问题描述
限时送ChatGPT账号..我正在使用 TF 2.2,我正在尝试使用 tf.data 创建管道.
I'm using TF 2.2 and I'm trying to use tf.data to create a pipeline.
以下工作正常:
def load_image(filePath, label):
print('Loading File: {}' + filePath)
raw_bytes = tf.io.read_file(filePath)
image = tf.io.decode_image(raw_bytes, expand_animations = False)
return image, label
# TrainDS Pipeline
trainDS = getDataset()
trainDS = trainDS.shuffle(size['train'])
trainDS = trainDS.map(load_image, num_parallel_calls=AUTOTUNE)
for d in trainDS:
print('Image: {} - Label: {}'.format(d[0], d[1]))
我想将 load_image()
与 Dataset.interleave()
一起使用.然后我尝试了:
I would like to use the load_image()
with the Dataset.interleave()
. Then I tried:
# TrainDS Pipeline
trainDS = getDataset()
trainDS = trainDS.shuffle(size['train'])
trainDS = trainDS.interleave(lambda x, y: load_image_with_label(x, y), cycle_length=4)
for d in trainDS:
print('Image: {} - Label: {}'.format(d[0], d[1]))
但我收到以下错误:
Exception has occurred: TypeError
`map_func` must return a `Dataset` object. Got <class 'tuple'>
File "/data/dev/train_daninhas.py", line 44, in <module>
trainDS = trainDS.interleave(lambda x, y: load_image_with_label(x, y), cycle_length=4)
如何调整我的代码以让 Dataset.interleave()
与 load_image()
一起工作以并行读取图像?
How can I adapt my code to have the Dataset.interleave()
working with the load_image()
to read the images in parallel ?
推荐答案
如错误提示,您需要修改 load_image
使其返回一个 Dataset
对象,我在 tensorflow 2.2.0
中展示了一个带有两张图片的示例:
As the error suggests, you need to modify the load_image
so that it return a Dataset
object, I have shown an example with two images on how to go about doing it in tensorflow 2.2.0
:
import tensorflow as tf
filenames = ["./img1.jpg", "./img2.jpg"]
labels = ["A", "B"]
def load_image(filePath, label):
print('Loading File: {}' + filePath)
raw_bytes = tf.io.read_file(filePath)
image = tf.io.decode_image(raw_bytes, expand_animations = False)
return tf.data.Dataset.from_tensors((image, label))
dataset = tf.data.Dataset.from_tensor_slices((filenames, labels))
dataset = dataset.interleave(lambda x, y: load_image(x, y), cycle_length=4)
for i in dataset.as_numpy_iterator():
image = i[0]
label = i[1]
print(image.shape)
print(label.decode())
# (275, 183, 3)
# A
# (275, 183, 3)
# B
希望这会有所帮助!
这篇关于如何使用自定义函数在 TF 2 中使用 tf.data.Dataset.interleave()?的文章就介绍到这了,希望我们推荐的答案对大家有所帮助,也希望大家多多支持IT屋!
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