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import os
import numpy as np
from PIL import Image
from datetime import datetime
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import optimizers, Model
from wandb.keras import WandbCallback
from .dce_net import build_dce_net
from .dataloader import UnpairedLowLightDataset
from .losses import (
color_constancy_loss,
exposure_loss,
illumination_smoothness_loss,
SpatialConsistencyLoss,
)
from ..commons import download_lol_dataset, init_wandb
class ZeroDCE(Model):
def __init__(self, experiment_name=None, wandb_api_key=None, **kwargs):
super(ZeroDCE, self).__init__(**kwargs)
self.experiment_name = experiment_name
if wandb_api_key is not None:
init_wandb("zero-dce", experiment_name, wandb_api_key)
self.using_wandb = True
else:
self.using_wandb = False
self.dce_model = build_dce_net()
def compile(self, learning_rate, **kwargs):
super(ZeroDCE, self).compile(**kwargs)
self.optimizer = optimizers.Adam(learning_rate=learning_rate)
self.spatial_constancy_loss = SpatialConsistencyLoss(reduction="none")
def get_enhanced_image(self, data, output):
r1 = output[:, :, :, :3]
r2 = output[:, :, :, 3:6]
r3 = output[:, :, :, 6:9]
r4 = output[:, :, :, 9:12]
r5 = output[:, :, :, 12:15]
r6 = output[:, :, :, 15:18]
r7 = output[:, :, :, 18:21]
r8 = output[:, :, :, 21:24]
x = data + r1 * (tf.square(data) - data)
x = x + r2 * (tf.square(x) - x)
x = x + r3 * (tf.square(x) - x)
enhanced_image = x + r4 * (tf.square(x) - x)
x = enhanced_image + r5 * (tf.square(enhanced_image) - enhanced_image)
x = x + r6 * (tf.square(x) - x)
x = x + r7 * (tf.square(x) - x)
enhanced_image = x + r8 * (tf.square(x) - x)
return enhanced_image
def call(self, data):
dce_net_output = self.dce_model(data)
return self.get_enhanced_image(data, dce_net_output)
def compute_losses(self, data, output):
enhanced_image = self.get_enhanced_image(data, output)
loss_illumination = 200 * illumination_smoothness_loss(output)
loss_spatial_constancy = tf.reduce_mean(
self.spatial_constancy_loss(enhanced_image, data)
)
loss_color_constancy = 5 * tf.reduce_mean(color_constancy_loss(enhanced_image))
loss_exposure = 10 * tf.reduce_mean(exposure_loss(enhanced_image))
total_loss = (
loss_illumination
+ loss_spatial_constancy
+ loss_color_constancy
+ loss_exposure
)
return {
"total_loss": total_loss,
"illumination_smoothness_loss": loss_illumination,
"spatial_constancy_loss": loss_spatial_constancy,
"color_constancy_loss": loss_color_constancy,
"exposure_loss": loss_exposure,
}
def train_step(self, data):
with tf.GradientTape() as tape:
output = self.dce_model(data)
losses = self.compute_losses(data, output)
gradients = tape.gradient(
losses["total_loss"], self.dce_model.trainable_weights
)
self.optimizer.apply_gradients(zip(gradients, self.dce_model.trainable_weights))
return losses
def test_step(self, data):
output = self.dce_model(data)
return self.compute_losses(data, output)
def save_weights(self, filepath, overwrite=True, save_format=None, options=None):
"""While saving the weights, we simply save the weights of the DCE-Net"""
self.dce_model.save_weights(
filepath, overwrite=overwrite, save_format=save_format, options=options
)
def load_weights(self, filepath, by_name=False, skip_mismatch=False, options=None):
"""While loading the weights, we simply load the weights of the DCE-Net"""
self.dce_model.load_weights(
filepath=filepath,
by_name=by_name,
skip_mismatch=skip_mismatch,
options=options,
)
def build_datasets(
self,
image_size: int = 256,
dataset_label: str = "lol",
apply_random_horizontal_flip: bool = True,
apply_random_vertical_flip: bool = True,
apply_random_rotation: bool = True,
val_split: float = 0.2,
batch_size: int = 16,
) -> None:
if dataset_label == "lol":
(self.low_images, _), (self.test_low_images, _) = download_lol_dataset()
data_loader = UnpairedLowLightDataset(
image_size,
apply_random_horizontal_flip,
apply_random_vertical_flip,
apply_random_rotation,
)
self.train_dataset, self.val_dataset = data_loader.get_datasets(
self.low_images, val_split, batch_size
)
def train(self, epochs: int):
log_dir = os.path.join(
self.experiment_name,
"logs",
datetime.now().strftime("%Y%m%d-%H%M%S"),
)
tensorboard_callback = keras.callbacks.TensorBoard(log_dir, histogram_freq=1)
model_checkpoint_callback = keras.callbacks.ModelCheckpoint(
os.path.join(self.experiment_name, "weights.h5"),
save_best_only=True,
save_weights_only=True,
)
callbacks = [
tensorboard_callback,
model_checkpoint_callback
]
if self.using_wandb:
callbacks += [WandbCallback()]
history = self.fit(
self.train_dataset,
validation_data=self.val_dataset,
epochs=epochs,
callbacks=callbacks,
)
return history
def infer(self, original_image):
image = keras.preprocessing.image.img_to_array(original_image)
image = image.astype("float32") / 255.0
image = np.expand_dims(image, axis=0)
output_image = self.call(image)
output_image = tf.cast((output_image[0, :, :, :] * 255), dtype=np.uint8)
output_image = Image.fromarray(output_image.numpy())
return output_image
def infer_from_file(self, original_image_file: str):
original_image = Image.open(original_image_file)
return self.infer(original_image)
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