What is Dimensions 37 and 50 in Tensorflow?

Question

Hello I am working on semantic segmentation with the DeeplabV3Plus architecture and Tensorflow (Keras). I did it well with another dataset but now I want to do it with my own. But in the first step of loading the data, it shows me a strange error. The function is

tf.data.Dataset.from_tensor_slices

and the error is:

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

The Error is "Dimensions 37 and 50 are not compatible", I searched for this, but cannot find a solution. Code:

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

It just was the wrong size for the picture.