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from __future__ import print_function, division | |
import numpy as np | |
import pandas as pd | |
import matplotlib.pyplot as plt | |
# Keras modules | |
from keras.layers import Input, LeakyReLU, UpSampling2D, Conv2D, Concatenate | |
from keras_contrib.layers.normalization import InstanceNormalization | |
from keras.models import Model | |
from keras.optimizers import Adam | |
class CycleGAN(): | |
def __init__(self, image_shape, cycle_lambda, image_hepler): | |
self.optimizer = Adam(0.0002, 0.5) | |
self.cycle_lambda = cycle_lambda | |
self.id_lambda = 0.1 * self.cycle_lambda | |
self._image_helper = image_hepler | |
self.img_shape = image_shape | |
# Calculate output shape | |
patch = int(self.img_shape[0] / 2**4) | |
self.disc_patch = (patch, patch, 1) | |
print("Build Discriminators...") | |
self._discriminatorX = self._build_discriminator_model() | |
self._compile_discriminator_model(self._discriminatorX) | |
self._discriminatorY = self._build_discriminator_model() | |
self._compile_discriminator_model(self._discriminatorY) | |
print("Build Generators...") | |
self._generatorXY = self._build_generator_model() | |
self._generatorYX = self._build_generator_model() | |
print("Build GAN...") | |
self._build_and_compile_gan() | |
def train(self, epochs, batch_size, train_data_path): | |
real = np.ones((batch_size,) + self.disc_patch) | |
fake = np.zeros((batch_size,) + self.disc_patch) | |
history = [] | |
for epoch in range(epochs): | |
for i, (imagesX, imagesY) in enumerate(self._image_helper.load_batch_of_train_images(train_data_path, batch_size)): | |
print ("---------------------------------------------------------") | |
print ("******************Epoch {} | Batch {}***************************".format(epoch, i)) | |
print("Generate images...") | |
fakeY = self._generatorXY.predict(imagesX) | |
fakeX = self._generatorYX.predict(imagesY) | |
print("Train Discriminators...") | |
discriminatorX_loss_real = self._discriminatorX.train_on_batch(imagesX, real) | |
discriminatorX_loss_fake = self._discriminatorX.train_on_batch(fakeX, fake) | |
discriminatorX_loss = 0.5 * np.add(discriminatorX_loss_real, discriminatorX_loss_fake) | |
discriminatorY_loss_real = self._discriminatorY.train_on_batch(imagesY, real) | |
discriminatorY_loss_fake = self._discriminatorY.train_on_batch(fakeY, fake) | |
discriminatorY_loss = 0.5 * np.add(discriminatorY_loss_real, discriminatorY_loss_fake) | |
mean_discriminator_loss = 0.5 * np.add(discriminatorX_loss, discriminatorY_loss) | |
print("Train Generators...") | |
generator_loss = self.gan.train_on_batch([imagesX, imagesY], | |
[real, real, | |
imagesX, imagesY, | |
imagesX, imagesY]) | |
print ("Discriminator loss: {}".format(mean_discriminator_loss[0])) | |
print ("Generator loss: {}".format(generator_loss[0])) | |
print ("---------------------------------------------------------") | |
history.append({"D":mean_discriminator_loss[0],"G":generator_loss}) | |
if i%100 ==0: | |
self._save_images("{}_{}".format(epoch, i), train_data_path) | |
self._plot_loss(history) | |
def _encode__layer(self, input_layer, filters): | |
layer = Conv2D(filters, kernel_size=4, strides=2, padding='same')(input_layer) | |
layer = LeakyReLU(alpha=0.2)(layer) | |
layer = InstanceNormalization()(layer) | |
return layer | |
def _decode_transform_layer(self, input_layer, forward_layer, filters): | |
layer = UpSampling2D(size=2)(input_layer) | |
layer = Conv2D(filters, kernel_size=4, strides=1, padding='same', activation='relu')(layer) | |
layer = InstanceNormalization()(layer) | |
layer = Concatenate()([layer, forward_layer]) | |
return layer | |
def _build_generator_model(self): | |
generator_input = Input(shape=self.img_shape) | |
print("Build Encoder...") | |
encode_layer_1 = self._encode__layer(generator_input, 32); | |
encode_layer_2 = self._encode__layer(encode_layer_1, 64); | |
encode_layer_3 = self._encode__layer(encode_layer_2, 128); | |
encode_layer_4 = self._encode__layer(encode_layer_3, 256); | |
print("Build Transformer - Decoder...") | |
decode_transform_layer1 = self._decode_transform_layer(encode_layer_4, encode_layer_3, 128); | |
decode_transform_layer2 = self._decode_transform_layer(decode_transform_layer1, encode_layer_2, 64); | |
decode_transform_layer3 = self._decode_transform_layer(decode_transform_layer2, encode_layer_1, 32); | |
generator_model = UpSampling2D(size = 2)(decode_transform_layer3) | |
generator_model = Conv2D(self.img_shape[2], kernel_size=4, strides=1, padding='same', activation='tanh')(generator_model) | |
final_generator_model = Model(generator_input, generator_model) | |
final_generator_model.summary() | |
return final_generator_model | |
def _build_discriminator_model(self): | |
discriminator_input = Input(shape=self.img_shape) | |
discriminator_model = Conv2D(64, kernel_size=4, strides=2, padding='same')(discriminator_input) | |
discriminator_model = LeakyReLU(alpha=0.2)(discriminator_model) | |
discriminator_model = Conv2D(128, kernel_size=4, strides=2, padding='same')(discriminator_model) | |
discriminator_model = LeakyReLU(alpha=0.2)(discriminator_model) | |
discriminator_model = InstanceNormalization()(discriminator_model) | |
discriminator_model = Conv2D(256, kernel_size=4, strides=2, padding='same')(discriminator_model) | |
discriminator_model = LeakyReLU(alpha=0.2)(discriminator_model) | |
discriminator_model = InstanceNormalization()(discriminator_model) | |
discriminator_model = Conv2D(512, kernel_size=4, strides=2, padding='same')(discriminator_model) | |
discriminator_model = LeakyReLU(alpha=0.2)(discriminator_model) | |
discriminator_model = InstanceNormalization()(discriminator_model) | |
discriminator_model = Conv2D(1, kernel_size=4, strides=1, padding='same')(discriminator_model) | |
return Model(discriminator_input, discriminator_model) | |
def _compile_discriminator_model(self, model): | |
model.compile(loss='binary_crossentropy', | |
optimizer=self.optimizer, | |
metrics=['accuracy']) | |
model.summary() | |
def _build_and_compile_gan(self): | |
imageX = Input(shape=self.img_shape) | |
imageY = Input(shape=self.img_shape) | |
fakeY = self._generatorXY(imageX) | |
fakeX = self._generatorYX(imageY) | |
reconstructedX = self._generatorYX(fakeY) | |
reconstructedY = self._generatorXY(fakeX) | |
imageX_id = self._generatorYX(imageX) | |
imageY_id = self._generatorXY(imageY) | |
self._discriminatorX.trainable = False | |
self._discriminatorY.trainable = False | |
validX = self._discriminatorX(fakeX) | |
validY = self._discriminatorY(fakeY) | |
self.gan = Model(inputs=[imageX, imageY], | |
outputs=[ validX, validY, | |
reconstructedX, reconstructedY, | |
imageX_id, imageY_id ]) | |
self.gan.compile(loss=['mse', 'mse', | |
'mae', 'mae', | |
'mae', 'mae'], | |
loss_weights=[ 1, 1, | |
self.cycle_lambda, self.cycle_lambda, | |
self.id_lambda, self.id_lambda ], | |
optimizer=self.optimizer) | |
self.gan.summary() | |
def _save_images(self, epoch, path): | |
(img_X, img_Y) = self._image_helper.load_testing_image(path) | |
fake_Y = self._generatorXY.predict(img_X) | |
fake_X = self._generatorYX.predict(img_Y) | |
plot_images = np.concatenate([img_X, fake_Y, img_Y, fake_X]) | |
# Rescale | |
plot_images = 0.5 * plot_images + 0.5 | |
self._image_helper.save_image(plot_images, epoch) | |
def _plot_loss(self, history): | |
hist = pd.DataFrame(history) | |
plt.figure(figsize=(20,5)) | |
for colnm in hist.columns: | |
plt.plot(hist[colnm],label=colnm) | |
plt.legend() | |
plt.ylabel("loss") | |
plt.xlabel("epochs") | |
plt.show() |
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Hello..!!
Thanks for awesome code. I have a question regarding value of 'cycle_lambda', how do you decide this loss_weight? I am having a similar issue with my code. I want to make the loss_weight trainable of a multi model discriminator. train_on_batch doesn't allow call back, any fix?