pyradox-generative

State of the Art Neural Networks for Generative Deep Learning

pyradox-generative

Installation

pip install pyradox-generative

Usage

This library provides light weight trainers for the following generative models:

Vanilla GAN

Just provide your genrator and discriminator and train your GAN

Data Preparation:

from pyradox_generative import GAN
import numpy as np
import tensorflow as tf
import tensorflow.keras as keras

(x_train, y_train), _ = keras.datasets.mnist.load_data()
x_train = x_train.astype(np.float32) / 255
x_train = x_train.reshape(-1, 28, 28, 1) * 2.0 - 1.0

dataset = tf.data.Dataset.from_tensor_slices(x_train)
dataset = dataset.shuffle(1024)
dataset = dataset.batch(32, drop_remainder=True).prefetch(1)

Define the generator and discriminator models:

generator = keras.models.Sequential(
    [
        keras.Input(shape=[28]),
        keras.layers.Dense(7 * 7 * 3),
        keras.layers.Reshape([7, 7, 3]),
        keras.layers.BatchNormalization(),
        keras.layers.Conv2DTranspose(
            32, kernel_size=3, strides=2, padding="same", activation="selu"
        ),
        keras.layers.Conv2DTranspose(
            1, kernel_size=3, strides=2, padding="same", activation="tanh"
        ),
    ],
    name="generator",
)

discriminator = keras.models.Sequential(
    [
        keras.layers.Conv2D(
            32,
            kernel_size=3,
            strides=2,
            padding="same",
            activation=keras.layers.LeakyReLU(0.2),
            input_shape=[28, 28, 1],
        ),
        keras.layers.Conv2D(
            3,
            kernel_size=3,
            strides=2,
            padding="same",
            activation=keras.layers.LeakyReLU(0.2),
        ),
        keras.layers.Flatten(),
        keras.layers.Dense(1, activation="sigmoid"),
    ],
    name="discriminator",
)

Plug in the models to the trainer class and train them using the very familiar compile and fit methods:

gan = GAN(discriminator=discriminator, generator=generator, latent_dim=28)
gan.compile(
    d_optimizer=keras.optimizers.Adam(learning_rate=0.0001),
    g_optimizer=keras.optimizers.Adam(learning_rate=0.0001),
    loss_fn=keras.losses.BinaryCrossentropy(),
)

history = gan.fit(dataset)

Conditional GAN

Just provide your genrator and discriminator and train your GAN

Data Preparation and calculate the input and output dimensions of generator and discriminator:

from pyradox_generative import ConditionalGAN
import numpy as np
import tensorflow as tf
import tensorflow.keras as keras

CODINGS_SIZE = 28
N_CHANNELS = 1
N_CLASSES = 10
G_INP_CHANNELS = CODINGS_SIZE + N_CLASSES
D_INP_CHANNELS = N_CHANNELS + N_CLASSES

(x_train, y_train), _ = keras.datasets.mnist.load_data()
x_train = x_train
x_train = x_train.astype(np.float32) / 255
x_train = x_train.reshape(-1, 28, 28, 1) * 2.0 - 1.0
y_train = y_train
y_train = keras.utils.to_categorical(y_train, 10)

dataset = tf.data.Dataset.from_tensor_slices((x_train, y_train))
dataset = dataset.shuffle(1024)
dataset = dataset.batch(32, drop_remainder=True).prefetch(1)

Define the generator and discriminator models:

generator = keras.models.Sequential(
    [
        keras.Input(shape=[G_INP_CHANNELS]),
        keras.layers.Dense(7 * 7 * 3),
        keras.layers.Reshape([7, 7, 3]),
        keras.layers.BatchNormalization(),
        keras.layers.Conv2DTranspose(
            32, kernel_size=3, strides=2, padding="same", activation="selu"
        ),
        keras.layers.Conv2DTranspose(
            1, kernel_size=3, strides=2, padding="same", activation="tanh"
        ),
    ],
    name="generator",
)

discriminator = keras.models.Sequential(
    [
        keras.layers.Conv2D(
            32,
            kernel_size=3,
            strides=2,
            padding="same",
            activation=keras.layers.LeakyReLU(0.2),
            input_shape=[28, 28, D_INP_CHANNELS],
        ),
        keras.layers.Conv2D(
            3,
            kernel_size=3,
            strides=2,
            padding="same",
            activation=keras.layers.LeakyReLU(0.2),
        ),
        keras.layers.Flatten(),
        keras.layers.Dense(1, activation="sigmoid"),
    ],
    name="discriminator",
)

Plug in the models to the trainer class and train them using the very familiar compile and fit methods:

gan = ConditionalGAN(
    discriminator=discriminator, generator=generator, latent_dim=CODINGS_SIZE
)
gan.compile(
    d_optimizer=keras.optimizers.Adam(learning_rate=0.0001),
    g_optimizer=keras.optimizers.Adam(learning_rate=0.0001),
    loss_fn=keras.losses.BinaryCrossentropy(),
)

history = gan.fit(dataset)

Wasserstein GAN

Just provide your genrator and discriminator and train your GAN

Data Preparation:

from pyradox_generative import WGANGP
import numpy as np
import tensorflow as tf
import tensorflow.keras as keras

(x_train, y_train), _ = keras.datasets.mnist.load_data()
x_train = x_train.astype(np.float32) / 255
x_train = x_train.reshape(-1, 28, 28, 1) * 2.0 - 1.0

dataset = tf.data.Dataset.from_tensor_slices(x_train)
dataset = dataset.shuffle(1024)
dataset = dataset.batch(32, drop_remainder=True).prefetch(1)

Define the generator and discriminator models:

generator = keras.models.Sequential(
    [
        keras.Input(shape=[28]),
        keras.layers.Dense(7 * 7 * 3),
        keras.layers.Reshape([7, 7, 3]),
        keras.layers.BatchNormalization(),
        keras.layers.Conv2DTranspose(
            32, kernel_size=3, strides=2, padding="same", activation="selu"
        ),
        keras.layers.Conv2DTranspose(
            1, kernel_size=3, strides=2, padding="same", activation="tanh"
        ),
    ],
    name="generator",
)

discriminator = keras.models.Sequential(
    [
        keras.layers.Conv2D(
            32,
            kernel_size=3,
            strides=2,
            padding="same",
            activation=keras.layers.LeakyReLU(0.2),
            input_shape=[28, 28, 1],
        ),
        keras.layers.Conv2D(
            3,
            kernel_size=3,
            strides=2,
            padding="same",
            activation=keras.layers.LeakyReLU(0.2),
        ),
        keras.layers.Flatten(),
        keras.layers.Dense(1, activation="sigmoid"),
    ],
    name="discriminator",
)

Plug in the models to the trainer class and train them using the very familiar compile and fit methods:

gan = WGANGP(
    discriminator=discriminator,
    generator=generator,
    latent_dim=28,
    discriminator_extra_steps=1,
    gp_weight=10,
)
gan.compile(
    d_optimizer=keras.optimizers.Adam(learning_rate=0.0001),
    g_optimizer=keras.optimizers.Adam(learning_rate=0.0001),
)

history = gan.fit(dataset)

Variational Auto Encoder

Just provide your encoder and decoder and train your VAE Sampling is done internally

Data Preparation:

from pyradox_generative import VAE
import numpy as np
import tensorflow as tf
import tensorflow.keras as keras

(x_train, y_train), _ = keras.datasets.mnist.load_data()
x_train = x_train.astype(np.float32) / 255
x_train = x_train.reshape(-1, 28, 28, 1) * 2.0 - 1.0

dataset = tf.data.Dataset.from_tensor_slices(x_train)
dataset = dataset.shuffle(1024)
dataset = dataset.batch(32, drop_remainder=True).prefetch(1)

Define the encoder and decoder models:

encoder = keras.models.Sequential(
    [
        keras.Input(shape=(28, 28, 1)),
        keras.layers.Conv2D(32, 3, activation="relu", strides=2, padding="same"),
        keras.layers.Conv2D(64, 3, activation="relu", strides=2, padding="same"),
        keras.layers.Flatten(),
        keras.layers.Dense(16, activation="relu"),
    ],
    name="encoder",
)

decoder = keras.models.Sequential(
    [
        keras.Input(shape=(28,)),
        keras.layers.Dense(7 * 7 * 64, activation="relu"),
        keras.layers.Reshape((7, 7, 64)),
        keras.layers.Conv2DTranspose(64, 3, activation="relu", strides=2, padding="same"),
        keras.layers.Conv2DTranspose(32, 3, activation="relu", strides=2, padding="same"),
        keras.layers.Conv2DTranspose(1, 3, activation="sigmoid", padding="same"),
    ],
    name="decoder",
)

Plug in the models to the trainer class and train them using the very familiar compile and fit methods:

vae = VAE(encoder=encoder, decoder=decoder, latent_dim=28)
vae.compile(keras.optimizers.Adam(learning_rate=0.001))
history = vae.fit(dataset)

Style GAN

Just provide your genrator and discriminator models and train your GAN

Data Preparation:

from pyradox_generative import StyleGAN
import numpy as np
import tensorflow as tf
from functools import partial

def resize_image(res, image):
    # only donwsampling, so use nearest neighbor that is faster to run
    image = tf.image.resize(
        image, (res, res), method=tf.image.ResizeMethod.NEAREST_NEIGHBOR
    )
    image = tf.cast(image, tf.float32) / 127.5 - 1.0
    return image


def create_dataloader(res):
    (x_train, y_train), _ = tf.keras.datasets.mnist.load_data()
    x_train = x_train[:100, :, :]
    x_train = np.pad(x_train, [(0, 0), (2, 2), (2, 2)], mode="constant")
    x_train = tf.image.grayscale_to_rgb(tf.expand_dims(x_train, axis=3), name=None)
    x_train = tf.data.Dataset.from_tensor_slices(x_train)

    batch_size = 32
    dl = x_train.map(partial(resize_image, res), num_parallel_calls=tf.data.AUTOTUNE)
    dl = dl.shuffle(200).batch(batch_size, drop_remainder=True).prefetch(1).repeat()
    return dl

Define the model by providing number of filters for each each resolution (log 2):

gan = StyleGAN(
    target_res=32,
    start_res=4,
    filter_nums={0: 32, 1: 32, 2: 32, 3: 32, 4: 32, 5: 32},
)
opt_cfg = {"learning_rate": 1e-3, "beta_1": 0.0, "beta_2": 0.99, "epsilon": 1e-8}

start_res_log2 = 2
target_res_log2 = 5

Train the Style GAN:

for res_log2 in range(start_res_log2, target_res_log2 + 1):
    res = 2 ** res_log2
    for phase in ["TRANSITION", "STABLE"]:
        if res == 4 and phase == "TRANSITION":
            continue

        train_dl = create_dataloader(res)

        steps = 10

        gan.compile(
            d_optimizer=tf.keras.optimizers.Adam(**opt_cfg),
            g_optimizer=tf.keras.optimizers.Adam(**opt_cfg),
            loss_weights={"gradient_penalty": 10, "drift": 0.001},
            steps_per_epoch=steps,
            res=res,
            phase=phase,
            run_eagerly=False,
        )

        print(phase)
        history = gan.fit(train_dl, epochs=1, steps_per_epoch=steps)

Cycle GAN

Just provide your genrator and discriminator models and train your GAN

Data Preparation:

import tensorflow_datasets as tfds
import tensorflow as tf
from tensorflow import keras
from pyradox_generative import CycleGAN

tfds.disable_progress_bar()
autotune = tf.data.AUTOTUNE
orig_img_size = (286, 286)
input_img_size = (256, 256, 3)


def normalize_img(img):
    img = tf.cast(img, dtype=tf.float32)
    return (img / 127.5) - 1.0


def preprocess_train_image(img, label):
    img = tf.image.random_flip_left_right(img)
    img = tf.image.resize(img, [*orig_img_size])
    img = tf.image.random_crop(img, size=[*input_img_size])
    img = normalize_img(img)
    return img


def preprocess_test_image(img, label):
    img = tf.image.resize(img, [input_img_size[0], input_img_size[1]])
    img = normalize_img(img)
    return img

train_horses, _ = tfds.load(
    "cycle_gan/horse2zebra", with_info=True, as_supervised=True, split="trainA[:5%]"
)
train_zebras, _ = tfds.load(
    "cycle_gan/horse2zebra", with_info=True, as_supervised=True, split="trainB[:5%]"
)

buffer_size = 256
batch_size = 1

train_horses = (
    train_horses.map(preprocess_train_image, num_parallel_calls=autotune)
    .cache()
    .shuffle(buffer_size)
    .batch(batch_size)
)
train_zebras = (
    train_zebras.map(preprocess_train_image, num_parallel_calls=autotune)
    .cache()
    .shuffle(buffer_size)
    .batch(batch_size)
)

Define the generator and discriminator models:

def build_generator(name):
    return keras.models.Sequential(
        [
            keras.layers.Input(shape=input_img_size),
            keras.layers.Conv2D(32, 3, activation="relu", padding="same"),
            keras.layers.Conv2D(32, 3, activation="relu", padding="same"),
            keras.layers.Conv2D(3, 3, activation="tanh", padding="same"),
        ],
        name=name,
    )


def build_discriminator(name):
    return keras.models.Sequential(
        [
            keras.layers.Input(shape=input_img_size),
            keras.layers.Conv2D(32, 3, activation="relu", padding="same"),
            keras.layers.MaxPooling2D(pool_size=2, strides=2),
            keras.layers.Conv2D(32, 3, activation="relu", padding="same"),
            keras.layers.MaxPooling2D(pool_size=2, strides=2),
            keras.layers.Conv2D(32, 3, activation="relu", padding="same"),
            keras.layers.MaxPooling2D(pool_size=2, strides=2),
            keras.layers.Conv2D(1, 3, activation="relu", padding="same"),
        ],
        name=name,
    )

Plug in the models to the trainer class and train them using the very familiar compile and fit methods:

gan = CycleGAN(
    generator_g=build_generator("gen_G"),
    generator_f=build_generator("gen_F"),
    discriminator_x=build_discriminator("disc_X"),
    discriminator_y=build_discriminator("disc_Y"),
)

gan.compile(
    gen_g_optimizer=keras.optimizers.Adam(learning_rate=2e-4, beta_1=0.5),
    gen_f_optimizer=keras.optimizers.Adam(learning_rate=2e-4, beta_1=0.5),
    disc_x_optimizer=keras.optimizers.Adam(learning_rate=2e-4, beta_1=0.5),
    disc_y_optimizer=keras.optimizers.Adam(learning_rate=2e-4, beta_1=0.5),
)

history = gan.fit(
    tf.data.Dataset.zip((train_horses, train_zebras)),
)

State of the Art Neural Networks for Generative Deep Learning

Related tags

Overview

pyradox-generative

Table of Contents

Installation

Usage

Vanilla GAN

Conditional GAN

Wasserstein GAN

Variational Auto Encoder

Style GAN

Cycle GAN

References

Owner

Ritvik Rastogi

PyTorch code for our ECCV 2020 paper "Single Image Super-Resolution via a Holistic Attention Network"

A tensorflow/keras implementation of StyleGAN to generate images of new Pokemon.

A PyTorch implementation of EfficientDet.

Frequency Domain Image Translation: More Photo-realistic, Better Identity-preserving

Precomputed Real-Time Texture Synthesis with Markovian Generative Adversarial Networks

How to Leverage Multimodal EHR Data for Better Medical Predictions?

Human motion synthesis using Unity3D

Evaluation toolkit of the informative tracking benchmark comprising 9 scenarios, 180 diverse videos, and new challenges.

Codes for paper "Towards Diverse Paragraph Captioning for Untrimmed Videos". CVPR 2021

ICSS - Interactive Continual Semantic Segmentation

An energy estimator for eyeriss-like DNN hardware accelerator

Code for the paper: Adversarial Training Against Location-Optimized Adversarial Patches. ECCV-W 2020.

The open source code of SA-UNet: Spatial Attention U-Net for Retinal Vessel Segmentation.

Transformers are Graph Neural Networks!

converts nominal survey data into a numerical value based on a dictionary lookup.

adversarial_multi_armed_bandit_variable_plays

Pose estimation with MoveNet Lightning

Implementation of the federated dual coordinate descent (FedDCD) method.

Official implementation of Protected Attribute Suppression System, ICCV 2021

Alignment Attention Fusion framework for Few-Shot Object Detection