数据挖掘 - 为什么 TensorFlow 将我的解码图像转换为 float32 而不是 uint8/16？ - 吾爱随笔录

我正在使用以下代码导入一堆 .png 图像并使用 TensorFlow 对其进行解码：

from __future__ import absolute_import, division, print_function
import tensorflow as tf
import numpy as np
import os

tf.enable_eager_execution()

NUM_TRAINING_SAMPLES = 333
NUM_CLASSES = 3
BATCH_SIZE = 5
NUM_EPOCHS = 6
INPUT_SIZE = (256, 256, 3)

random_indices = np.random.choice(range(13000), NUM_TRAINING_SAMPLES)
directory = "/home/local/CYCLOMEDIA001/ebos/Downloads/SYNTHIA_RAND_CVPR16"
directory_images = "/home/Downloads/SYNTHIA_RAND_CVPR16/RGB"
directory_labels = "/home/Downloads/SYNTHIA_RAND_CVPR16/GT"
train_images = np.array(os.listdir(directory_images))
train_labels = np.array(os.listdir(directory_images))
train_images = train_images[random_indices]
train_labels = train_labels[random_indices]
train_images = [tf.read_file(os.path.join(directory_images, img)) for img in train_images]
train_labels = [tf.read_file(os.path.join(directory_labels, img)) for img in train_labels]
train_images = [tf.io.decode_image(img, channels=3) for img in train_images]
train_labels = [tf.io.decode_image(img, channels=3) for img in train_labels]
train_images = tf.image.resize_images(train_images, INPUT_SIZE[:2])
train_labels = tf.image.resize_images(train_labels, INPUT_SIZE[:2])

train_dataset = tf.data.Dataset.from_tensor_slices((train_images, train_labels))
train_dataset = train_dataset.batch(3)
print(train_dataset.output_types)

这将返回：

(tf.float32, tf.float32)

但是，根据文档，它应该返回一个 uint8 或 uint16 的张量。为什么以及在哪里进行转换？

我用打印语句检查了所有中间步骤，这并没有告诉我太多，因为大多数中间列表都属于“tensorflow.python.framework.ops.EagerTensor”类。