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gp_dcgan.py
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gp_dcgan.py
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import tensorflow as tf
from tensorgp.engine import *
import matplotlib.pyplot as plt
import matplotlib.ticker as mticker
import numpy as np
import os
import PIL
from heapq import nsmallest, nlargest
from tensorflow.keras import layers
import time
from keras.models import load_model
from skimage import io
from skimage.transform import resize
from skimage.color import rgb2gray
# function sets available
full_set = {'abs', 'add', 'and', 'clip', 'cos', 'div', 'exp', 'frac', 'if', 'len', 'lerp', 'log', 'max', 'mdist',
'min', 'mod', 'mult', 'neg', 'or', 'pow', 'sign', 'sin', 'sqrt', 'sstep', 'sstepp', 'step', 'sub', 'tan',
'warp', 'xor'}
extended_set = {'max', 'min', 'abs', 'add', 'and', 'or', 'mult', 'sub', 'xor', 'neg', 'cos', 'sin', 'tan', 'sqrt',
'div', 'exp', 'log', 'warp'}
simple_set = {'add', 'sub', 'mult', 'div', 'sin', 'tan', 'cos'}
normal_set = {'add', 'mult', 'sub', 'div', 'cos', 'sin', 'tan', 'abs', 'sign', 'pow'}
# custom_set = {'sstep', 'add', 'sub', 'mult', 'div', 'sin', 'tan', 'cos', 'log', 'warp'}
custom_set = {'add', 'cos', 'div', 'if', 'min', 'mult', 'sin', 'sub', 'tan', 'warp'}
#Function set +, −, * , /, min, max, abs, neg, warp, sign, sqrt, pow, mdist, sin, cos, if
std_set = {'add', 'sub', 'mult', 'div', 'sin', 'cos', 'min', 'max', 'abs', 'neg', 'warp', 'sign', 'sqrt', 'pow', 'mdist', 'if'}
cnn_model = load_model('MNIST_keras_CNN.h5')
class dcgan(object):
def __init__(self,
batch_size=32,
gens_per_batch=100,
archive_save = 1,
digits_to_train=None,
run_from_last_pop=True,
linear_gens_per_batch=False,
log_losses=True,
seed=202020212022,
log_digits_class=True,
fset=None,
run_dir=None,
gp_fp=None):
self.seed = seed
tf.random.set_seed(self.seed)
self.img_rows = 28
self.img_cols = 28
self.channels = 1
self.input_shape = [self.img_rows, self.img_cols, self.channels]
self.archive = []
self.archive_save = archive_save
self.log_losses = log_losses
self.log_digits_class = log_digits_class
temp = sys.argv[2] if len(sys.argv) > 2 else ""
pref = datetime.datetime.utcnow().strftime('%Y_%m_%d__%H_%M_%S_%f')[:-3] + "_" + temp
# print(date)
self.run_dir = os.getcwd() + delimiter + "gp_dcgan_results" + delimiter + "run__" + pref + delimiter if run_dir is None else run_dir
self.gp_fp = self.run_dir + "gp" + delimiter if gp_fp is None else gp_fp
self.gan_images = self.run_dir + "dcgan_images" + delimiter
self.run_from_last_pop = run_from_last_pop
self.linear_gens_per_batch = linear_gens_per_batch
# os.makedirs(self.run_dir)
# print("Created dir: ", self.run_dir)
self.batch_size = batch_size
self.gens_per_batch = gens_per_batch
self.last_gen_imgs = []
self.cross_entropy = tf.keras.losses.BinaryCrossentropy(from_logits=True)
self.discriminator = self.make_discriminator_model()
self.disc_optimizer = tf.keras.optimizers.Adam(1e-4)
resolution = [self.img_rows, self.img_cols]
self.fset = normal_set if fset is None else fset
stop_value = self.gens_per_batch - 1 if self.linear_gens_per_batch else 0
self.generator = Engine(fitness_func=self.disc_forward_pass,
population_size=self.batch_size,
tournament_size=2,
mutation_rate=0.3,
crossover_rate=0.8,
max_tree_depth=14,
target_dims=resolution,
# method='grow',
method='ramped half-and-half',
objective='maximizing',
device='/gpu:0',
stop_criteria='generation',
domain_mode='log',
operators=self.fset,
min_init_depth=3,
max_init_depth=6,
terminal_prob=0.5,
min_domain=-1,
max_domain=1,
bloat_control='std',
elitism=1,
stop_value=stop_value,
effective_dims=2,
seed=self.seed,
debug=0,
save_to_file=10000, # save all images from each 10 generations
minimal_print=True,
save_graphics=True,
show_graphics=False,
write_gen_stats=True,
write_log=False,
write_final_pop=True,
stats_file_path=self.gp_fp,
graphics_file_path=self.run_dir,
run_dir_path=self.gp_fp,
read_init_pop_from_file=None,
mutation_funcs=[Engine.subtree_mutation, Engine.point_mutation,
Engine.delete_mutation, Engine.insert_mutation],
mutation_probs=[0.6, 0.2, 0.1, 0.1]
)
# os.makedirs(self.gp_fp)
# print("Created dir: ", self.gp_fp)
os.makedirs(self.gan_images)
# print("Created dir: ", self.gan_images)
self.gloss = 0
self.dloss = 0
self.training_time = 0
self.loss_hist = []
self.digits_to_train = digits_to_train if digits_to_train is not None else [0 for i in range(10)]
(self.x_train, y_train), (_, _) = tf.keras.datasets.mnist.load_data()
train_mask = np.isin(y_train, self.digits_to_train)
self.x_train = self.x_train[train_mask]
self.x_train = self.x_train.reshape(self.x_train.shape[0], self.img_rows, self.img_cols, self.channels).astype(
'float32')
self.x_train = (self.x_train - 127.5) / 127.5 # Normalize the images to [-1, 1]
print("Len of selected dataset: ", len(self.x_train))
self.x_train = tf.data.Dataset.from_tensor_slices(self.x_train).shuffle(len(self.x_train)).batch(self.batch_size)
#print(self.x_train.shape)
def disc_forward_pass(self, **kwargs):
population = kwargs.get('population')
#generation = kwargs.get('generation')
#tensors = kwargs.get('tensors')
_resolution = kwargs.get('resolution')
fit = 0
max_fit = float('-inf')
fitness = []
best_ind = 0
tensors = [p['tensor'] for p in population]
# TODO: is predict okay here?
fit_array = self.discriminator(np.array(np.expand_dims(tensors, axis=3)), training=False)
# scores
for index in range(len(tensors)):
fit = float(fit_array[index][0])
if fit > max_fit:
max_fit = fit
best_ind = index
fitness.append(fit)
population[index]['fitness'] = fit
return population, best_ind
def make_discriminator_model(self):
model = tf.keras.Sequential()
model.add(layers.Conv2D(64, (5, 5), strides=(2, 2), padding='same', input_shape=self.input_shape))
model.add(layers.LeakyReLU())
model.add(layers.Dropout(0.3))
model.add(layers.Conv2D(128, (5, 5), strides=(2, 2), padding='same'))
model.add(layers.LeakyReLU())
model.add(layers.Dropout(0.3))
model.add(layers.Flatten())
model.add(layers.Dense(1))
return model
def compute_losses(self, gen_output, real_output):
gen_loss = self.cross_entropy(tf.zeros_like(gen_output), gen_output)
real_loss = self.cross_entropy(tf.ones_like(real_output), real_output)
self.dloss = gen_loss + real_loss
self.gloss = -self.dloss
self.loss_hist.append([self.dloss.numpy(), self.gloss.numpy()])
def print_training_hist(self):
for h in self.loss_hist:
print(h)
def train_step(self, images, step):
#index = np.random.randint(0, self.x_train.shape[0], self.batch_size)
#images = self.x_train[index]
with tf.GradientTape() as disc_tape:
ep = self.gens_per_batch if self.linear_gens_per_batch else step + 1
_, generated_images = self.generator.run(stop_value=ep, start_from_last_pop=self.run_from_last_pop)
self.archive += [nlargest(self.archive_save, self.generator.population, key=itemgetter('fitness'))]
# tf.debugging.assert_greater_equal(generated_images, -1.0, message="Less than min domain!")
# tf.debugging.assert_less_equal(generated_images, 1.0, message="Grater than max domain!")
self.last_gen_imgs = np.expand_dims(generated_images, axis=3)
classify_digits(self.last_gen_imgs)
#(self.last_gen_imgs.shape)
gen_output = self.discriminator(self.last_gen_imgs, training=True)
real_output = self.discriminator(images, training=True)
self.compute_losses(gen_output, real_output)
gradients_of_discriminator = disc_tape.gradient(self.dloss, self.discriminator.trainable_variables)
self.disc_optimizer.apply_gradients(zip(gradients_of_discriminator, self.discriminator.trainable_variables))
def train(self, epochs = 1):
start = time.time()
for epoch in range(epochs):
step = 0
for images in self.x_train:
self.train_step(images, step)
if self.log_losses: self.write_losses_epochs(step, epoch)
if self.log_digits_class: self.write_digits_classifications(step, epoch)
# for image_batch in self.dataset:
# Save the model every 15 epochs
self.generate_and_save_images(step + 1, epoch + 1)
if (step + 1) % 15 == 0:
pass
step += 1
print('[DCGAN - step {}/{} of epoch {}/{}]:\t[Gloss, Dloss]: [{}, {}]\tTime so far: {} sec'.format(step + 1, len(self.x_train), epoch + 1, epochs, self.gloss,
self.dloss, time.time() - start))
# Generate after the final epoch
self.generate_and_save_images(step + 1, epoch + 1)
self.training_time = time.time() - start
self.plot_losses()
return self.training_time, self.loss_hist
def generate_and_save_images(self, s, e):
self.last_gen_imgs = np.array(self.last_gen_imgs)
self.last_gen_imgs = 0.5 * self.last_gen_imgs + 0.5 # .... [-1, 1] to [0, 1]
fig = plt.figure(figsize=(8, 4))
for i in range(self.last_gen_imgs.shape[0]):
plt.subplot(4, 8, i + 1)
plt.imshow(self.last_gen_imgs[i, :, :, 0] * 127.5 + 127.5, cmap='gray')
plt.axis('off')
plt.savefig(self.gan_images + 'image_at_epoch{:04d}_step{:04d}.png'.format(e, s))
plt.close()
def write_losses_epochs(self, step, epoch):
fn = self.run_dir + "dcgan_losses.txt"
with open(fn, mode='a', newline='') as file:
fwriter = csv.writer(file, delimiter=',')
if epoch == 0 and step == 0:
file.write("[d_loss, g_loss]\n")
fwriter.writerow([self.dloss.numpy(), self.gloss.numpy()])
def write_digits_classifications(self, step, epoch):
fn = self.run_dir + "digit_classifications.txt"
with open(fn, mode='a', newline='') as file:
fwriter = csv.writer(file, delimiter=',')
if epoch == 0 and step == 0:
file.write("[step, epoch, classifications]\n")
#add_row = [step, epoch] + classify_digits(self.last_gen_imgs)
fwriter.writerow([step, epoch] + list(classify_digits(self.last_gen_imgs)))
def plot_losses(self, show_graphics = False):
fig, ax = plt.subplots(1, 1)
ax.plot(range(len(self.loss_hist)), np.asarray(self.loss_hist)[:, 0], linestyle='-', label="D loss")
pylab.legend(loc='upper left')
ax.set_xlabel('Training steps')
ax.set_ylabel('Loss')
ax.get_xaxis().set_major_formatter(mticker.ScalarFormatter())
ax.get_yaxis().set_major_formatter(mticker.ScalarFormatter())
ax.set_title('Discriminator loss across training steps')
fig.set_size_inches(12, 8)
plt.savefig(fname=self.run_dir + 'Losses.svg', format="svg")
if show_graphics: plt.show()
plt.close(fig)
def classify_from_name(imname='test_im.png', invert=True):
x = io.imread(imname)
# compute a bit-wise inversion so black becomes white and vice versa
if invert:
np.invert(x)
x = rgb2gray(x)
# make it the right size
x = resize(x, (28, 28))
# print(x)
# convert to a 4D tensor to feed into our model
x = x.reshape(1, 28, 28, 1)
x = x.astype('float32')
classify_digits(x)
def classify_digits(digits):
out = cnn_model(digits, training=False)
return np.argmax(out, axis=1)
#print(out.shape)
#print("Output:", out)
#print("Argmax: ", np.argmax(out, axis=1))
if __name__ == '__main__':
gen_pop = 32
digits = [1]
if len(sys.argv) > 1:
print("Going for digit: ", sys.argv[1])
digits = [int(sys.argv[1])]
# run_from_last_pop = True
# linear_gens_per_batch = True
gens = [5]
epochs = 5
#fsets = [custom_set, extended_set, normal_set, std_set]
fsets = [normal_set]
#bv = [True, False]
for g in gens:
for cur_set in fsets:
mnist_dcgan = dcgan(batch_size=gen_pop, gens_per_batch=g, fset=cur_set, digits_to_train=digits,
run_from_last_pop=1, linear_gens_per_batch=True,
log_losses=True, log_digits_class=True)
train_time, train_hist = mnist_dcgan.train(epochs = epochs)
print("Elapsed training time (s): ", train_time)
#mnist_dcgan.print_training_hist()
"""
epochs = 100
gen_pop = 32
#run_from_last_pop = True
#linear_gens_per_batch = True
gens = 100
fsets = extended_set
print("\n\nCurrent number of gens: ", gens)
print("Current set: ", str(fsets))
print("CRun from last pop?: ", False)
print("Linear gens per batch?: ", True)
mnist_dcgan = dcgan(batch_size=gen_pop, gens_per_batch=100, fset=fsets,
run_from_last_pop=False, linear_gens_per_batch=True)
train_time, train_hist = mnist_dcgan.train(epochs = epochs)
print("Elapsed training time (s): ", train_time)
mnist_dcgan.print_training_hist()
"""