► 代码示例 / 计算机视觉 / 用于弱光图像增强的 Zero-DCE

用于弱光图像增强的Zero-DCE

作者： Soumik Rakshit
创建日期 2021/09/18
最后修改日期 2023/07/15
描述：实现用于弱光图像增强的零参考深度曲线估计。

ⓘ 此示例使用 Keras 3

简介

零参考深度曲线估计或Zero-DCE将弱光图像增强表述为使用深度神经网络估计图像特定色调曲线的任务。在本例中，我们训练了一个轻量级的深度网络，即DCE-Net，以估计用于给定图像动态范围调整的像素级和高阶色调曲线。

Zero-DCE 以弱光图像作为输入，并生成高阶色调曲线作为输出。然后，这些曲线用于对输入的动态范围进行像素级调整，以获得增强的图像。曲线估计过程以保持增强图像的范围并保留相邻像素的对比度的方式完成。这种曲线估计的灵感来自 Adobe Photoshop 等照片编辑软件中使用的曲线调整，用户可以在其中调整图像色调范围中的各个点。

Zero-DCE 很有吸引力，因为它对参考图像的假设较少：在训练期间，它不需要任何输入/输出图像对。这是通过一组精心制定的非参考损失函数实现的，这些函数隐式地衡量增强质量并指导网络的训练。

参考文献

下载 LOLDataset

LoL 数据集已创建用于弱光图像增强。它提供 485 张用于训练的图像和 15 张用于测试的图像。数据集中每一对图像都包含一个弱光输入图像及其对应的曝光良好的参考图像。

import os

os.environ["KERAS_BACKEND"] = "tensorflow"

import random
import numpy as np
from glob import glob
from PIL import Image, ImageOps
import matplotlib.pyplot as plt

import keras
from keras import layers

import tensorflow as tf

!wget https://huggingface.co/datasets/geekyrakshit/LoL-Dataset/resolve/main/lol_dataset.zip
!unzip -q lol_dataset.zip && rm lol_dataset.zip

--2023-11-20 20:01:50--  https://hugging-face.cn/datasets/geekyrakshit/LoL-Dataset/resolve/main/lol_dataset.zip
Resolving huggingface.co (huggingface.co)... 3.163.189.74, 3.163.189.90, 3.163.189.114, ...
Connecting to huggingface.co (huggingface.co)|3.163.189.74|:443... connected.
HTTP request sent, awaiting response... 302 Found
Location: https://cdn-lfs.huggingface.co/repos/d9/09/d909ef7668bb417b7065a311bd55a3084cc83a1f918e13cb41c5503328432db2/419fddc48958cd0f5599939ee0248852a37ceb8bb738c9b9525e95b25a89de9a?response-content-disposition=attachment%3B+filename*%3DUTF-8%27%27lol_dataset.zip%3B+filename%3D%22lol_dataset.zip%22%3B&response-content-type=application%2Fzip&Expires=1700769710&Policy=eyJTdGF0ZW1lbnQiOlt7IkNvbmRpdGlvbiI6eyJEYXRlTGVzc1RoYW4iOnsiQVdTOkVwb2NoVGltZSI6MTcwMDc2OTcxMH19LCJSZXNvdXJjZSI6Imh0dHBzOi8vY2RuLWxmcy5odWdnaW5nZmFjZS5jby9yZXBvcy9kOS8wOS9kOTA5ZWY3NjY4YmI0MTdiNzA2NWEzMTFiZDU1YTMwODRjYzgzYTFmOTE4ZTEzY2I0MWM1NTAzMzI4NDMyZGIyLzQxOWZkZGM0ODk1OGNkMGY1NTk5OTM5ZWUwMjQ4ODUyYTM3Y2ViOGJiNzM4YzliOTUyNWU5NWIyNWE4OWRlOWE%7EcmVzcG9uc2UtY29udGVudC1kaXNwb3NpdGlvbj0qJnJlc3BvbnNlLWNvbnRlbnQtdHlwZT0qIn1dfQ__&Signature=VPqHlt0h6mUV7D3alDMIO61VSvUX498wZn5rIpo4u5yTYOu2s9CbO82xeGfrZguIuENVO6yiuoUAlZO4XXDsGC0Gc3MR3KIoTGuI9URA815nrdvFE616XBooGAW200KOUmVj2IoySAufi-7ORPuspaVJoKqWr8wytt0hDpNMeaWSg766kVMkJB1Aywq6yu5KHFGkqvOPDWNZZO6yfOtdX2XfbXVuiaiUlS03gRZ58H9pYn535TrE3BYP4W1u%7EehJ4OACpsRsnrsrXDr--PLH5RsxApOR2neFLySta3LiN9mtdjSpOKGn0oUapDfCWG7Ik5OMB5PGGzQBTB5J0b0O9g__&Key-Pair-Id=KVTP0A1DKRTAX [following]
--2023-11-20 20:01:50--  https://cdn-lfs.huggingface.co/repos/d9/09/d909ef7668bb417b7065a311bd55a3084cc83a1f918e13cb41c5503328432db2/419fddc48958cd0f5599939ee0248852a37ceb8bb738c9b9525e95b25a89de9a?response-content-disposition=attachment%3B+filename*%3DUTF-8%27%27lol_dataset.zip%3B+filename%3D%22lol_dataset.zip%22%3B&response-content-type=application%2Fzip&Expires=1700769710&Policy=eyJTdGF0ZW1lbnQiOlt7IkNvbmRpdGlvbiI6eyJEYXRlTGVzc1RoYW4iOnsiQVdTOkVwb2NoVGltZSI6MTcwMDc2OTcxMH19LCJSZXNvdXJjZSI6Imh0dHBzOi8vY2RuLWxmcy5odWdnaW5nZmFjZS5jby9yZXBvcy9kOS8wOS9kOTA5ZWY3NjY4YmI0MTdiNzA2NWEzMTFiZDU1YTMwODRjYzgzYTFmOTE4ZTEzY2I0MWM1NTAzMzI4NDMyZGIyLzQxOWZkZGM0ODk1OGNkMGY1NTk5OTM5ZWUwMjQ4ODUyYTM3Y2ViOGJiNzM4YzliOTUyNWU5NWIyNWE4OWRlOWE%7EcmVzcG9uc2UtY29udGVudC1kaXNwb3NpdGlvbj0qJnJlc3BvbnNlLWNvbnRlbnQtdHlwZT0qIn1dfQ__&Signature=VPqHlt0h6mUV7D3alDMIO61VSvUX498wZn5rIpo4u5yTYOu2s9CbO82xeGfrZguIuENVO6yiuoUAlZO4XXDsGC0Gc3MR3KIoTGuI9URA815nrdvFE616XBooGAW200KOUmVj2IoySAufi-7ORPuspaVJoKqWr8wytt0hDpNMeaWSg766kVMkJB1Aywq6yu5KHFGkqvOPDWNZZO6yfOtdX2XfbXVuiaiUlS03gRZ58H9pYn535TrE3BYP4W1u%7EehJ4OACpsRsnrsrXDr--PLH5RsxApOR2neFLySta3LiN9mtdjSpOKGn0oUapDfCWG7Ik5OMB5PGGzQBTB5J0b0O9g__&Key-Pair-Id=KVTP0A1DKRTAX
Resolving cdn-lfs.huggingface.co (cdn-lfs.huggingface.co)... 108.138.94.122, 108.138.94.25, 108.138.94.14, ...
Connecting to cdn-lfs.huggingface.co (cdn-lfs.huggingface.co)|108.138.94.122|:443... connected.
HTTP request sent, awaiting response... 200 OK
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Saving to: ‘lol_dataset.zip’

lol_dataset.zip     100%[===================>] 331.09M  37.4MB/s    in 9.5s

2023-11-20 20:02:00 (34.9 MB/s) - ‘lol_dataset.zip’ saved [347171015/347171015]

创建 TensorFlow 数据集

我们使用 LoL 数据集训练集中 300 张弱光图像进行训练，并使用其余 185 张弱光图像进行验证。我们将图像调整为 256 x 256 大小，用于训练和验证。请注意，为了训练 DCE-Net，我们不需要相应的增强图像。

IMAGE_SIZE = 256
BATCH_SIZE = 16
MAX_TRAIN_IMAGES = 400


def load_data(image_path):
    image = tf.io.read_file(image_path)
    image = tf.image.decode_png(image, channels=3)
    image = tf.image.resize(images=image, size=[IMAGE_SIZE, IMAGE_SIZE])
    image = image / 255.0
    return image


def data_generator(low_light_images):
    dataset = tf.data.Dataset.from_tensor_slices((low_light_images))
    dataset = dataset.map(load_data, num_parallel_calls=tf.data.AUTOTUNE)
    dataset = dataset.batch(BATCH_SIZE, drop_remainder=True)
    return dataset


train_low_light_images = sorted(glob("./lol_dataset/our485/low/*"))[:MAX_TRAIN_IMAGES]
val_low_light_images = sorted(glob("./lol_dataset/our485/low/*"))[MAX_TRAIN_IMAGES:]
test_low_light_images = sorted(glob("./lol_dataset/eval15/low/*"))


train_dataset = data_generator(train_low_light_images)
val_dataset = data_generator(val_low_light_images)

print("Train Dataset:", train_dataset)
print("Validation Dataset:", val_dataset)

Train Dataset: <_BatchDataset element_spec=TensorSpec(shape=(16, 256, 256, 3), dtype=tf.float32, name=None)>
Validation Dataset: <_BatchDataset element_spec=TensorSpec(shape=(16, 256, 256, 3), dtype=tf.float32, name=None)>

Zero-DCE 框架

DCE-Net 的目标是在给定输入图像的情况下估计一组最佳拟合的光增强曲线（LE 曲线）。然后，该框架通过迭代应用曲线来映射输入 RGB 通道的所有像素，以获得最终增强的图像。

了解光增强曲线

光增强曲线是一种可以自动将弱光图像映射到其增强版本的曲线，其中自适应曲线参数仅依赖于输入图像。在设计这种曲线时，应考虑三个目标

增强图像的每个像素值应在归一化范围 [0,1] 内，以避免溢出截断引起的的信息丢失。
它应该单调，以保持相邻像素之间的对比度。
此曲线的形状应尽可能简单，并且曲线应可微，以允许反向传播。

光增强曲线分别应用于三个 RGB 通道，而不是仅应用于照明通道。三通道调整可以更好地保留固有颜色并降低过度饱和的风险。

DCE-Net

DCE-Net 是一种轻量级的深度神经网络，它学习输入图像与其最佳拟合曲线参数图之间的映射关系。DCE-Net 的输入是低光照图像，输出是一组对应于高阶曲线的像素级曲线参数图。它是一个具有对称级联的七层卷积神经网络（CNN）。每一层包含 32 个大小为 3×3、步长为 1 的卷积核，后面跟着 ReLU 激活函数。最后一层卷积层后面跟着 Tanh 激活函数，它会生成 24 个参数图，用于 8 次迭代，其中每次迭代需要三个通道的三个曲线参数图。

def build_dce_net():
    input_img = keras.Input(shape=[None, None, 3])
    conv1 = layers.Conv2D(
        32, (3, 3), strides=(1, 1), activation="relu", padding="same"
    )(input_img)
    conv2 = layers.Conv2D(
        32, (3, 3), strides=(1, 1), activation="relu", padding="same"
    )(conv1)
    conv3 = layers.Conv2D(
        32, (3, 3), strides=(1, 1), activation="relu", padding="same"
    )(conv2)
    conv4 = layers.Conv2D(
        32, (3, 3), strides=(1, 1), activation="relu", padding="same"
    )(conv3)
    int_con1 = layers.Concatenate(axis=-1)([conv4, conv3])
    conv5 = layers.Conv2D(
        32, (3, 3), strides=(1, 1), activation="relu", padding="same"
    )(int_con1)
    int_con2 = layers.Concatenate(axis=-1)([conv5, conv2])
    conv6 = layers.Conv2D(
        32, (3, 3), strides=(1, 1), activation="relu", padding="same"
    )(int_con2)
    int_con3 = layers.Concatenate(axis=-1)([conv6, conv1])
    x_r = layers.Conv2D(24, (3, 3), strides=(1, 1), activation="tanh", padding="same")(
        int_con3
    )
    return keras.Model(inputs=input_img, outputs=x_r)

损失函数

为了在 DCE-Net 中实现零参考学习，我们使用了一组可微的零参考损失函数，使我们能够评估增强图像的质量。

色彩恒常性损失

色彩恒常性损失用于校正增强图像中潜在的色彩偏差。

def color_constancy_loss(x):
    mean_rgb = tf.reduce_mean(x, axis=(1, 2), keepdims=True)
    mr, mg, mb = (
        mean_rgb[:, :, :, 0],
        mean_rgb[:, :, :, 1],
        mean_rgb[:, :, :, 2],
    )
    d_rg = tf.square(mr - mg)
    d_rb = tf.square(mr - mb)
    d_gb = tf.square(mb - mg)
    return tf.sqrt(tf.square(d_rg) + tf.square(d_rb) + tf.square(d_gb))

曝光损失

为了抑制欠曝光/过曝光区域，我们使用了曝光控制损失。它测量局部区域的平均强度值与预设的良好曝光水平（设置为0.6）之间的距离。

def exposure_loss(x, mean_val=0.6):
    x = tf.reduce_mean(x, axis=3, keepdims=True)
    mean = tf.nn.avg_pool2d(x, ksize=16, strides=16, padding="VALID")
    return tf.reduce_mean(tf.square(mean - mean_val))

光照平滑度损失

为了保持相邻像素之间的单调性关系，将光照平滑度损失添加到每个曲线参数图中。

def illumination_smoothness_loss(x):
    batch_size = tf.shape(x)[0]
    h_x = tf.shape(x)[1]
    w_x = tf.shape(x)[2]
    count_h = (tf.shape(x)[2] - 1) * tf.shape(x)[3]
    count_w = tf.shape(x)[2] * (tf.shape(x)[3] - 1)
    h_tv = tf.reduce_sum(tf.square((x[:, 1:, :, :] - x[:, : h_x - 1, :, :])))
    w_tv = tf.reduce_sum(tf.square((x[:, :, 1:, :] - x[:, :, : w_x - 1, :])))
    batch_size = tf.cast(batch_size, dtype=tf.float32)
    count_h = tf.cast(count_h, dtype=tf.float32)
    count_w = tf.cast(count_w, dtype=tf.float32)
    return 2 * (h_tv / count_h + w_tv / count_w) / batch_size

空间一致性损失

空间一致性损失通过保持输入图像及其增强版本之间相邻区域之间的对比度，鼓励增强图像的空间连贯性。

class SpatialConsistencyLoss(keras.losses.Loss):
    def __init__(self, **kwargs):
        super().__init__(reduction="none")

        self.left_kernel = tf.constant(
            [[[[0, 0, 0]], [[-1, 1, 0]], [[0, 0, 0]]]], dtype=tf.float32
        )
        self.right_kernel = tf.constant(
            [[[[0, 0, 0]], [[0, 1, -1]], [[0, 0, 0]]]], dtype=tf.float32
        )
        self.up_kernel = tf.constant(
            [[[[0, -1, 0]], [[0, 1, 0]], [[0, 0, 0]]]], dtype=tf.float32
        )
        self.down_kernel = tf.constant(
            [[[[0, 0, 0]], [[0, 1, 0]], [[0, -1, 0]]]], dtype=tf.float32
        )

    def call(self, y_true, y_pred):
        original_mean = tf.reduce_mean(y_true, 3, keepdims=True)
        enhanced_mean = tf.reduce_mean(y_pred, 3, keepdims=True)
        original_pool = tf.nn.avg_pool2d(
            original_mean, ksize=4, strides=4, padding="VALID"
        )
        enhanced_pool = tf.nn.avg_pool2d(
            enhanced_mean, ksize=4, strides=4, padding="VALID"
        )

        d_original_left = tf.nn.conv2d(
            original_pool,
            self.left_kernel,
            strides=[1, 1, 1, 1],
            padding="SAME",
        )
        d_original_right = tf.nn.conv2d(
            original_pool,
            self.right_kernel,
            strides=[1, 1, 1, 1],
            padding="SAME",
        )
        d_original_up = tf.nn.conv2d(
            original_pool, self.up_kernel, strides=[1, 1, 1, 1], padding="SAME"
        )
        d_original_down = tf.nn.conv2d(
            original_pool,
            self.down_kernel,
            strides=[1, 1, 1, 1],
            padding="SAME",
        )

        d_enhanced_left = tf.nn.conv2d(
            enhanced_pool,
            self.left_kernel,
            strides=[1, 1, 1, 1],
            padding="SAME",
        )
        d_enhanced_right = tf.nn.conv2d(
            enhanced_pool,
            self.right_kernel,
            strides=[1, 1, 1, 1],
            padding="SAME",
        )
        d_enhanced_up = tf.nn.conv2d(
            enhanced_pool, self.up_kernel, strides=[1, 1, 1, 1], padding="SAME"
        )
        d_enhanced_down = tf.nn.conv2d(
            enhanced_pool,
            self.down_kernel,
            strides=[1, 1, 1, 1],
            padding="SAME",
        )

        d_left = tf.square(d_original_left - d_enhanced_left)
        d_right = tf.square(d_original_right - d_enhanced_right)
        d_up = tf.square(d_original_up - d_enhanced_up)
        d_down = tf.square(d_original_down - d_enhanced_down)
        return d_left + d_right + d_up + d_down

深度曲线估计模型

我们将 Zero-DCE 框架实现为 Keras 子类模型。

class ZeroDCE(keras.Model):
    def __init__(self, **kwargs):
        super().__init__(**kwargs)
        self.dce_model = build_dce_net()

    def compile(self, learning_rate, **kwargs):
        super().compile(**kwargs)
        self.optimizer = keras.optimizers.Adam(learning_rate=learning_rate)
        self.spatial_constancy_loss = SpatialConsistencyLoss(reduction="none")
        self.total_loss_tracker = keras.metrics.Mean(name="total_loss")
        self.illumination_smoothness_loss_tracker = keras.metrics.Mean(
            name="illumination_smoothness_loss"
        )
        self.spatial_constancy_loss_tracker = keras.metrics.Mean(
            name="spatial_constancy_loss"
        )
        self.color_constancy_loss_tracker = keras.metrics.Mean(
            name="color_constancy_loss"
        )
        self.exposure_loss_tracker = keras.metrics.Mean(name="exposure_loss")

    @property
    def metrics(self):
        return [
            self.total_loss_tracker,
            self.illumination_smoothness_loss_tracker,
            self.spatial_constancy_loss_tracker,
            self.color_constancy_loss_tracker,
            self.exposure_loss_tracker,
        ]

    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))

        self.total_loss_tracker.update_state(losses["total_loss"])
        self.illumination_smoothness_loss_tracker.update_state(
            losses["illumination_smoothness_loss"]
        )
        self.spatial_constancy_loss_tracker.update_state(
            losses["spatial_constancy_loss"]
        )
        self.color_constancy_loss_tracker.update_state(losses["color_constancy_loss"])
        self.exposure_loss_tracker.update_state(losses["exposure_loss"])

        return {metric.name: metric.result() for metric in self.metrics}

    def test_step(self, data):
        output = self.dce_model(data)
        losses = self.compute_losses(data, output)

        self.total_loss_tracker.update_state(losses["total_loss"])
        self.illumination_smoothness_loss_tracker.update_state(
            losses["illumination_smoothness_loss"]
        )
        self.spatial_constancy_loss_tracker.update_state(
            losses["spatial_constancy_loss"]
        )
        self.color_constancy_loss_tracker.update_state(losses["color_constancy_loss"])
        self.exposure_loss_tracker.update_state(losses["exposure_loss"])

        return {metric.name: metric.result() for metric in self.metrics}

    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,
        )

训练

zero_dce_model = ZeroDCE()
zero_dce_model.compile(learning_rate=1e-4)
history = zero_dce_model.fit(train_dataset, validation_data=val_dataset, epochs=100)


def plot_result(item):
    plt.plot(history.history[item], label=item)
    plt.plot(history.history["val_" + item], label="val_" + item)
    plt.xlabel("Epochs")
    plt.ylabel(item)
    plt.title("Train and Validation {} Over Epochs".format(item), fontsize=14)
    plt.legend()
    plt.grid()
    plt.show()


plot_result("total_loss")
plot_result("illumination_smoothness_loss")
plot_result("spatial_constancy_loss")
plot_result("color_constancy_loss")
plot_result("exposure_loss")

Epoch 1/100
  2/25 ━[37m━━━━━━━━━━━━━━━━━━━  1s 85ms/step - color_constancy_loss: 0.0013 - exposure_loss: 3.0376 - illumination_smoothness_loss: 2.5211 - spatial_constancy_loss: 4.6834e-07 - total_loss: 5.5601     

WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1700510538.106578 3409375 device_compiler.h:187] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

 25/25 ━━━━━━━━━━━━━━━━━━━━ 16s 123ms/step - color_constancy_loss: 0.0029 - exposure_loss: 2.9968 - illumination_smoothness_loss: 2.1813 - spatial_constancy_loss: 1.8559e-06 - total_loss: 5.1810 - val_color_constancy_loss: 0.0023 - val_exposure_loss: 2.9489 - val_illumination_smoothness_loss: 2.7063 - val_spatial_constancy_loss: 5.0979e-06 - val_total_loss: 5.6575
Epoch 2/100
 25/25 ━━━━━━━━━━━━━━━━━━━━ 2s 64ms/step - color_constancy_loss: 0.0030 - exposure_loss: 2.9854 - illumination_smoothness_loss: 1.2876 - spatial_constancy_loss: 6.1811e-06 - total_loss: 4.2759 - val_color_constancy_loss: 0.0023 - val_exposure_loss: 2.9381 - val_illumination_smoothness_loss: 1.8299 - val_spatial_constancy_loss: 1.3742e-05 - val_total_loss: 4.7703
Epoch 3/100
 25/25 ━━━━━━━━━━━━━━━━━━━━ 2s 64ms/step - color_constancy_loss: 0.0031 - exposure_loss: 2.9746 - illumination_smoothness_loss: 0.8735 - spatial_constancy_loss: 1.6664e-05 - total_loss: 3.8512 - val_color_constancy_loss: 0.0024 - val_exposure_loss: 2.9255 - val_illumination_smoothness_loss: 1.3135 - val_spatial_constancy_loss: 3.1783e-05 - val_total_loss: 4.2414
Epoch 4/100
 25/25 ━━━━━━━━━━━━━━━━━━━━ 2s 64ms/step - color_constancy_loss: 0.0032 - exposure_loss: 2.9623 - illumination_smoothness_loss: 0.6259 - spatial_constancy_loss: 3.7938e-05 - total_loss: 3.5914 - val_color_constancy_loss: 0.0025 - val_exposure_loss: 2.9118 - val_illumination_smoothness_loss: 0.9835 - val_spatial_constancy_loss: 6.1902e-05 - val_total_loss: 3.8979
Epoch 5/100
 25/25 ━━━━━━━━━━━━━━━━━━━━ 2s 63ms/step - color_constancy_loss: 0.0033 - exposure_loss: 2.9493 - illumination_smoothness_loss: 0.4700 - spatial_constancy_loss: 7.2080e-05 - total_loss: 3.4226 - val_color_constancy_loss: 0.0026 - val_exposure_loss: 2.8976 - val_illumination_smoothness_loss: 0.7751 - val_spatial_constancy_loss: 1.0500e-04 - val_total_loss: 3.6754
Epoch 6/100
 25/25 ━━━━━━━━━━━━━━━━━━━━ 2s 64ms/step - color_constancy_loss: 0.0034 - exposure_loss: 2.9358 - illumination_smoothness_loss: 0.3693 - spatial_constancy_loss: 1.1878e-04 - total_loss: 3.3086 - val_color_constancy_loss: 0.0027 - val_exposure_loss: 2.8829 - val_illumination_smoothness_loss: 0.6316 - val_spatial_constancy_loss: 1.6075e-04 - val_total_loss: 3.5173
Epoch 7/100
 25/25 ━━━━━━━━━━━━━━━━━━━━ 2s 65ms/step - color_constancy_loss: 0.0036 - exposure_loss: 2.9219 - illumination_smoothness_loss: 0.2996 - spatial_constancy_loss: 1.7723e-04 - total_loss: 3.2252 - val_color_constancy_loss: 0.0028 - val_exposure_loss: 2.8660 - val_illumination_smoothness_loss: 0.5261 - val_spatial_constancy_loss: 2.3790e-04 - val_total_loss: 3.3951
Epoch 8/100
 25/25 ━━━━━━━━━━━━━━━━━━━━ 2s 64ms/step - color_constancy_loss: 0.0037 - exposure_loss: 2.9056 - illumination_smoothness_loss: 0.2486 - spatial_constancy_loss: 2.5932e-04 - total_loss: 3.1582 - val_color_constancy_loss: 0.0029 - val_exposure_loss: 2.8466 - val_illumination_smoothness_loss: 0.4454 - val_spatial_constancy_loss: 3.4372e-04 - val_total_loss: 3.2952
Epoch 9/100
 25/25 ━━━━━━━━━━━━━━━━━━━━ 2s 63ms/step - color_constancy_loss: 0.0039 - exposure_loss: 2.8872 - illumination_smoothness_loss: 0.2110 - spatial_constancy_loss: 3.6800e-04 - total_loss: 3.1025 - val_color_constancy_loss: 0.0031 - val_exposure_loss: 2.8244 - val_illumination_smoothness_loss: 0.3853 - val_spatial_constancy_loss: 4.8290e-04 - val_total_loss: 3.2132
Epoch 10/100
 25/25 ━━━━━━━━━━━━━━━━━━━━ 2s 63ms/step - color_constancy_loss: 0.0041 - exposure_loss: 2.8665 - illumination_smoothness_loss: 0.1846 - spatial_constancy_loss: 5.0693e-04 - total_loss: 3.0558 - val_color_constancy_loss: 0.0033 - val_exposure_loss: 2.8002 - val_illumination_smoothness_loss: 0.3395 - val_spatial_constancy_loss: 6.5965e-04 - val_total_loss: 3.1436
Epoch 11/100
 25/25 ━━━━━━━━━━━━━━━━━━━━ 2s 64ms/step - color_constancy_loss: 0.0044 - exposure_loss: 2.8440 - illumination_smoothness_loss: 0.1654 - spatial_constancy_loss: 6.8036e-04 - total_loss: 3.0145 - val_color_constancy_loss: 0.0035 - val_exposure_loss: 2.7749 - val_illumination_smoothness_loss: 0.3031 - val_spatial_constancy_loss: 8.6824e-04 - val_total_loss: 3.0824
Epoch 12/100
 25/25 ━━━━━━━━━━━━━━━━━━━━ 2s 64ms/step - color_constancy_loss: 0.0047 - exposure_loss: 2.8198 - illumination_smoothness_loss: 0.1512 - spatial_constancy_loss: 8.9387e-04 - total_loss: 2.9765 - val_color_constancy_loss: 0.0038 - val_exposure_loss: 2.7463 - val_illumination_smoothness_loss: 0.2753 - val_spatial_constancy_loss: 0.0011 - val_total_loss: 3.0265
Epoch 13/100
 25/25 ━━━━━━━━━━━━━━━━━━━━ 2s 64ms/step - color_constancy_loss: 0.0050 - exposure_loss: 2.7928 - illumination_smoothness_loss: 0.1408 - spatial_constancy_loss: 0.0012 - total_loss: 2.9398 - val_color_constancy_loss: 0.0041 - val_exposure_loss: 2.7132 - val_illumination_smoothness_loss: 0.2537 - val_spatial_constancy_loss: 0.0015 - val_total_loss: 2.9724
Epoch 14/100
 25/25 ━━━━━━━━━━━━━━━━━━━━ 2s 64ms/step - color_constancy_loss: 0.0054 - exposure_loss: 2.7600 - illumination_smoothness_loss: 0.1340 - spatial_constancy_loss: 0.0016 - total_loss: 2.9009 - val_color_constancy_loss: 0.0045 - val_exposure_loss: 2.6673 - val_illumination_smoothness_loss: 0.2389 - val_spatial_constancy_loss: 0.0021 - val_total_loss: 2.9129
Epoch 15/100
 25/25 ━━━━━━━━━━━━━━━━━━━━ 2s 63ms/step - color_constancy_loss: 0.0060 - exposure_loss: 2.7115 - illumination_smoothness_loss: 0.1314 - spatial_constancy_loss: 0.0022 - total_loss: 2.8512 - val_color_constancy_loss: 0.0055 - val_exposure_loss: 2.5820 - val_illumination_smoothness_loss: 0.2374 - val_spatial_constancy_loss: 0.0035 - val_total_loss: 2.8284
Epoch 16/100
 25/25 ━━━━━━━━━━━━━━━━━━━━ 2s 64ms/step - color_constancy_loss: 0.0075 - exposure_loss: 2.6129 - illumination_smoothness_loss: 0.1414 - spatial_constancy_loss: 0.0041 - total_loss: 2.7660 - val_color_constancy_loss: 0.0081 - val_exposure_loss: 2.3797 - val_illumination_smoothness_loss: 0.2453 - val_spatial_constancy_loss: 0.0083 - val_total_loss: 2.6414
Epoch 17/100
 25/25 ━━━━━━━━━━━━━━━━━━━━ 2s 63ms/step - color_constancy_loss: 0.0128 - exposure_loss: 2.3149 - illumination_smoothness_loss: 0.1766 - spatial_constancy_loss: 0.0148 - total_loss: 2.5190 - val_color_constancy_loss: 0.0286 - val_exposure_loss: 1.5060 - val_illumination_smoothness_loss: 0.3288 - val_spatial_constancy_loss: 0.0648 - val_total_loss: 1.9282
Epoch 18/100
 25/25 ━━━━━━━━━━━━━━━━━━━━ 2s 63ms/step - color_constancy_loss: 0.0505 - exposure_loss: 1.3386 - illumination_smoothness_loss: 0.2606 - spatial_constancy_loss: 0.1196 - total_loss: 1.7693 - val_color_constancy_loss: 0.0827 - val_exposure_loss: 0.6645 - val_illumination_smoothness_loss: 0.2964 - val_spatial_constancy_loss: 0.2687 - val_total_loss: 1.3123
Epoch 19/100
 25/25 ━━━━━━━━━━━━━━━━━━━━ 2s 63ms/step - color_constancy_loss: 0.0873 - exposure_loss: 0.8174 - illumination_smoothness_loss: 0.2378 - spatial_constancy_loss: 0.2577 - total_loss: 1.4002 - val_color_constancy_loss: 0.0861 - val_exposure_loss: 0.6856 - val_illumination_smoothness_loss: 0.2464 - val_spatial_constancy_loss: 0.2539 - val_total_loss: 1.2719
Epoch 20/100
 25/25 ━━━━━━━━━━━━━━━━━━━━ 2s 64ms/step - color_constancy_loss: 0.0753 - exposure_loss: 0.8584 - illumination_smoothness_loss: 0.1858 - spatial_constancy_loss: 0.2394 - total_loss: 1.3589 - val_color_constancy_loss: 0.0882 - val_exposure_loss: 0.6714 - val_illumination_smoothness_loss: 0.2195 - val_spatial_constancy_loss: 0.2620 - val_total_loss: 1.2410
Epoch 21/100
 25/25 ━━━━━━━━━━━━━━━━━━━━ 2s 63ms/step - color_constancy_loss: 0.0779 - exposure_loss: 0.8382 - illumination_smoothness_loss: 0.1706 - spatial_constancy_loss: 0.2486 - total_loss: 1.3354 - val_color_constancy_loss: 0.0886 - val_exposure_loss: 0.6648 - val_illumination_smoothness_loss: 0.2072 - val_spatial_constancy_loss: 0.2643 - val_total_loss: 1.2249
Epoch 22/100
 25/25 ━━━━━━━━━━━━━━━━━━━━ 2s 64ms/step - color_constancy_loss: 0.0784 - exposure_loss: 0.8337 - illumination_smoothness_loss: 0.1590 - spatial_constancy_loss: 0.2502 - total_loss: 1.3212 - val_color_constancy_loss: 0.0889 - val_exposure_loss: 0.6647 - val_illumination_smoothness_loss: 0.1934 - val_spatial_constancy_loss: 0.2653 - val_total_loss: 1.2122
Epoch 23/100
 25/25 ━━━━━━━━━━━━━━━━━━━━ 2s 63ms/step - color_constancy_loss: 0.0783 - exposure_loss: 0.8329 - illumination_smoothness_loss: 0.1498 - spatial_constancy_loss: 0.2508 - total_loss: 1.3118 - val_color_constancy_loss: 0.0897 - val_exposure_loss: 0.6602 - val_illumination_smoothness_loss: 0.1834 - val_spatial_constancy_loss: 0.2671 - val_total_loss: 1.2003
Epoch 24/100
 25/25 ━━━━━━━━━━━━━━━━━━━━ 2s 63ms/step - color_constancy_loss: 0.0787 - exposure_loss: 0.8283 - illumination_smoothness_loss: 0.1426 - spatial_constancy_loss: 0.2529 - total_loss: 1.3025 - val_color_constancy_loss: 0.0897 - val_exposure_loss: 0.6601 - val_illumination_smoothness_loss: 0.1754 - val_spatial_constancy_loss: 0.2671 - val_total_loss: 1.1923
Epoch 25/100
 25/25 ━━━━━━━━━━━━━━━━━━━━ 2s 64ms/step - color_constancy_loss: 0.0785 - exposure_loss: 0.8294 - illumination_smoothness_loss: 0.1365 - spatial_constancy_loss: 0.2524 - total_loss: 1.2968 - val_color_constancy_loss: 0.0902 - val_exposure_loss: 0.6562 - val_illumination_smoothness_loss: 0.1672 - val_spatial_constancy_loss: 0.2692 - val_total_loss: 1.1828
Epoch 26/100
 25/25 ━━━━━━━━━━━━━━━━━━━━ 2s 64ms/step - color_constancy_loss: 0.0793 - exposure_loss: 0.8229 - illumination_smoothness_loss: 0.1316 - spatial_constancy_loss: 0.2554 - total_loss: 1.2892 - val_color_constancy_loss: 0.0896 - val_exposure_loss: 0.6567 - val_illumination_smoothness_loss: 0.1606 - val_spatial_constancy_loss: 0.2699 - val_total_loss: 1.1768
Epoch 27/100
 25/25 ━━━━━━━━━━━━━━━━━━━━ 2s 65ms/step - color_constancy_loss: 0.0788 - exposure_loss: 0.8285 - illumination_smoothness_loss: 0.1238 - spatial_constancy_loss: 0.2534 - total_loss: 1.2845 - val_color_constancy_loss: 0.0906 - val_exposure_loss: 0.6519 - val_illumination_smoothness_loss: 0.1574 - val_spatial_constancy_loss: 0.2725 - val_total_loss: 1.1724
Epoch 28/100
 25/25 ━━━━━━━━━━━━━━━━━━━━ 2s 64ms/step - color_constancy_loss: 0.0794 - exposure_loss: 0.8247 - illumination_smoothness_loss: 0.1194 - spatial_constancy_loss: 0.2550 - total_loss: 1.2785 - val_color_constancy_loss: 0.0914 - val_exposure_loss: 0.6451 - val_illumination_smoothness_loss: 0.1542 - val_spatial_constancy_loss: 0.2783 - val_total_loss: 1.1689
Epoch 29/100
 25/25 ━━━━━━━━━━━━━━━━━━━━ 2s 63ms/step - color_constancy_loss: 0.0797 - exposure_loss: 0.8203 - illumination_smoothness_loss: 0.1139 - spatial_constancy_loss: 0.2577 - total_loss: 1.2715 - val_color_constancy_loss: 0.0914 - val_exposure_loss: 0.6468 - val_illumination_smoothness_loss: 0.1435 - val_spatial_constancy_loss: 0.2775 - val_total_loss: 1.1592
Epoch 30/100
 25/25 ━━━━━━━━━━━━━━━━━━━━ 2s 64ms/step - color_constancy_loss: 0.0795 - exposure_loss: 0.8199 - illumination_smoothness_loss: 0.1083 - spatial_constancy_loss: 0.2581 - total_loss: 1.2659 - val_color_constancy_loss: 0.0911 - val_exposure_loss: 0.6483 - val_illumination_smoothness_loss: 0.1336 - val_spatial_constancy_loss: 0.2768 - val_total_loss: 1.1498
Epoch 31/100
 25/25 ━━━━━━━━━━━━━━━━━━━━ 2s 64ms/step - color_constancy_loss: 0.0797 - exposure_loss: 0.8194 - illumination_smoothness_loss: 0.1037 - spatial_constancy_loss: 0.2589 - total_loss: 1.2617 - val_color_constancy_loss: 0.0912 - val_exposure_loss: 0.6483 - val_illumination_smoothness_loss: 0.1289 - val_spatial_constancy_loss: 0.2772 - val_total_loss: 1.1456
Epoch 32/100
 25/25 ━━━━━━━━━━━━━━━━━━━━ 2s 63ms/step - color_constancy_loss: 0.0794 - exposure_loss: 0.8226 - illumination_smoothness_loss: 0.0982 - spatial_constancy_loss: 0.2578 - total_loss: 1.2580 - val_color_constancy_loss: 0.0923 - val_exposure_loss: 0.6421 - val_illumination_smoothness_loss: 0.1251 - val_spatial_constancy_loss: 0.2814 - val_total_loss: 1.1409
Epoch 33/100
 25/25 ━━━━━━━━━━━━━━━━━━━━ 2s 64ms/step - color_constancy_loss: 0.0801 - exposure_loss: 0.8188 - illumination_smoothness_loss: 0.0939 - spatial_constancy_loss: 0.2601 - total_loss: 1.2529 - val_color_constancy_loss: 0.0934 - val_exposure_loss: 0.6367 - val_illumination_smoothness_loss: 0.1261 - val_spatial_constancy_loss: 0.2853 - val_total_loss: 1.1416
Epoch 34/100
 25/25 ━━━━━━━━━━━━━━━━━━━━ 2s 63ms/step - color_constancy_loss: 0.0802 - exposure_loss: 0.8173 - illumination_smoothness_loss: 0.0889 - spatial_constancy_loss: 0.2611 - total_loss: 1.2475 - val_color_constancy_loss: 0.0941 - val_exposure_loss: 0.6326 - val_illumination_smoothness_loss: 0.1227 - val_spatial_constancy_loss: 0.2883 - val_total_loss: 1.1378
Epoch 35/100
 25/25 ━━━━━━━━━━━━━━━━━━━━ 2s 65ms/step - color_constancy_loss: 0.0807 - exposure_loss: 0.8134 - illumination_smoothness_loss: 0.0844 - spatial_constancy_loss: 0.2632 - total_loss: 1.2418 - val_color_constancy_loss: 0.0946 - val_exposure_loss: 0.6312 - val_illumination_smoothness_loss: 0.1180 - val_spatial_constancy_loss: 0.2893 - val_total_loss: 1.1330
Epoch 36/100
 25/25 ━━━━━━━━━━━━━━━━━━━━ 2s 63ms/step - color_constancy_loss: 0.0808 - exposure_loss: 0.8119 - illumination_smoothness_loss: 0.0798 - spatial_constancy_loss: 0.2644 - total_loss: 1.2368 - val_color_constancy_loss: 0.0941 - val_exposure_loss: 0.6351 - val_illumination_smoothness_loss: 0.1096 - val_spatial_constancy_loss: 0.2865 - val_total_loss: 1.1253
Epoch 37/100
 25/25 ━━━━━━━━━━━━━━━━━━━━ 2s 63ms/step - color_constancy_loss: 0.0807 - exposure_loss: 0.8127 - illumination_smoothness_loss: 0.0759 - spatial_constancy_loss: 0.2637 - total_loss: 1.2330 - val_color_constancy_loss: 0.0949 - val_exposure_loss: 0.6295 - val_illumination_smoothness_loss: 0.1088 - val_spatial_constancy_loss: 0.2904 - val_total_loss: 1.1237
Epoch 38/100
 25/25 ━━━━━━━━━━━━━━━━━━━━ 2s 64ms/step - color_constancy_loss: 0.0812 - exposure_loss: 0.8091 - illumination_smoothness_loss: 0.0732 - spatial_constancy_loss: 0.2658 - total_loss: 1.2293 - val_color_constancy_loss: 0.0946 - val_exposure_loss: 0.6313 - val_illumination_smoothness_loss: 0.1022 - val_spatial_constancy_loss: 0.2893 - val_total_loss: 1.1174
Epoch 39/100
 25/25 ━━━━━━━━━━━━━━━━━━━━ 2s 63ms/step - color_constancy_loss: 0.0810 - exposure_loss: 0.8100 - illumination_smoothness_loss: 0.0694 - spatial_constancy_loss: 0.2655 - total_loss: 1.2259 - val_color_constancy_loss: 0.0953 - val_exposure_loss: 0.6278 - val_illumination_smoothness_loss: 0.1015 - val_spatial_constancy_loss: 0.2918 - val_total_loss: 1.1164
Epoch 40/100
 25/25 ━━━━━━━━━━━━━━━━━━━━ 2s 64ms/step - color_constancy_loss: 0.0813 - exposure_loss: 0.8077 - illumination_smoothness_loss: 0.0668 - spatial_constancy_loss: 0.2668 - total_loss: 1.2226 - val_color_constancy_loss: 0.0951 - val_exposure_loss: 0.6294 - val_illumination_smoothness_loss: 0.0950 - val_spatial_constancy_loss: 0.2907 - val_total_loss: 1.1103
Epoch 41/100
 25/25 ━━━━━━━━━━━━━━━━━━━━ 2s 63ms/step - color_constancy_loss: 0.0814 - exposure_loss: 0.8074 - illumination_smoothness_loss: 0.0639 - spatial_constancy_loss: 0.2669 - total_loss: 1.2195 - val_color_constancy_loss: 0.0955 - val_exposure_loss: 0.6263 - val_illumination_smoothness_loss: 0.0946 - val_spatial_constancy_loss: 0.2930 - val_total_loss: 1.1093
Epoch 42/100
 25/25 ━━━━━━━━━━━━━━━━━━━━ 2s 63ms/step - color_constancy_loss: 0.0816 - exposure_loss: 0.8056 - illumination_smoothness_loss: 0.0613 - spatial_constancy_loss: 0.2684 - total_loss: 1.2168 - val_color_constancy_loss: 0.0950 - val_exposure_loss: 0.6304 - val_illumination_smoothness_loss: 0.0876 - val_spatial_constancy_loss: 0.2900 - val_total_loss: 1.1031
Epoch 43/100
 25/25 ━━━━━━━━━━━━━━━━━━━━ 2s 64ms/step - color_constancy_loss: 0.0813 - exposure_loss: 0.8074 - illumination_smoothness_loss: 0.0582 - spatial_constancy_loss: 0.2671 - total_loss: 1.2140 - val_color_constancy_loss: 0.0953 - val_exposure_loss: 0.6271 - val_illumination_smoothness_loss: 0.0859 - val_spatial_constancy_loss: 0.2925 - val_total_loss: 1.1008
Epoch 44/100
 25/25 ━━━━━━━━━━━━━━━━━━━━ 2s 63ms/step - color_constancy_loss: 0.0816 - exposure_loss: 0.8048 - illumination_smoothness_loss: 0.0564 - spatial_constancy_loss: 0.2687 - total_loss: 1.2115 - val_color_constancy_loss: 0.0956 - val_exposure_loss: 0.6266 - val_illumination_smoothness_loss: 0.0837 - val_spatial_constancy_loss: 0.2930 - val_total_loss: 1.0988
Epoch 45/100
 25/25 ━━━━━━━━━━━━━━━━━━━━ 2s 63ms/step - color_constancy_loss: 0.0816 - exposure_loss: 0.8045 - illumination_smoothness_loss: 0.0541 - spatial_constancy_loss: 0.2690 - total_loss: 1.2093 - val_color_constancy_loss: 0.0955 - val_exposure_loss: 0.6275 - val_illumination_smoothness_loss: 0.0796 - val_spatial_constancy_loss: 0.2923 - val_total_loss: 1.0949
Epoch 46/100
 25/25 ━━━━━━━━━━━━━━━━━━━━ 2s 64ms/step - color_constancy_loss: 0.0816 - exposure_loss: 0.8043 - illumination_smoothness_loss: 0.0517 - spatial_constancy_loss: 0.2691 - total_loss: 1.2067 - val_color_constancy_loss: 0.0959 - val_exposure_loss: 0.6245 - val_illumination_smoothness_loss: 0.0790 - val_spatial_constancy_loss: 0.2945 - val_total_loss: 1.0939
Epoch 47/100
 25/25 ━━━━━━━━━━━━━━━━━━━━ 2s 63ms/step - color_constancy_loss: 0.0819 - exposure_loss: 0.8025 - illumination_smoothness_loss: 0.0505 - spatial_constancy_loss: 0.2701 - total_loss: 1.2050 - val_color_constancy_loss: 0.0960 - val_exposure_loss: 0.6242 - val_illumination_smoothness_loss: 0.0764 - val_spatial_constancy_loss: 0.2949 - val_total_loss: 1.0914
Epoch 48/100
 25/25 ━━━━━━━━━━━━━━━━━━━━ 2s 63ms/step - color_constancy_loss: 0.0819 - exposure_loss: 0.8021 - illumination_smoothness_loss: 0.0482 - spatial_constancy_loss: 0.2706 - total_loss: 1.2027 - val_color_constancy_loss: 0.0957 - val_exposure_loss: 0.6262 - val_illumination_smoothness_loss: 0.0721 - val_spatial_constancy_loss: 0.2934 - val_total_loss: 1.0874
Epoch 49/100
 25/25 ━━━━━━━━━━━━━━━━━━━━ 2s 64ms/step - color_constancy_loss: 0.0818 - exposure_loss: 0.8027 - illumination_smoothness_loss: 0.0463 - spatial_constancy_loss: 0.2702 - total_loss: 1.2010 - val_color_constancy_loss: 0.0959 - val_exposure_loss: 0.6244 - val_illumination_smoothness_loss: 0.0712 - val_spatial_constancy_loss: 0.2947 - val_total_loss: 1.0863
Epoch 50/100
 25/25 ━━━━━━━━━━━━━━━━━━━━ 2s 64ms/step - color_constancy_loss: 0.0820 - exposure_loss: 0.8015 - illumination_smoothness_loss: 0.0446 - spatial_constancy_loss: 0.2711 - total_loss: 1.1992 - val_color_constancy_loss: 0.0959 - val_exposure_loss: 0.6248 - val_illumination_smoothness_loss: 0.0688 - val_spatial_constancy_loss: 0.2945 - val_total_loss: 1.0839
Epoch 51/100
 25/25 ━━━━━━━━━━━━━━━━━━━━ 2s 63ms/step - color_constancy_loss: 0.0819 - exposure_loss: 0.8019 - illumination_smoothness_loss: 0.0429 - spatial_constancy_loss: 0.2707 - total_loss: 1.1974 - val_color_constancy_loss: 0.0964 - val_exposure_loss: 0.6224 - val_illumination_smoothness_loss: 0.0677 - val_spatial_constancy_loss: 0.2964 - val_total_loss: 1.0829
Epoch 52/100
 25/25 ━━━━━━━━━━━━━━━━━━━━ 2s 63ms/step - color_constancy_loss: 0.0823 - exposure_loss: 0.7996 - illumination_smoothness_loss: 0.0416 - spatial_constancy_loss: 0.2721 - total_loss: 1.1955 - val_color_constancy_loss: 0.0958 - val_exposure_loss: 0.6240 - val_illumination_smoothness_loss: 0.0644 - val_spatial_constancy_loss: 0.2951 - val_total_loss: 1.0793
Epoch 53/100
 25/25 ━━━━━━━━━━━━━━━━━━━━ 2s 63ms/step - color_constancy_loss: 0.0822 - exposure_loss: 0.8004 - illumination_smoothness_loss: 0.0399 - spatial_constancy_loss: 0.2717 - total_loss: 1.1941 - val_color_constancy_loss: 0.0960 - val_exposure_loss: 0.6234 - val_illumination_smoothness_loss: 0.0633 - val_spatial_constancy_loss: 0.2957 - val_total_loss: 1.0785
Epoch 54/100
 25/25 ━━━━━━━━━━━━━━━━━━━━ 2s 64ms/step - color_constancy_loss: 0.0823 - exposure_loss: 0.7997 - illumination_smoothness_loss: 0.0382 - spatial_constancy_loss: 0.2723 - total_loss: 1.1924 - val_color_constancy_loss: 0.0959 - val_exposure_loss: 0.6242 - val_illumination_smoothness_loss: 0.0591 - val_spatial_constancy_loss: 0.2951 - val_total_loss: 1.0744
Epoch 55/100
 25/25 ━━━━━━━━━━━━━━━━━━━━ 2s 64ms/step - color_constancy_loss: 0.0822 - exposure_loss: 0.7999 - illumination_smoothness_loss: 0.0362 - spatial_constancy_loss: 0.2721 - total_loss: 1.1904 - val_color_constancy_loss: 0.0965 - val_exposure_loss: 0.6211 - val_illumination_smoothness_loss: 0.0603 - val_spatial_constancy_loss: 0.2974 - val_total_loss: 1.0754
Epoch 56/100
 25/25 ━━━━━━━━━━━━━━━━━━━━ 2s 64ms/step - color_constancy_loss: 0.0825 - exposure_loss: 0.7983 - illumination_smoothness_loss: 0.0351 - spatial_constancy_loss: 0.2732 - total_loss: 1.1890 - val_color_constancy_loss: 0.0960 - val_exposure_loss: 0.6237 - val_illumination_smoothness_loss: 0.0547 - val_spatial_constancy_loss: 0.2955 - val_total_loss: 1.0699
Epoch 57/100
 25/25 ━━━━━━━━━━━━━━━━━━━━ 2s 64ms/step - color_constancy_loss: 0.0823 - exposure_loss: 0.7987 - illumination_smoothness_loss: 0.0331 - spatial_constancy_loss: 0.2730 - total_loss: 1.1871 - val_color_constancy_loss: 0.0963 - val_exposure_loss: 0.6236 - val_illumination_smoothness_loss: 0.0540 - val_spatial_constancy_loss: 0.2956 - val_total_loss: 1.0694
Epoch 58/100
 25/25 ━━━━━━━━━━━━━━━━━━━━ 2s 64ms/step - color_constancy_loss: 0.0823 - exposure_loss: 0.7990 - illumination_smoothness_loss: 0.0319 - spatial_constancy_loss: 0.2727 - total_loss: 1.1859 - val_color_constancy_loss: 0.0965 - val_exposure_loss: 0.6210 - val_illumination_smoothness_loss: 0.0537 - val_spatial_constancy_loss: 0.2976 - val_total_loss: 1.0688
Epoch 59/100
 25/25 ━━━━━━━━━━━━━━━━━━━━ 2s 63ms/step - color_constancy_loss: 0.0826 - exposure_loss: 0.7969 - illumination_smoothness_loss: 0.0315 - spatial_constancy_loss: 0.2740 - total_loss: 1.1850 - val_color_constancy_loss: 0.0966 - val_exposure_loss: 0.6208 - val_illumination_smoothness_loss: 0.0530 - val_spatial_constancy_loss: 0.2978 - val_total_loss: 1.0682
Epoch 60/100
 25/25 ━━━━━━━━━━━━━━━━━━━━ 2s 63ms/step - color_constancy_loss: 0.0824 - exposure_loss: 0.7971 - illumination_smoothness_loss: 0.0304 - spatial_constancy_loss: 0.2740 - total_loss: 1.1840 - val_color_constancy_loss: 0.0966 - val_exposure_loss: 0.6206 - val_illumination_smoothness_loss: 0.0516 - val_spatial_constancy_loss: 0.2979 - val_total_loss: 1.0667
Epoch 61/100
 25/25 ━━━━━━━━━━━━━━━━━━━━ 2s 63ms/step - color_constancy_loss: 0.0825 - exposure_loss: 0.7969 - illumination_smoothness_loss: 0.0295 - spatial_constancy_loss: 0.2741 - total_loss: 1.1829 - val_color_constancy_loss: 0.0969 - val_exposure_loss: 0.6194 - val_illumination_smoothness_loss: 0.0506 - val_spatial_constancy_loss: 0.2988 - val_total_loss: 1.0657
Epoch 62/100
 25/25 ━━━━━━━━━━━━━━━━━━━━ 2s 64ms/step - color_constancy_loss: 0.0827 - exposure_loss: 0.7954 - illumination_smoothness_loss: 0.0287 - spatial_constancy_loss: 0.2749 - total_loss: 1.1817 - val_color_constancy_loss: 0.0967 - val_exposure_loss: 0.6203 - val_illumination_smoothness_loss: 0.0494 - val_spatial_constancy_loss: 0.2981 - val_total_loss: 1.0644
Epoch 63/100
 25/25 ━━━━━━━━━━━━━━━━━━━━ 2s 63ms/step - color_constancy_loss: 0.0825 - exposure_loss: 0.7966 - illumination_smoothness_loss: 0.0278 - spatial_constancy_loss: 0.2742 - total_loss: 1.1810 - val_color_constancy_loss: 0.0971 - val_exposure_loss: 0.6184 - val_illumination_smoothness_loss: 0.0491 - val_spatial_constancy_loss: 0.2996 - val_total_loss: 1.0642
Epoch 64/100
 25/25 ━━━━━━━━━━━━━━━━━━━━ 2s 67ms/step - color_constancy_loss: 0.0827 - exposure_loss: 0.7949 - illumination_smoothness_loss: 0.0268 - spatial_constancy_loss: 0.2753 - total_loss: 1.1797 - val_color_constancy_loss: 0.0969 - val_exposure_loss: 0.6199 - val_illumination_smoothness_loss: 0.0460 - val_spatial_constancy_loss: 0.2984 - val_total_loss: 1.0611
Epoch 65/100
 25/25 ━━━━━━━━━━━━━━━━━━━━ 2s 64ms/step - color_constancy_loss: 0.0826 - exposure_loss: 0.7957 - illumination_smoothness_loss: 0.0254 - spatial_constancy_loss: 0.2748 - total_loss: 1.1785 - val_color_constancy_loss: 0.0976 - val_exposure_loss: 0.6180 - val_illumination_smoothness_loss: 0.0464 - val_spatial_constancy_loss: 0.2998 - val_total_loss: 1.0618
Epoch 66/100
 25/25 ━━━━━━━━━━━━━━━━━━━━ 2s 64ms/step - color_constancy_loss: 0.0827 - exposure_loss: 0.7948 - illumination_smoothness_loss: 0.0249 - spatial_constancy_loss: 0.2753 - total_loss: 1.1777 - val_color_constancy_loss: 0.0975 - val_exposure_loss: 0.6189 - val_illumination_smoothness_loss: 0.0448 - val_spatial_constancy_loss: 0.2991 - val_total_loss: 1.0602
Epoch 67/100
 25/25 ━━━━━━━━━━━━━━━━━━━━ 2s 63ms/step - color_constancy_loss: 0.0825 - exposure_loss: 0.7954 - illumination_smoothness_loss: 0.0241 - spatial_constancy_loss: 0.2750 - total_loss: 1.1770 - val_color_constancy_loss: 0.0977 - val_exposure_loss: 0.6179 - val_illumination_smoothness_loss: 0.0441 - val_spatial_constancy_loss: 0.2998 - val_total_loss: 1.0595
Epoch 68/100
 25/25 ━━━━━━━━━━━━━━━━━━━━ 2s 64ms/step - color_constancy_loss: 0.0827 - exposure_loss: 0.7946 - illumination_smoothness_loss: 0.0231 - spatial_constancy_loss: 0.2757 - total_loss: 1.1761 - val_color_constancy_loss: 0.0973 - val_exposure_loss: 0.6198 - val_illumination_smoothness_loss: 0.0410 - val_spatial_constancy_loss: 0.2980 - val_total_loss: 1.0562
Epoch 69/100
 25/25 ━━━━━━━━━━━━━━━━━━━━ 2s 63ms/step - color_constancy_loss: 0.0826 - exposure_loss: 0.7947 - illumination_smoothness_loss: 0.0226 - spatial_constancy_loss: 0.2752 - total_loss: 1.1752 - val_color_constancy_loss: 0.0979 - val_exposure_loss: 0.6170 - val_illumination_smoothness_loss: 0.0435 - val_spatial_constancy_loss: 0.3003 - val_total_loss: 1.0587
Epoch 70/100
 25/25 ━━━━━━━━━━━━━━━━━━━━ 2s 63ms/step - color_constancy_loss: 0.0828 - exposure_loss: 0.7940 - illumination_smoothness_loss: 0.0224 - spatial_constancy_loss: 0.2758 - total_loss: 1.1749 - val_color_constancy_loss: 0.0976 - val_exposure_loss: 0.6182 - val_illumination_smoothness_loss: 0.0414 - val_spatial_constancy_loss: 0.2994 - val_total_loss: 1.0566
Epoch 71/100
 25/25 ━━━━━━━━━━━━━━━━━━━━ 2s 64ms/step - color_constancy_loss: 0.0827 - exposure_loss: 0.7941 - illumination_smoothness_loss: 0.0216 - spatial_constancy_loss: 0.2758 - total_loss: 1.1742 - val_color_constancy_loss: 0.0974 - val_exposure_loss: 0.6189 - val_illumination_smoothness_loss: 0.0389 - val_spatial_constancy_loss: 0.2986 - val_total_loss: 1.0538
Epoch 72/100
 25/25 ━━━━━━━━━━━━━━━━━━━━ 2s 64ms/step - color_constancy_loss: 0.0827 - exposure_loss: 0.7941 - illumination_smoothness_loss: 0.0211 - spatial_constancy_loss: 0.2755 - total_loss: 1.1734 - val_color_constancy_loss: 0.0979 - val_exposure_loss: 0.6166 - val_illumination_smoothness_loss: 0.0420 - val_spatial_constancy_loss: 0.3005 - val_total_loss: 1.0571
Epoch 73/100
 25/25 ━━━━━━━━━━━━━━━━━━━━ 2s 63ms/step - color_constancy_loss: 0.0828 - exposure_loss: 0.7935 - illumination_smoothness_loss: 0.0214 - spatial_constancy_loss: 0.2759 - total_loss: 1.1735 - val_color_constancy_loss: 0.0977 - val_exposure_loss: 0.6172 - val_illumination_smoothness_loss: 0.0401 - val_spatial_constancy_loss: 0.3001 - val_total_loss: 1.0551
Epoch 74/100
 25/25 ━━━━━━━━━━━━━━━━━━━━ 2s 63ms/step - color_constancy_loss: 0.0828 - exposure_loss: 0.7935 - illumination_smoothness_loss: 0.0205 - spatial_constancy_loss: 0.2760 - total_loss: 1.1727 - val_color_constancy_loss: 0.0978 - val_exposure_loss: 0.6168 - val_illumination_smoothness_loss: 0.0395 - val_spatial_constancy_loss: 0.3005 - val_total_loss: 1.0546
Epoch 75/100
 25/25 ━━━━━━━━━━━━━━━━━━━━ 2s 63ms/step - color_constancy_loss: 0.0828 - exposure_loss: 0.7924 - illumination_smoothness_loss: 0.0204 - spatial_constancy_loss: 0.2764 - total_loss: 1.1721 - val_color_constancy_loss: 0.0977 - val_exposure_loss: 0.6176 - val_illumination_smoothness_loss: 0.0385 - val_spatial_constancy_loss: 0.2997 - val_total_loss: 1.0536
Epoch 76/100
 25/25 ━━━━━━━━━━━━━━━━━━━━ 2s 63ms/step - color_constancy_loss: 0.0827 - exposure_loss: 0.7933 - illumination_smoothness_loss: 0.0198 - spatial_constancy_loss: 0.2760 - total_loss: 1.1718 - val_color_constancy_loss: 0.0979 - val_exposure_loss: 0.6166 - val_illumination_smoothness_loss: 0.0376 - val_spatial_constancy_loss: 0.3002 - val_total_loss: 1.0524
Epoch 77/100
 25/25 ━━━━━━━━━━━━━━━━━━━━ 2s 64ms/step - color_constancy_loss: 0.0828 - exposure_loss: 0.7925 - illumination_smoothness_loss: 0.0195 - spatial_constancy_loss: 0.2763 - total_loss: 1.1710 - val_color_constancy_loss: 0.0979 - val_exposure_loss: 0.6170 - val_illumination_smoothness_loss: 0.0384 - val_spatial_constancy_loss: 0.2999 - val_total_loss: 1.0532
Epoch 78/100
 25/25 ━━━━━━━━━━━━━━━━━━━━ 2s 64ms/step - color_constancy_loss: 0.0827 - exposure_loss: 0.7929 - illumination_smoothness_loss: 0.0196 - spatial_constancy_loss: 0.2761 - total_loss: 1.1713 - val_color_constancy_loss: 0.0979 - val_exposure_loss: 0.6170 - val_illumination_smoothness_loss: 0.0369 - val_spatial_constancy_loss: 0.3000 - val_total_loss: 1.0518
Epoch 79/100
 25/25 ━━━━━━━━━━━━━━━━━━━━ 2s 63ms/step - color_constancy_loss: 0.0828 - exposure_loss: 0.7922 - illumination_smoothness_loss: 0.0192 - spatial_constancy_loss: 0.2763 - total_loss: 1.1704 - val_color_constancy_loss: 0.0981 - val_exposure_loss: 0.6157 - val_illumination_smoothness_loss: 0.0380 - val_spatial_constancy_loss: 0.3009 - val_total_loss: 1.0527
Epoch 80/100
 25/25 ━━━━━━━━━━━━━━━━━━━━ 2s 63ms/step - color_constancy_loss: 0.0828 - exposure_loss: 0.7918 - illumination_smoothness_loss: 0.0191 - spatial_constancy_loss: 0.2766 - total_loss: 1.1703 - val_color_constancy_loss: 0.0980 - val_exposure_loss: 0.6159 - val_illumination_smoothness_loss: 0.0373 - val_spatial_constancy_loss: 0.3004 - val_total_loss: 1.0516
Epoch 81/100
 25/25 ━━━━━━━━━━━━━━━━━━━━ 2s 64ms/step - color_constancy_loss: 0.0828 - exposure_loss: 0.7917 - illumination_smoothness_loss: 0.0190 - spatial_constancy_loss: 0.2764 - total_loss: 1.1699 - val_color_constancy_loss: 0.0981 - val_exposure_loss: 0.6153 - val_illumination_smoothness_loss: 0.0373 - val_spatial_constancy_loss: 0.3009 - val_total_loss: 1.0516
Epoch 82/100
 25/25 ━━━━━━━━━━━━━━━━━━━━ 2s 66ms/step - color_constancy_loss: 0.0829 - exposure_loss: 0.7915 - illumination_smoothness_loss: 0.0187 - spatial_constancy_loss: 0.2766 - total_loss: 1.1697 - val_color_constancy_loss: 0.0979 - val_exposure_loss: 0.6170 - val_illumination_smoothness_loss: 0.0348 - val_spatial_constancy_loss: 0.2996 - val_total_loss: 1.0493
Epoch 83/100
 25/25 ━━━━━━━━━━━━━━━━━━━━ 2s 65ms/step - color_constancy_loss: 0.0828 - exposure_loss: 0.7918 - illumination_smoothness_loss: 0.0182 - spatial_constancy_loss: 0.2763 - total_loss: 1.1691 - val_color_constancy_loss: 0.0980 - val_exposure_loss: 0.6158 - val_illumination_smoothness_loss: 0.0358 - val_spatial_constancy_loss: 0.3004 - val_total_loss: 1.0500
Epoch 84/100
 25/25 ━━━━━━━━━━━━━━━━━━━━ 2s 65ms/step - color_constancy_loss: 0.0829 - exposure_loss: 0.7911 - illumination_smoothness_loss: 0.0184 - spatial_constancy_loss: 0.2766 - total_loss: 1.1689 - val_color_constancy_loss: 0.0982 - val_exposure_loss: 0.6146 - val_illumination_smoothness_loss: 0.0366 - val_spatial_constancy_loss: 0.3010 - val_total_loss: 1.0505
Epoch 85/100
 25/25 ━━━━━━━━━━━━━━━━━━━━ 2s 64ms/step - color_constancy_loss: 0.0829 - exposure_loss: 0.7907 - illumination_smoothness_loss: 0.0185 - spatial_constancy_loss: 0.2767 - total_loss: 1.1687 - val_color_constancy_loss: 0.0980 - val_exposure_loss: 0.6154 - val_illumination_smoothness_loss: 0.0361 - val_spatial_constancy_loss: 0.3006 - val_total_loss: 1.0501
Epoch 86/100
 25/25 ━━━━━━━━━━━━━━━━━━━━ 2s 65ms/step - color_constancy_loss: 0.0828 - exposure_loss: 0.7910 - illumination_smoothness_loss: 0.0182 - spatial_constancy_loss: 0.2765 - total_loss: 1.1685 - val_color_constancy_loss: 0.0982 - val_exposure_loss: 0.6145 - val_illumination_smoothness_loss: 0.0356 - val_spatial_constancy_loss: 0.3009 - val_total_loss: 1.0492
Epoch 87/100
 25/25 ━━━━━━━━━━━━━━━━━━━━ 2s 64ms/step - color_constancy_loss: 0.0829 - exposure_loss: 0.7902 - illumination_smoothness_loss: 0.0181 - spatial_constancy_loss: 0.2767 - total_loss: 1.1680 - val_color_constancy_loss: 0.0981 - val_exposure_loss: 0.6149 - val_illumination_smoothness_loss: 0.0357 - val_spatial_constancy_loss: 0.3007 - val_total_loss: 1.0494
Epoch 88/100
 25/25 ━━━━━━━━━━━━━━━━━━━━ 2s 64ms/step - color_constancy_loss: 0.0829 - exposure_loss: 0.7904 - illumination_smoothness_loss: 0.0180 - spatial_constancy_loss: 0.2766 - total_loss: 1.1679 - val_color_constancy_loss: 0.0983 - val_exposure_loss: 0.6133 - val_illumination_smoothness_loss: 0.0359 - val_spatial_constancy_loss: 0.3015 - val_total_loss: 1.0491
Epoch 89/100
 25/25 ━━━━━━━━━━━━━━━━━━━━ 2s 64ms/step - color_constancy_loss: 0.0830 - exposure_loss: 0.7893 - illumination_smoothness_loss: 0.0181 - spatial_constancy_loss: 0.2770 - total_loss: 1.1674 - val_color_constancy_loss: 0.0981 - val_exposure_loss: 0.6148 - val_illumination_smoothness_loss: 0.0350 - val_spatial_constancy_loss: 0.3006 - val_total_loss: 1.0484
Epoch 90/100
 25/25 ━━━━━━━━━━━━━━━━━━━━ 2s 63ms/step - color_constancy_loss: 0.0829 - exposure_loss: 0.7901 - illumination_smoothness_loss: 0.0178 - spatial_constancy_loss: 0.2765 - total_loss: 1.1673 - val_color_constancy_loss: 0.0984 - val_exposure_loss: 0.6128 - val_illumination_smoothness_loss: 0.0358 - val_spatial_constancy_loss: 0.3017 - val_total_loss: 1.0487
Epoch 91/100
 25/25 ━━━━━━━━━━━━━━━━━━━━ 2s 63ms/step - color_constancy_loss: 0.0831 - exposure_loss: 0.7886 - illumination_smoothness_loss: 0.0181 - spatial_constancy_loss: 0.2771 - total_loss: 1.1669 - val_color_constancy_loss: 0.0981 - val_exposure_loss: 0.6142 - val_illumination_smoothness_loss: 0.0351 - val_spatial_constancy_loss: 0.3007 - val_total_loss: 1.0481
Epoch 92/100
 25/25 ━━━━━━━━━━━━━━━━━━━━ 2s 63ms/step - color_constancy_loss: 0.0829 - exposure_loss: 0.7895 - illumination_smoothness_loss: 0.0177 - spatial_constancy_loss: 0.2766 - total_loss: 1.1668 - val_color_constancy_loss: 0.0983 - val_exposure_loss: 0.6133 - val_illumination_smoothness_loss: 0.0349 - val_spatial_constancy_loss: 0.3011 - val_total_loss: 1.0476
Epoch 93/100
 25/25 ━━━━━━━━━━━━━━━━━━━━ 2s 64ms/step - color_constancy_loss: 0.0831 - exposure_loss: 0.7884 - illumination_smoothness_loss: 0.0179 - spatial_constancy_loss: 0.2770 - total_loss: 1.1664 - val_color_constancy_loss: 0.0984 - val_exposure_loss: 0.6125 - val_illumination_smoothness_loss: 0.0355 - val_spatial_constancy_loss: 0.3014 - val_total_loss: 1.0478
Epoch 94/100
 25/25 ━━━━━━━━━━━━━━━━━━━━ 2s 65ms/step - color_constancy_loss: 0.0831 - exposure_loss: 0.7882 - illumination_smoothness_loss: 0.0181 - spatial_constancy_loss: 0.2769 - total_loss: 1.1663 - val_color_constancy_loss: 0.0983 - val_exposure_loss: 0.6128 - val_illumination_smoothness_loss: 0.0349 - val_spatial_constancy_loss: 0.3012 - val_total_loss: 1.0473
Epoch 95/100
 25/25 ━━━━━━━━━━━━━━━━━━━━ 2s 63ms/step - color_constancy_loss: 0.0831 - exposure_loss: 0.7881 - illumination_smoothness_loss: 0.0179 - spatial_constancy_loss: 0.2770 - total_loss: 1.1660 - val_color_constancy_loss: 0.0983 - val_exposure_loss: 0.6130 - val_illumination_smoothness_loss: 0.0341 - val_spatial_constancy_loss: 0.3009 - val_total_loss: 1.0462
Epoch 96/100
 25/25 ━━━━━━━━━━━━━━━━━━━━ 2s 63ms/step - color_constancy_loss: 0.0832 - exposure_loss: 0.7874 - illumination_smoothness_loss: 0.0179 - spatial_constancy_loss: 0.2771 - total_loss: 1.1656 - val_color_constancy_loss: 0.0983 - val_exposure_loss: 0.6125 - val_illumination_smoothness_loss: 0.0353 - val_spatial_constancy_loss: 0.3010 - val_total_loss: 1.0471
Epoch 97/100
 25/25 ━━━━━━━━━━━━━━━━━━━━ 2s 63ms/step - color_constancy_loss: 0.0830 - exposure_loss: 0.7882 - illumination_smoothness_loss: 0.0181 - spatial_constancy_loss: 0.2765 - total_loss: 1.1658 - val_color_constancy_loss: 0.0984 - val_exposure_loss: 0.6120 - val_illumination_smoothness_loss: 0.0346 - val_spatial_constancy_loss: 0.3014 - val_total_loss: 1.0464
Epoch 98/100
 25/25 ━━━━━━━━━━━━━━━━━━━━ 2s 63ms/step - color_constancy_loss: 0.0832 - exposure_loss: 0.7869 - illumination_smoothness_loss: 0.0180 - spatial_constancy_loss: 0.2772 - total_loss: 1.1653 - val_color_constancy_loss: 0.0984 - val_exposure_loss: 0.6118 - val_illumination_smoothness_loss: 0.0344 - val_spatial_constancy_loss: 0.3012 - val_total_loss: 1.0458
Epoch 99/100
 25/25 ━━━━━━━━━━━━━━━━━━━━ 2s 64ms/step - color_constancy_loss: 0.0832 - exposure_loss: 0.7863 - illumination_smoothness_loss: 0.0182 - spatial_constancy_loss: 0.2772 - total_loss: 1.1650 - val_color_constancy_loss: 0.0983 - val_exposure_loss: 0.6120 - val_illumination_smoothness_loss: 0.0343 - val_spatial_constancy_loss: 0.3007 - val_total_loss: 1.0453
Epoch 100/100
 25/25 ━━━━━━━━━━━━━━━━━━━━ 2s 64ms/step - color_constancy_loss: 0.0831 - exposure_loss: 0.7873 - illumination_smoothness_loss: 0.0180 - spatial_constancy_loss: 0.2765 - total_loss: 1.1649 - val_color_constancy_loss: 0.0984 - val_exposure_loss: 0.6115 - val_illumination_smoothness_loss: 0.0341 - val_spatial_constancy_loss: 0.3011 - val_total_loss: 1.0451

png

推理

def plot_results(images, titles, figure_size=(12, 12)):
    fig = plt.figure(figsize=figure_size)
    for i in range(len(images)):
        fig.add_subplot(1, len(images), i + 1).set_title(titles[i])
        _ = plt.imshow(images[i])
        plt.axis("off")
    plt.show()


def infer(original_image):
    image = keras.utils.img_to_array(original_image)
    image = image.astype("float32") / 255.0
    image = np.expand_dims(image, axis=0)
    output_image = zero_dce_model(image)
    output_image = tf.cast((output_image[0, :, :, :] * 255), dtype=np.uint8)
    output_image = Image.fromarray(output_image.numpy())
    return output_image

测试图像上的推理

我们将由 MIRNet 增强后的 LOLDataset 中的测试图像与通过PIL.ImageOps.autocontrast()函数增强的图像进行比较。

您可以使用托管在Hugging Face Hub上的训练模型，并在Hugging Face Spaces上尝试演示。

for val_image_file in test_low_light_images:
    original_image = Image.open(val_image_file)
    enhanced_image = infer(original_image)
    plot_results(
        [original_image, ImageOps.autocontrast(original_image), enhanced_image],
        ["Original", "PIL Autocontrast", "Enhanced"],
        (20, 12),
    )