State of the Art Neural Networks for Generative Deep Learning

Related tags

Deep Learningpyradox-generative
Overview

pyradox-generative

State of the Art Neural Networks for Generative Deep Learning

Downloads Downloads Downloads

Table of Contents

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)),
)

References

Owner
Ritvik Rastogi
I have been writing code since 2016, and taught myself a handful of skills and programming languages. I love solving problems by writing code
Ritvik Rastogi
GitHub Repository
PICARD - Parsing Incrementally for Constrained Auto-Regressive Decoding from Language Models

This is the official implementation of the following paper: Torsten Scholak, Nathan Schucher, Dzmitry Bahdanau. PICARD - Parsing Incrementally for Con

ElementAI 217 Jan 01, 2023
A flexible submap-based framework towards spatio-temporally consistent volumetric mapping and scene understanding.

Panoptic Mapping This package contains panoptic_mapping, a general framework for semantic volumetric mapping. We provide, among other, a submap-based

ETHZ ASL 194 Dec 20, 2022
Some experiments with tennis player aging curves using Hilbert space GPs in PyMC. Only experimental for now.

NOTE: This is still being developed! Setup notes This document uses Jeff Sackmann's tennis data. You can obtain it as follows: git clone https://githu

Martin Ingram 1 Jan 20, 2022
A best practice for tensorflow project template architecture.

A best practice for tensorflow project template architecture.

Mahmoud Gamal Salem 3.6k Dec 22, 2022
VOS: Learning What You Don’t Know by Virtual Outlier Synthesis

VOS This is the source code accompanying the paper VOS: Learning What You Don’t

248 Dec 25, 2022
DeepHawkeye is a library to detect unusual patterns in images using features from pretrained neural networks

English | 简体中文 Introduction DeepHawkeye is a library to detect unusual patterns in images using features from pretrained neural networks Reference Pat

CV Newbie 28 Dec 13, 2022
This repository is for our paper Exploiting Scene Graphs for Human-Object Interaction Detection accepted by ICCV 2021.

SG2HOI This repository is for our paper Exploiting Scene Graphs for Human-Object Interaction Detection accepted by ICCV 2021. Installation Pytorch 1.7

HT 10 Dec 20, 2022
Code for Two-stage Identifier: "Locate and Label: A Two-stage Identifier for Nested Named Entity Recognition"

Code for Two-stage Identifier: "Locate and Label: A Two-stage Identifier for Nested Named Entity Recognition", accepted at ACL 2021. For details of the model and experiments, please see our paper.

tricktreat 87 Dec 16, 2022
This is a template for the Non-autoregressive Deep Learning-Based TTS model (in PyTorch).

Non-autoregressive Deep Learning-Based TTS Template This is a template for the Non-autoregressive TTS model. It contains Data Preprocessing Pipeline D

Keon Lee 13 Dec 05, 2022
Library for machine learning stacking generalization.

stacked_generalization Implemented machine learning *stacking technic[1]* as handy library in Python. Feature weighted linear stacking is also availab

114 Jul 19, 2022
Multi Agent Reinforcement Learning for ROS in 2D Simulation Environments

IROS21 information To test the code and reproduce the experiments, follow the installation steps in Installation.md. Afterwards, follow the steps in E

11 Oct 29, 2022
Build Graph Nets in Tensorflow

Graph Nets library Graph Nets is DeepMind's library for building graph networks in Tensorflow and Sonnet. Contact DeepMind 5.2k Jan 05, 2023

BackgroundRemover lets you Remove Background from images and video with a simple command line interface

BackgroundRemover BackgroundRemover is a command line tool to remove background from video and image, made by nadermx to power https://BackgroundRemov

Johnathan Nader 1.7k Dec 30, 2022
Multimodal Co-Attention Transformer (MCAT) for Survival Prediction in Gigapixel Whole Slide Images

Multimodal Co-Attention Transformer (MCAT) for Survival Prediction in Gigapixel Whole Slide Images [ICCV 2021] © Mahmood Lab - This code is made avail

Mahmood Lab @ Harvard/BWH 63 Dec 01, 2022
Official implementation of our paper "Learning to Bootstrap for Combating Label Noise"

Learning to Bootstrap for Combating Label Noise This repo is the official implementation of our paper "Learning to Bootstrap for Combating Label Noise

21 Apr 09, 2022
You Only Hypothesize Once: Point Cloud Registration with Rotation-equivariant Descriptors

You Only Hypothesize Once: Point Cloud Registration with Rotation-equivariant Descriptors In this paper, we propose a novel local descriptor-based fra

Haiping Wang 80 Dec 15, 2022
Boosted neural network for tabular data

XBNet - Xtremely Boosted Network Boosted neural network for tabular data XBNet is an open source project which is built with PyTorch which tries to co

Tushar Sarkar 175 Jan 04, 2023
State of the Art Neural Networks for Generative Deep Learning

pyradox-generative State of the Art Neural Networks for Generative Deep Learning Table of Contents pyradox-generative Table of Contents Installation U

Ritvik Rastogi 8 Sep 29, 2022
Official code for "Eigenlanes: Data-Driven Lane Descriptors for Structurally Diverse Lanes", CVPR2022

[CVPR 2022] Eigenlanes: Data-Driven Lane Descriptors for Structurally Diverse Lanes Dongkwon Jin, Wonhui Park, Seong-Gyun Jeong, Heeyeon Kwon, and Cha

Dongkwon Jin 106 Dec 29, 2022
Automatically Build Multiple ML Models with a Single Line of Code. Created by Ram Seshadri. Collaborators Welcome. Permission Granted upon Request.

Auto-ViML Automatically Build Variant Interpretable ML models fast! Auto_ViML is pronounced "auto vimal" (autovimal logo created by Sanket Ghanmare) N

AutoViz and Auto_ViML 397 Dec 30, 2022