResBlock,
    SelfAttention,
    DiscResBlock,
    DiscOptResBlock,
)
import os
import numpy as np
import cv2
import tensorflow as tf
from tensorflow.keras import layers
from ..datasets.load_cifar10 import load_cifar10_with_labels
from ..datasets.load_custom_data import load_custom_data_with_labels
from ..losses.hinge_loss import hinge_loss_generator, hinge_loss_discriminator
import datetime
from tqdm import tqdm
import logging
import imageio

logging.getLogger("tensorflow").setLevel(logging.ERROR)
# Silence Imageio warnings


def silence_imageio_warning(*args, **kwargs):
    pass


imageio.core.util._precision_warn = silence_imageio_warning

__all__ = ["SAGAN"]


class Generator(tf.keras.Model):
    def __init__(self, n_classes, filters=64):
        super(Generator, self).__init__()
        self.filters = filters
        self.sn_linear = SpectralNormalization(
            tf.keras.layers.Dense(filters * 16 * 4 * 4)
        )
        self.rs = tf.keras.layers.Reshape((4, 4, 16 * filters))
        self.res_block1 = GenResBlock(
            n_classes=n_classes, filters=filters * 16, spectral_norm=True
        )
        self.res_block2 = GenResBlock(
            n_classes=n_classes, filters=filters * 8, spectral_norm=True
        )
        self.res_block3 = GenResBlock(
            n_classes=n_classes, filters=filters * 4, spectral_norm=True
        )
        self.attn = SelfAttention(spectral_norm=True)
        self.res_block4 = GenResBlock(
            n_classes=n_classes, filters=filters * 2, spectral_norm=True
        )
        self.res_block5 = GenResBlock(
            n_classes=n_classes, filters=filters, spectral_norm=True
        )
        self.bn1 = tf.keras.layers.BatchNormalization()
        self.snconv2d1 = SpectralNormalization(
            tf.keras.layers.Conv2D(kernel_size=3, filters=3, strides=1, padding="same")
        )

    def call(self, inp, labels):
        x = self.sn_linear(inp)
        x = tf.reshape(x, (-1, 4, 4, self.filters * 16))
        x = self.res_block1(x, labels=labels)
        x = self.res_block2(x, labels=labels)
        x = self.res_block3(x, labels=labels)
        x = self.attn(x)
        x = self.res_block4(x, labels=labels)
        x = self.res_block5(x, labels=labels)
        x = self.bn1(x)
        x = tf.nn.relu(x)
        x = self.snconv2d1(x)
        x = tf.nn.tanh(x)
        return x


class Discriminator(tf.keras.Model):
    def __init__(self, n_classes, filters=64):
        super(Discriminator, self).__init__()
        self.opt_block1 = DiscOptResBlock(filters=filters, spectral_norm=True)
        self.res_block1 = DiscResBlock(filters=filters * 2, spectral_norm=True)
        self.attn = SelfAttention(spectral_norm=True)
        self.res_block2 = DiscResBlock(filters=filters * 4, spectral_norm=True)
        self.res_block3 = DiscResBlock(filters=filters * 8, spectral_norm=True)
        self.res_block4 = DiscResBlock(filters=filters * 16, spectral_norm=True)
        self.res_block5 = DiscResBlock(
            filters=filters * 16, downsample=False, spectral_norm=True
        )

        self.sn_dense1 = SpectralNormalization(tf.keras.layers.Dense(1))
        self.sn_embedding = tf.keras.layers.Embedding(n_classes, filters * 16)

    def call(self, inp, labels):
        h0 = self.opt_block1(inp)
        h1 = self.res_block1(h0)
        h1 = self.attn(h1)
        h2 = self.res_block2(h1)
        h3 = self.res_block3(h2)
        h4 = self.res_block4(h3)
        h5 = self.res_block5(h4)
        h5 = tf.nn.relu(h5)
        h6 = tf.reduce_sum(h5, [1, 2])
        out = self.sn_dense1(h6)
        h_labels = self.sn_embedding(labels)
        out += tf.reduce_sum(h6 * h_labels, axis=1, keepdims=True)
        return out


[docs]class SAGAN: r"""`Self-Attention GAN <https://arxiv.org/abs/1805.08318>`_ model Args: noise_dim (int, optional): represents the dimension of the prior to sample values. Defaults to ``128`` gen_path (str, optional): path to generator checkpoint to load model weights. Defaults to ``None`` disc_path (str, optional): path to discriminator checkpoint to load model weights. Defaults to ``None`` """ def __init__(self, noise_dim=128, gen_path=None, disc_path=None): self.image_size = None self.noise_dim = noise_dim self.n_classes = None self.gen_model = None self.disc_model = None self.config = locals() def load_data( self, data_dir=None, use_mnist=False, use_cifar10=False, batch_size=32, img_shape=(64, 64), ): if use_cifar10: train_data, train_labels = load_cifar10_with_labels() self.n_classes = 10 else: train_data, train_labels = load_custom_data_with_labels(data_dir, img_shape) self.n_classes = np.unique(train_labels).shape[0] # Resize images tp 128x128 def resize(image, label): image = tf.image.resize(image, [128, 128]) return image, label train_data = (train_data / 255) * 2 - 1 train_ds = tf.data.Dataset.from_tensor_slices((train_data, train_labels)) self.image_size = (128, 128, train_data[0].shape[-1]) train_ds = train_ds.map(resize) train_ds = train_ds.shuffle(10000).batch(batch_size) return train_ds
[docs] def get_sample(self, data=None, n_samples=1, save_dir=None): r"""View sample of the data Args: data (tf.data object): dataset to load samples from n_samples (int, optional): number of samples to load. Defaults to ``1`` save_dir (str, optional): directory to save the sample images. Defaults to ``None`` Return: ``None`` if save_dir is ``not None``, otherwise returns numpy array of samples with shape (n_samples, img_shape) """ assert data is not None, "Data not provided" sample_images = [] data = data.unbatch() for img, label in data.take(n_samples): img = img.numpy() sample_images.append(img) sample_images = np.array(sample_images) if save_dir is None: return sample_images assert os.path.exists(save_dir), "Directory does not exist" for i, sample in enumerate(sample_images): imageio.imwrite(os.path.join(save_dir, "sample_" + str(i) + ".jpg"), sample)
[docs] def generator(self): r"""Generator module for Self-Attention GAN. Use it as a regular TensorFlow 2.0 Keras Model. Return: A tf.keras model """ return Generator(self.n_classes)
[docs] def discriminator(self): r"""Discriminator module for Self-Attention GAN. Use it as a regular TensorFlow 2.0 Keras Model. Return: A tf.keras model """ return Discriminator(self.n_classes)
def __load_model(self): self.gen_model, self.disc_model = ( Generator(self.n_classes), Discriminator(self.n_classes), ) if self.config["gen_path"] is not None: self.gen_model.load_weights(self.config["gen_path"]) print("Generator checkpoint restored") if self.config["disc_path"] is not None: self.disc_model.load_weights(self.config["disc_path"]) print("Discriminator checkpoint restored") @tf.function def train_step(self, images, labels): with tf.GradientTape() as disc_tape: bs = images.shape[0] noise = tf.random.normal([bs, self.noise_dim]) fake_labels = tf.convert_to_tensor(np.random.randint(0, self.n_classes, bs)) generated_images = self.gen_model(noise, labels) real_output = self.disc_model(images, labels, training=True) fake_output = self.disc_model(generated_images, fake_labels, training=True) disc_loss = hinge_loss_discriminator(real_output, fake_output) gradients_of_discriminator = disc_tape.gradient( disc_loss, self.disc_model.trainable_variables ) self.discriminator_optimizer.apply_gradients( zip(gradients_of_discriminator, self.disc_model.trainable_variables) ) with tf.GradientTape() as gen_tape: noise = tf.random.normal([bs, self.noise_dim]) fake_labels = tf.random.uniform((bs,), 0, 10, dtype=tf.int32) generated_images = self.gen_model(noise, fake_labels) fake_output = self.disc_model(generated_images, fake_labels, training=False) gen_loss = hinge_loss_generator(fake_output) gradients_of_generator = gen_tape.gradient( gen_loss, self.gen_model.trainable_variables ) self.generator_optimizer.apply_gradients( zip(gradients_of_generator, self.gen_model.trainable_variables) ) train_stats = { "d_loss": disc_loss, "g_loss": gen_loss, "d_grads": gradients_of_discriminator, "g_grads": gradients_of_generator, } return train_stats
[docs] def fit( self, train_ds=None, epochs=100, gen_optimizer="Adam", disc_optimizer="Adam", verbose=1, gen_learning_rate=1e-4, disc_learning_rate=4e-4, beta_1=0, beta_2=0.9, tensorboard=False, save_model=None, ): r"""Function to train the model Args: train_ds (tf.data object): training data epochs (int, optional): number of epochs to train the model. Defaults to ``100`` gen_optimizer (str, optional): optimizer used to train generator. Defaults to ``Adam`` disc_optimizer (str, optional): optimizer used to train discriminator. Defaults to ``Adam`` verbose (int, optional): 1 - prints training outputs, 0 - no outputs. Defaults to ``1`` gen_learning_rate (float, optional): learning rate of the generator optimizer. Defaults to ``0.0001`` disc_learning_rate (float, optional): learning rate of the discriminator optimizer. Defaults to ``0.0002`` beta_1 (float, optional): decay rate of the first momement. set if ``Adam`` optimizer is used. Defaults to ``0.5`` beta_2 (float, optional): decay rate of the second momement. set if ``Adam`` optimizer is used. Defaults to ``0.5`` tensorboard (bool, optional): if true, writes loss values to ``logs/gradient_tape`` directory which aids visualization. Defaults to ``False`` save_model (str, optional): Directory to save the trained model. Defaults to ``None`` """ self.__load_model() kwargs = {} kwargs["learning_rate"] = gen_learning_rate if gen_optimizer == "Adam": kwargs["beta_1"] = beta_1 self.generator_optimizer = getattr(tf.keras.optimizers, gen_optimizer)(**kwargs) kwargs = {} kwargs["learning_rate"] = disc_learning_rate if disc_optimizer == "Adam": kwargs["beta_1"] = beta_1 self.discriminator_optimizer = getattr(tf.keras.optimizers, disc_optimizer)( **kwargs ) if tensorboard: current_time = datetime.datetime.now().strftime("%Y%m%d-%H%M%S") train_log_dir = "logs/gradient_tape/" + current_time + "/train" train_summary_writer = tf.summary.create_file_writer(train_log_dir) steps = 0 average_generator_loss = tf.keras.metrics.Mean() average_discriminator_loss = tf.keras.metrics.Mean() try: total = tf.data.experimental.cardinality(train_ds).numpy() except BaseException: total = 0 for epoch in range(epochs): average_generator_loss.reset_states() average_discriminator_loss.reset_states() pbar = tqdm(total=total, desc="Epoch - " + str(epoch + 1)) for i, batch in enumerate(train_ds): image_batch, label_batch = batch label_batch = tf.squeeze(label_batch) train_stats = self.train_step(image_batch, label_batch) G_loss = train_stats["g_loss"] D_loss = train_stats["d_loss"] average_generator_loss(G_loss) average_discriminator_loss(D_loss) steps += 1 pbar.update(1) pbar.set_postfix( disc_loss=average_discriminator_loss.result().numpy(), gen_loss=average_generator_loss.result().numpy(), ) if tensorboard: with train_summary_writer.as_default(): tf.summary.scalar("discr_loss", D_loss.numpy(), step=steps) tf.summary.scalar("genr_loss", G_loss.numpy(), step=steps) pbar.close() del pbar if verbose == 1: print( "Epoch:", epoch + 1, "D_loss:", average_generator_loss.result().numpy(), "G_loss", average_discriminator_loss.result().numpy(), ) if save_model is not None: assert isinstance(save_model, str), "Not a valid directory" if save_model[-1] != "/": self.gen_model.save_weights(save_model + "/generator_checkpoint") self.disc_model.save_weights(save_model + "/discriminator_checkpoint") else: self.gen_model.save_weights(save_model + "generator_checkpoint") self.disc_model.save_weights(save_model + "discriminator_checkpoint")
[docs] def generate_samples(self, n_samples=1, labels_list=None, save_dir=None): r"""Generate samples using the trained model Args: n_samples (int, optional): number of samples to generate. Defaults to ``1`` labels_list (int, list): list of labels representing the class of sample to generate save_dir (str, optional): directory to save the generated images. Defaults to ``None`` Return: returns ``None`` if save_dir is ``not None``, otherwise returns a numpy array with generated samples """ assert ( labels_list is not None ), "Enter list of labels to condition the generator" assert ( len(labels_list) == n_samples ), "Number of samples does not match length of labels list" if self.gen_model is None: self.__load_model() Z = np.random.uniform(-1, 1, (n_samples, self.noise_dim)) labels_list = np.array(labels_list) generated_samples = self.gen_model([Z, labels_list]).numpy() if save_dir is None: return generated_samples assert os.path.exists(save_dir), "Directory does not exist" for i, sample in enumerate(generated_samples): imageio.imwrite(os.path.join(save_dir, "sample_" + str(i) + ".jpg"), sample)