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

StrongSORT: Make DeepSORT Great Again

A tensorflow implementation of an HMM layer

MagFace: A Universal Representation for Face Recognition and Quality Assessment

PECOS - Prediction for Enormous and Correlated Spaces

Learning with Noisy Labels via Sparse Regularization, ICCV2021

🔥 Real-time Super Resolution enhancement (4x) with content loss and relativistic adversarial optimization 🔥

QAT(quantize aware training) for classification with MQBench

Pytorch Implementation of the paper "Cross-domain Correspondence Learning for Exemplar-based Image Translation"

Serving PyTorch 1.0 Models as a Web Server in C++

Boosted CVaR Classification (NeurIPS 2021)

Level Based Customer Segmentation

TensorFlow (Python API) implementation of Neural Style

Benchmark for the generalization of 3D machine learning models across different remeshing/samplings of a surface.

Beyond imagenet attack (accepted by ICLR 2022) towards crafting adversarial examples for black-box domains.

PyTorch implementation of Algorithm 1 of "On the Anatomy of MCMC-Based Maximum Likelihood Learning of Energy-Based Models"

Code and model benchmarks for "SEVIR : A Storm Event Imagery Dataset for Deep Learning Applications in Radar and Satellite Meteorology"

an implementation of Revisiting Adaptive Convolutions for Video Frame Interpolation using PyTorch

Code for ICCV 2021 paper "HuMoR: 3D Human Motion Model for Robust Pose Estimation"

Codes for CVPR2021 paper "PWCLO-Net: Deep LiDAR Odometry in 3D Point Clouds Using Hierarchical Embedding Mask Optimization"

A repository for generating stylized talking 3D and 3D face