我试着用drone-images训练DeepLabv 3+。我的数据集由尺寸为6000 x4000的图像/掩码组成。图像为jpg格式，掩码为png格式。如果我运行以下代码，我会得到ValueError：labels.shape必须等于logits.shape，最后一个维度除外。接收时间：标签形状=（786432，）和对数形状=（262144，23）
我对深度学习还很陌生，很高兴能得到任何建议。

import os
import cv2
import numpy as np
from glob import glob
from scipy.io import loadmat
import matplotlib.pyplot as plt

import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers

#Erstellung des Data-Sets
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IMAGE_SIZE = 512
BATCH_SIZE = 1
NUM_CLASSES = 23
DATA_DIR = 'C:/Users/Julian/Desktop/semantic_drone_dataset_semantics/deeplab'
NUM_TRAIN_IMAGES = 380
NUM_VAL_IMAGES = 20

train_images = sorted(glob(os.path.join(DATA_DIR, "images/*")))[:NUM_TRAIN_IMAGES]
train_masks = sorted(glob(os.path.join(DATA_DIR, "masks/*")))[:NUM_TRAIN_IMAGES]
val_images = sorted(glob(os.path.join(DATA_DIR, "images/*")))[NUM_TRAIN_IMAGES : NUM_VAL_IMAGES + NUM_TRAIN_IMAGES]
val_masks = sorted(glob(os.path.join(DATA_DIR, "masks/*")))[NUM_TRAIN_IMAGES : NUM_VAL_IMAGES + NUM_TRAIN_IMAGES]




def read_image(image_path, mask=False):
    image = tf.io.read_file(image_path)
    if mask:
        image = tf.image.decode_png(image, channels=3)
        image.set_shape([None, None, 3])
        image = tf.image.resize(images=image, size=[IMAGE_SIZE, IMAGE_SIZE])
    else:
        image = tf.image.decode_png(image, channels=3)
        image.set_shape([None, None, 3])
        image = tf.image.resize(images=image, size=[IMAGE_SIZE, IMAGE_SIZE])
        image = image / 127.5 - 1
    return image

def load_data(image_list, mask_list):
    image = read_image(image_list)
    mask = read_image(mask_list, mask=True)
    return image, mask

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

train_dataset = data_generator(train_images, train_masks)
val_dataset = data_generator(val_images, val_masks)

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

#DeeplabV3+ erstellen
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def convolution_block(
    block_input,
    num_filters=256,
    kernel_size=3,
    dilation_rate=1,
    padding="same",
    use_bias=False,
):
    x = layers.Conv2D(
        num_filters,
        kernel_size=kernel_size,
        dilation_rate=dilation_rate,
        padding="same",
        use_bias=use_bias,
        kernel_initializer=keras.initializers.HeNormal(),
    )(block_input)
    x = layers.BatchNormalization()(x)
    return tf.nn.relu(x)

def DilatedSpatialPyramidPooling(dspp_input):
    dims = dspp_input.shape
    x = layers.AveragePooling2D(pool_size=(dims[-3], dims[-2]))(dspp_input)
    x = convolution_block(x, kernel_size=1, use_bias=True)
    out_pool = layers.UpSampling2D(
        size=(dims[-3] // x.shape[1], dims[-2] // x.shape[2]), interpolation="bilinear",
    )(x)

    out_1 = convolution_block(dspp_input, kernel_size=1, dilation_rate=1)
    out_6 = convolution_block(dspp_input, kernel_size=3, dilation_rate=6)
    out_12 = convolution_block(dspp_input, kernel_size=3, dilation_rate=12)
    out_18 = convolution_block(dspp_input, kernel_size=3, dilation_rate=18)

    x = layers.Concatenate(axis=-1)([out_pool, out_1, out_6, out_12, out_18])
    output = convolution_block(x, kernel_size=1)
    return output

def DeeplabV3Plus(image_size, num_classes):
    model_input = keras.Input(shape=(image_size, image_size, 3))
    resnet50 = keras.applications.ResNet50(
        weights="imagenet", include_top=False, input_tensor=model_input
    )
    x = resnet50.get_layer("conv4_block6_2_relu").output
    x = DilatedSpatialPyramidPooling(x)

    input_a = layers.UpSampling2D(
        size=(image_size // 4 // x.shape[1], image_size // 4 // x.shape[2]),
        interpolation="bilinear",
    )(x)
    input_b = resnet50.get_layer("conv2_block3_2_relu").output
    input_b = convolution_block(input_b, num_filters=48, kernel_size=1)

    x = layers.Concatenate(axis=-1)([input_a, input_b])
    x = convolution_block(x)
    x = convolution_block(x)
    x = layers.UpSampling2D(
        size=(image_size // x.shape[1], image_size // x.shape[2]),
        interpolation="bilinear",
    )(x)
    model_output = layers.Conv2D(num_classes, kernel_size=(1, 1), padding="same")(x)
    return keras.Model(inputs=model_input, outputs=model_output)

model = DeeplabV3Plus(image_size=IMAGE_SIZE, num_classes=NUM_CLASSES)
model.summary()

#Modell trainieren
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loss = keras.losses.SparseCategoricalCrossentropy(from_logits=True)
model.compile(
    optimizer=keras.optimizers.Adam(learning_rate=0.001),
    loss=loss,
    metrics=["accuracy"],
)

history = model.fit(train_dataset, validation_data=val_dataset, epochs=25)

plt.plot(history.history["loss"])
plt.title("Training Loss")
plt.ylabel("loss")
plt.xlabel("epoch")
plt.show()

plt.plot(history.history["accuracy"])
plt.title("Training Accuracy")
plt.ylabel("accuracy")
plt.xlabel("epoch")
plt.show()

plt.plot(history.history["val_loss"])
plt.title("Validation Loss")
plt.ylabel("val_loss")
plt.xlabel("epoch")
plt.show()

plt.plot(history.history["val_accuracy"])
plt.title("Validation Accuracy")
plt.ylabel("val_accuracy")
plt.xlabel("epoch")
plt.show()