Tensorflow 中的维度 37 和 50 是什么？

Question

您好，我正在使用 DeeplabV3Plus 架构和 Tensorflow (Keras) 进行语义分割。我用另一个数据集做得很好，但现在我想用自己的数据集来做。但是在加载数据的第一步中，它向我显示了一个奇怪的错误。功能是

tf.data.Dataset.from_tensor_slices

错误是：

ValueError                                Traceback (most recent call last)
~\AppData\Local\Temp\ipykernel_20192\306109049.py in <module>
     57 
     58 train_dataset = data_generator(train_images, train_masks)
---> 59 val_dataset = data_generator(val_images, val_masks)
     60 
     61 print("Train Dataset:", train_dataset)

~\AppData\Local\Temp\ipykernel_20192\306109049.py in data_generator(image_list, mask_list)
     50 
     51 def data_generator(image_list, mask_list):
---> 52     dataset = tf.data.Dataset.from_tensor_slices((image_list, mask_list))
     53     dataset = dataset.map(load_data, num_parallel_calls=tf.data.experimental.AUTOTUNE)
     54     dataset = dataset.batch(BATCH_SIZE, drop_remainder=True)

~\AppData\Local\Programs\Python\Python37\lib\site-packages\tensorflow\python\data\ops\dataset_ops.py in from_tensor_slices(tensors, name)
    812       Dataset: A `Dataset`.
    813     """
--> 814     return TensorSliceDataset(tensors, name=name)
    815 
    816   class _GeneratorState(object):

~\AppData\Local\Programs\Python\Python37\lib\site-packages\tensorflow\python\data\ops\dataset_ops.py in __init__(self, element, is_files, name)
   4720       batch_dim.assert_is_compatible_with(
   4721           tensor_shape.Dimension(
-> 4722               tensor_shape.dimension_value(t.get_shape()[0])))
   4723 
   4724     variant_tensor = gen_dataset_ops.tensor_slice_dataset(

~\AppData\Local\Programs\Python\Python37\lib\site-packages\tensorflow\python\framework\tensor_shape.py in assert_is_compatible_with(self, other)
    298     if not self.is_compatible_with(other):
    299       raise ValueError("Dimensions %s and %s are not compatible" %
--> 300                        (self, other))
    301 
    302   def merge_with(self, other):

ValueError: Dimensions 37 and 50 are not compatible

错误是“尺寸 37 和 50 不兼容”，我搜索了这个，但找不到解决方案。 代码：

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


IMAGE_SIZE = 512
BATCH_SIZE = 4
NUM_CLASSES = 20
DATA_DIR = r'C:/Users/Joshi/Desktop/CARLA_0.9.13/WindowsNoEditor/PythonAPI/examples/out'
NUM_TRAIN_IMAGES = 250
NUM_VAL_IMAGES = 50


train_images = sorted(glob(os.path.join(DATA_DIR, "out/*")))[:NUM_TRAIN_IMAGES]
train_masks = sorted(glob(os.path.join(DATA_DIR, "Seman/*")))[:NUM_TRAIN_IMAGES]
val_images = sorted(glob(os.path.join(DATA_DIR, "out/*")))[
    NUM_TRAIN_IMAGES : NUM_VAL_IMAGES + NUM_TRAIN_IMAGES
]
val_masks = sorted(glob(os.path.join(DATA_DIR, "Seman/*")))[
    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=1)
        image.set_shape([None, None, 1])
        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.experimental.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)

Answer 1

这只是图片的尺寸不对。