设计一段IDLE UNET分割代码，其中训练集原始图像"data\train\trainvol""，标签"data\train\trainseg"，验证集原始图像"data\val\valvol"，标签"data\val\valseg"

以下是一个简单的IDLE UNET分割代码示例，实现了对训练集和验证集的数据读取、模型训练和评估：

import os
import numpy as np
import tensorflow as tf
from tensorflow.keras.models import Model
from tensorflow.keras.layers import Input, Conv2D, MaxPooling2D, Dropout, UpSampling2D, concatenate

# 设置训练集和验证集的路径
train_vol_path = "data/train/trainvol"
train_seg_path = "data/train/trainseg"
val_vol_path = "data/val/valvol"
val_seg_path = "data/val/valseg"

# 定义IDLE UNET模型
def IDLE_UNET(input_size=(256, 256, 1)):
    inputs = Input(input_size)

    # 编码器
    conv1 = Conv2D(64, 3, activation='relu', padding='same', kernel_initializer='he_normal')(inputs)
    conv1 = Conv2D(64, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv1)
    pool1 = MaxPooling2D(pool_size=(2, 2))(conv1)

    conv2 = Conv2D(128, 3, activation='relu', padding='same', kernel_initializer='he_normal')(pool1)
    conv2 = Conv2D(128, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv2)
    pool2 = MaxPooling2D(pool_size=(2, 2))(conv2)

    conv3 = Conv2D(256, 3, activation='relu', padding='same', kernel_initializer='he_normal')(pool2)
    conv3 = Conv2D(256, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv3)
    pool3 = MaxPooling2D(pool_size=(2, 2))(conv3)

    conv4 = Conv2D(512, 3, activation='relu', padding='same', kernel_initializer='he_normal')(pool3)
    conv4 = Conv2D(512, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv4)
    drop4 = Dropout(0.5)(conv4)
    pool4 = MaxPooling2D(pool_size=(2, 2))(drop4)

    conv5 = Conv2D(1024, 3, activation='relu', padding='same', kernel_initializer='he_normal')(pool4)
    conv5 = Conv2D(1024, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv5)
    drop5 = Dropout(0.5)(conv5)

    # 解码器
    up6 = Conv2D(512, 2, activation='relu', padding='same', kernel_initializer='he_normal')(
        UpSampling2D(size=(2, 2))(drop5))
    merge6 = concatenate([drop4, up6], axis=3)
    conv6 = Conv2D(512, 3, activation='relu', padding='same', kernel_initializer='he_normal')(merge6)
    conv6 = Conv2D(512, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv6)

    up7 = Conv2D(256, 2, activation='relu', padding='same', kernel_initializer='he_normal')(
        UpSampling2D(size=(2, 2))(conv6))
    merge7 = concatenate([conv3, up7], axis=3)
    conv7 = Conv2D(256, 3, activation='relu', padding='same', kernel_initializer='he_normal')(merge7)
    conv7 = Conv2D(256, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv7)

    up8 = Conv2D(128, 2, activation='relu', padding='same', kernel_initializer='he_normal')(
        UpSampling2D(size=(2, 2))(conv7))
    merge8 = concatenate([conv2, up8], axis=3)
    conv8 = Conv2D(128, 3, activation='relu', padding='same', kernel_initializer='he_normal')(merge8)
    conv8 = Conv2D(128, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv8)

    up9 = Conv2D(64, 2, activation='relu', padding='same', kernel_initializer='he_normal')(
        UpSampling2D(size=(2, 2))(conv8))
    merge9 = concatenate([conv1, up9], axis=3)
    conv9 = Conv2D(64, 3, activation='relu', padding='same', kernel_initializer='he_normal')(merge9)
    conv9 = Conv2D(64, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv9)
    conv9 = Conv2D(2, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv9)
    conv10 = Conv2D(1, 1, activation='sigmoid')(conv9)

    model = Model(inputs=[inputs], outputs=[conv10])

    return model

# 加载训练数据
train_vol = []
train_seg = []
for filename in os.listdir(train_vol_path):
    if filename.endswith(".npy"):
        vol = np.load(os.path.join(train_vol_path, filename))
        seg = np.load(os.path.join(train_seg_path, filename))
        train_vol.append(vol)
        train_seg.append(seg)
train_vol = np.array(train_vol)
train_seg = np.array(train_seg)

# 加载验证数据
val_vol = []
val_seg = []
for filename in os.listdir(val_vol_path):
    if filename.endswith(".npy"):
        vol = np.load(os.path.join(val_vol_path, filename))
        seg = np.load(os.path.join(val_seg_path, filename))
        val_vol.append(vol)
        val_seg.append(seg)
val_vol = np.array(val_vol)
val_seg = np.array(val_seg)

# 训练模型
model = IDLE_UNET()
model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
model.fit(train_vol, train_seg, batch_size=4, epochs=10, validation_data=(val_vol, val_seg))

# 评估模型
results = model.evaluate(val_vol, val_seg, batch_size=4)
print("Validation loss:", results[0])
print("Validation accuracy:", results[1])

请注意，以上代码只是一个简单的示例，具体实现可能需要根据您的数据集和任务进行调整。同时，您需要确保训练集和验证集的数据格式与代码中的数据读取方式一致，否则会出现错误。