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b30d79e 1e6638d fbc1d53 b30d79e fbc1d53 b30d79e fbc1d53 b30d79e fbc1d53 b30d79e fbc1d53 b30d79e fbc1d53 b30d79e fbc1d53 b30d79e fbc1d53 b30d79e fbc1d53 b30d79e fbc1d53 b30d79e | 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 | from transformers import PretrainedConfig
import tensorflow as tf
from transformers import TFPreTrainedModel
from tensorflow_examples.models.pix2pix import pix2pix
from transformers import TFPreTrainedModel
from transformers import PretrainedConfig
class CycleGANConfig(PretrainedConfig):
model_type = "cyclegan"
def __init__(
self,
output_channels=3,
norm_type='instancenorm',
generator_type='unet',
discriminator_target=False,
lambda = 10,
learning_rate = 2e-4,
beta_1 = 0.5,
epochs = 50,
training_checkpoint = "./rgb2thermal_checkpoints/train",
**kwargs
):
super().__init__(**kwargs)
self.output_channels = output_channels
self.norm_type = norm_type
self.generator_type = generator_type
self.discriminator_target = discriminator_target
class TFCycleGANModel(TFPreTrainedModel):
config_class = CycleGANConfig
def __init__(self, config):
super().__init__(config)
self.generator_g = pix2pix.unet_generator(config.output_channels, norm_type=config.norm_type)
self.generator_f = pix2pix.unet_generator(config.output_channels, norm_type=config.norm_type)
self.discriminator_x = pix2pix.discriminator(norm_type=config.norm_type, target=config.discriminator_target)
self.discriminator_y = pix2pix.discriminator(norm_type=config.norm_type, target=config.discriminator_target)
self.generator_g_optimizer = tf.keras.optimizers.Adam(config.learning_rate, beta_1=config.beta_1)
self.generator_f_optimizer = tf.keras.optimizers.Adam(config.learning_rate, beta_1=config.beta_1)
self.discriminator_x_optimizer = tf.keras.optimizers.Adam(config.learning_rate, beta_1=config.beta_1)
self.discriminator_y_optimizer = tf.keras.optimizers.Adam(config.learning_rate, beta_1=config.beta_1)
self.LAMBDA = config.lambda
self.loss_obj = tf.keras.losses.BinaryCrossentropy(from_logits=True)
self.training_checkpoint = config.training_checkpoint
self.EPOCHS = config.epochs
def call(self, inputs):
return self.generator_g(inputs)
def generate(self, inputs):
return self.generator_g(inputs)
def random_crop(self, image):
cropped_image = tf.image.random_crop(image, size=[IMG_HEIGHT, IMG_WIDTH, 3])
return cropped_image
# normalizing the images to [-1, 1]
def normalize(self, image):
image = tf.cast(image, tf.float32)
image = (image / 127.5) - 1
return image
# Enhance data augmentation
def more_augment(self, image):
#Random Flip Left Right
image = tf.image.random_flip_left_right(image)
#Random brightness
image = tf.image.random_brightness(image, max_delta=0.1)
#random Contrast
image = tf.image.random_contrast(image, lower=0.9, upper=1.1)
return image
def random_jitter(self, image):
# resizing to 286 x 286 x 3
image = tf.image.resize(image, [286, 286], method=tf.image.ResizeMethod.NEAREST_NEIGHBOR)
# randomly cropping to 256 x 256 x 3
image = self.random_crop(image)
# random mirroring
image = tf.image.random_flip_left_right(image)
return image
def preprocess_image_train(self, image, label):
image = self.random_jitter(image)
image = self.more_augment(image)
image = self.normalize(image)
return image
def preprocess_image_test(self, image, label):
image = self.normalize(image)
return image
def discriminator_loss(self, real, generated):
real_loss = self.loss_obj(tf.ones_like(real), real)
generated_loss = self.loss_obj(tf.zeros_like(generated), generated)
total_disc_loss = real_loss + generated_loss
return total_disc_loss * 0.5
def generator_loss(self, generated):
return loss_obj(tf.ones_like(generated), generated)
def calc_cycle_loss(self, real_image, cycled_image):
loss1 = tf.reduce_mean(tf.abs(real_image - cycled_image))
return self.LAMBDA * loss1
def identity_loss(self, real_image, same_image):
loss = tf.reduce_mean(tf.abs(real_image - same_image))
return self.LAMBDA * 0.5 * loss
@tf.function
def train_step(self, real_x, real_y):
# persistent is set to True because the tape is used more than
# once to calculate the gradients.
with tf.GradientTape(persistent=True) as tape:
# Generator G translates X -> Y
# Generator F translates Y -> X.
fake_y = self.generator_g(real_x, training=True)
cycled_x = self.generator_f(fake_y, training=True)
fake_x = self.generator_f(real_y, training=True)
cycled_y = self.generator_g(fake_x, training=True)
# same_x and same_y are used for identity loss.
same_x = self.generator_f(real_x, training=True)
same_y = self.generator_g(real_y, training=True)
disc_real_x = self.discriminator_x(real_x, training=True)
disc_real_y = self.discriminator_y(real_y, training=True)
disc_fake_x = self.discriminator_x(fake_x, training=True)
disc_fake_y = self.discriminator_y(fake_y, training=True)
# calculate the loss
gen_g_loss = self.generator_loss(disc_fake_y)
gen_f_loss = self.generator_loss(disc_fake_x)
total_cycle_loss = self.calc_cycle_loss(real_x, cycled_x) + self.calc_cycle_loss(real_y, cycled_y)
# # Total generator loss = adversarial loss + cycle loss
total_gen_g_loss = gen_g_loss + total_cycle_loss + self.identity_loss(real_y, same_y)
total_gen_f_loss = gen_f_loss + total_cycle_loss + self.identity_loss(real_x, same_x)
# total_gen_g_loss = gen_g_loss + 10 * total_cycle_loss + 5 * self.identity_loss(real_y, same_y)
# total_gen_f_loss = gen_f_loss + 10 * total_cycle_loss + 5 * self.identity_loss(real_x, same_x)
disc_x_loss = self.discriminator_loss(disc_real_x, disc_fake_x)
disc_y_loss = self.discriminator_loss(disc_real_y, disc_fake_y)
# Calculate the gradients for generator and discriminator
generator_g_gradients = tape.gradient(total_gen_g_loss,
self.generator_g.trainable_variables)
generator_f_gradients = tape.gradient(total_gen_f_loss,
self.generator_f.trainable_variables)
discriminator_x_gradients = tape.gradient(disc_x_loss,
self.discriminator_x.trainable_variables)
discriminator_y_gradients = tape.gradient(disc_y_loss,
self.discriminator_y.trainable_variables)
# Apply the gradients to the optimizer
self.generator_g_optimizer.apply_gradients(zip(generator_g_gradients,
self.generator_g.trainable_variables))
self.generator_f_optimizer.apply_gradients(zip(generator_f_gradients,
self.generator_f.trainable_variables))
self.discriminator_x_optimizer.apply_gradients(zip(discriminator_x_gradients,
self.discriminator_x.trainable_variables))
self.discriminator_y_optimizer.apply_gradients(zip(discriminator_y_gradients,
self.discriminator_y.trainable_variables))
class TFCycleGANModel(TFPreTrainedModel):
@classmethod
def from_pretrained(cls, pretrained_model_name_or_path = "./rgb2thermal_checkpoints/train", *model_args, **kwargs):
config = kwargs.pop("config", None)
if not isinstance(config, CycleGANConfig):
config = CycleGANConfig.from_pretrained(pretrained_model_name_or_path, **kwargs)
kwargs["config"] = config
model = super().from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
# Load the checkpoint
checkpoint_path = pretrained_model_name_or_path
checkpoint = tf.train.Checkpoint(generator_g=model.generator_g,
generator_f=model.generator_f,
discriminator_x=model.discriminator_x,
discriminator_y=model.discriminator_y)
checkpoint.restore(tf.train.latest_checkpoint(checkpoint_path)).expect_partial()
print("Model restored from checkpoint")
return model
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