File size: 8,296 Bytes
b0ae254 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 |
import tensorflow as tf
import matplotlib.pyplot as plt
import numpy as np
import tensorflow as tf
from pathlib import Path
import os
import PIL
from tqdm.auto import tqdm
import argparse
from tensorflow.keras import layers
from datasets import load_dataset
from transformers import DefaultDataCollator
from huggingface_hub import push_to_hub_keras
def parse_args(args=None):
parser = argparse.ArgumentParser()
parser.add_argument("--dataset", type=str, default="mnist", help="Dataset to load from the HuggingFace hub.")
parser.add_argument("--batch_size", type=int, default=128, help="Batch size to use during training")
parser.add_argument("--number_of_examples_to_generate", type=int, default=4, help="Number of examples to be generated in inference mode")
parser.add_argument(
"--generator_hidden_size",
type=int,
default=28,
help="Hidden size of the generator's feature maps.",
)
parser.add_argument("--latent_dim", type=int, default=100, help="Dimensionality of the latent space.")
parser.add_argument(
"--discriminator_hidden_size",
type=int,
default=28,
help="Hidden size of the discriminator's feature maps.",
)
parser.add_argument(
"--image_size",
type=int,
default=28,
help="Spatial size to use when resizing images for training.",
)
parser.add_argument(
"--num_channels",
type=int,
default=3,
help="Number of channels in the training images. For color images this is 3.",
)
parser.add_argument("--num_epochs", type=int, default=5, help="number of epochs of training")
parser.add_argument("--output_dir", type=Path, default=Path("./output"), help="Name of the directory to dump generated images during training.")
parser.add_argument(
"--push_to_hub",
action="store_true",
help="Whether to push the model to the HuggingFace hub after training.",
)
parser.add_argument(
"--model_name",
default=None,
type=str,
help="Name of the model on the hub.",
)
parser.add_argument(
"--organization_name",
default="huggan",
type=str,
help="Organization name to push to, in case args.push_to_hub is specified.",
)
args = parser.parse_args()
if args.push_to_hub:
assert args.output_dir is not None, "Need an `output_dir` to create a repo when `--push_to_hub` is passed."
assert args.model_name is not None, "Need a `model_name` to create a repo when `--push_to_hub` is passed."
if args.output_dir is not None:
os.makedirs(args.output_dir, exist_ok=True)
return args
def stack_generator_layers(model, units):
model.add(layers.Conv2DTranspose(units, (4, 4), strides=2, padding='same', use_bias=False))
model.add(layers.BatchNormalization())
model.add(layers.LeakyReLU())
return model
def create_generator(channel, hidden_size, latent_dim):
generator = tf.keras.Sequential()
generator.add(layers.Input((latent_dim,))) #
generator.add(layers.Dense(hidden_size*4*7*7, use_bias=False, input_shape=(100,)))
generator.add(layers.LeakyReLU())
generator.add(layers.Reshape((7, 7, hidden_size*4)))
units = [hidden_size*2, hidden_size*1]
for unit in units:
generator = stack_generator_layers(generator, unit)
generator.add(layers.Conv2DTranspose(args.num_channels, (4, 4), strides=1, padding='same', use_bias=False, activation='tanh'))
return generator
def stack_discriminator_layers(model, units, use_batch_norm=False, use_dropout=False):
model.add(layers.Conv2D(units, (4, 4), strides=(2, 2), padding='same'))
if use_batch_norm:
model.add(layers.BatchNormalization())
if use_dropout:
model.add(layers.Dropout(0.1))
model.add(layers.LeakyReLU())
return model
def create_discriminator(channel, hidden_size, args):
discriminator = tf.keras.Sequential()
discriminator.add(layers.Input((args.image_size, args.image_size, args.num_channels)))
discriminator = stack_discriminator_layers(discriminator, hidden_size, use_batch_norm = True, use_dropout = True)
discriminator = stack_discriminator_layers(discriminator, hidden_size * 2)
discriminator = stack_discriminator_layers(discriminator,True, hidden_size*4)
discriminator = stack_discriminator_layers(discriminator,True, hidden_size*16)
discriminator.add(layers.Flatten())
discriminator.add(layers.Dense(1))
return discriminator
def discriminator_loss(real_image, generated_image):
real_loss = cross_entropy(tf.ones_like(real_image), real_image)
fake_loss = cross_entropy(tf.zeros_like(generated_image), generated_image)
total_loss = real_loss + fake_loss
return total_loss
@tf.function
def train_step(images):
noise = tf.random.normal([128, 100])
with tf.GradientTape() as gen_tape, tf.GradientTape() as disc_tape:
generated_images = generator(noise, training=True)
real_image = discriminator(images, training=True)
generated_image = discriminator(generated_images, training=True)
# calculate loss inside train step
gen_loss = cross_entropy(tf.ones_like(generated_image), generated_image)
disc_loss = discriminator_loss(real_image, generated_image)
gradients_of_generator = gen_tape.gradient(gen_loss, generator.trainable_variables)
gradients_of_discriminator = disc_tape.gradient(disc_loss, discriminator.trainable_variables)
generator_optimizer.apply_gradients(zip(gradients_of_generator, generator.trainable_variables))
discriminator_optimizer.apply_gradients(zip(gradients_of_discriminator, discriminator.trainable_variables))
def generate_and_save_images(model, epoch, test_input, output_dir, number_of_examples_to_generate):
predictions = model(test_input, training=False)
fig = plt.figure(figsize=(number_of_examples_to_generate*4, number_of_examples_to_generate*16))
for i in range(predictions.shape[0]):
plt.subplot(1, number_of_examples_to_generate, i+1)
if args.num_channels == 1:
plt.imshow(predictions[i, :, :, :], cmap='gray')
else:
plt.imshow(predictions[i, :, :, :])
plt.axis('off')
plt.savefig(f'{output_dir}/image_at_epoch_{epoch}.png')
def train(dataset, epochs, output_dir, args):
for epoch in range(epochs):
print("Epoch:", epoch)
for image_batch in tqdm(dataset):
train_step(image_batch)
generate_and_save_images(generator,
epoch + 1,
seed,
output_dir,
args.number_of_examples_to_generate)
def preprocess(examples):
images = (np.asarray(examples["image"]).astype('float32')- 127.5) / 127.5
images = np.expand_dims(images, -1)
examples["pixel_values"] = images
return examples
def preprocess_images(dataset, args):
data_collator = DefaultDataCollator(return_tensors="tf")
processed_dataset = dataset.map(preprocess)
tf_train_dataset = processed_dataset["train"].to_tf_dataset(
columns=['pixel_values'],
shuffle=True,
batch_size=args.batch_size,
collate_fn=data_collator)
return tf_train_dataset
if __name__ == "__main__":
args = parse_args()
print("Downloading dataset..")
dataset = load_dataset(args.dataset)
dataset= preprocess_images(dataset, args)
print("Training model..")
generator = create_generator(args.num_channels, args.generator_hidden_size, args.latent_dim)
discriminator = create_discriminator(args.num_channels, args.discriminator_hidden_size, args)
generator_optimizer = tf.keras.optimizers.Adam(1e-4)
discriminator_optimizer = tf.keras.optimizers.Adam(1e-4)
# create seed with dimensions of number of examples to generate and noise
seed = tf.random.normal([args.number_of_examples_to_generate, args.latent_dim])
cross_entropy = tf.keras.losses.BinaryCrossentropy(from_logits=True)
train(dataset, args.num_epochs, args.output_dir, args)
if args.push_to_hub is not None:
push_to_hub_keras(generator, repo_path_or_name=f"{args.output_dir}/{args.model_name}",organization=args.organization_name)
|