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使用数据集API创建图像数据集时,出现“typeerror:expected bytes,str found”

tensorflow-datasets tensorflow python

0

Colonder · 技术社区 · 7 年前

我想使用DataSetAPI从我的图像中创建一个TensorFlow的数据集。这些图像被组织成一个复杂的层次结构,但在最后,总是有两个目录“假”和“真”。我写了这段代码

import tensorflow as tf
from tensorflow.data import Dataset
import os

def enumerate_all_files(rootdir):
    for subdir, dir, files in os.walk(rootdir):
        for file in files:
            # return path to the file and its label
            # label is simply a 1 or 0 depending on whether an image is in the "Genuine" folder or not
            yield os.path.join(subdir, file), int(subdir.split(os.path.sep)[-1] == "Genuine")

def input_parser(img_path, label):
    # convert the label to one-hot encoding
    one_hot = tf.one_hot(label, 2)
    # read the img from file
    img_file = tf.read_file(img_path)
    img_decoded = tf.image.decode_png(img_file, channels=3)
    return img_decoded, one_hot

def get_dataset():
    generator = lambda: enumerate_all_files("/tmp/images/training/")
    dataset = Dataset.from_generator(generator, (tf.string, tf.int32)).shuffle(1000).batch(100)
    dataset = dataset.map(input_parser)
    return dataset

但是,当我在终端运行它时,

tf.enable_eager_execution()
# all the code above
d = get_dataset()
for f in d.make_one_shot_iterator():
    print(f)

它因出错而崩溃

W tensorflow/core/framework/op_kernel.cc:1306] Unknown: SystemError: <weakref at 0x7ff8232f0620; to 'function' at 0x7ff8233c9048 (generator_py_func)> returned a result with an error set
TypeError: expected bytes, str found  

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
  File "lcnn.py", line 29, in <module>
    for f in d.make_one_shot_iterator():
  File "/opt/anaconda3/envs/tensorflow/lib/python3.6/site-packages/tensorflow/python/data/ops/iterator_ops.py", line 487, in __next__
    return self.next()
  File "/opt/anaconda3/envs/tensorflow/lib/python3.6/site-packages/tensorflow/python/data/ops/iterator_ops.py", line 518, in next
    return self._next_internal()
  File "/opt/anaconda3/envs/tensorflow/lib/python3.6/site-packages/tensorflow/python/data/ops/iterator_ops.py", line 508, in _next_internal
    output_shapes=self._flat_output_shapes)
  File "/opt/anaconda3/envs/tensorflow/lib/python3.6/site-packages/tensorflow/python/ops/gen_dataset_ops.py", line 1848, in iterator_get_next_sync
    "output_types", output_types, "output_shapes", output_shapes)
SystemError: <built-in function TFE_Py_FastPathExecute> returned a result with an error set

我在这里做错什么了?

编辑
我试着不打电话运行代码 map , shuffle 和 batch 以及评论 input_parser 但还是出现了错误。

编辑2
我变了 Dataset.from_generator 到 Dataset.from_tensor_slices 看看打开图片的代码是否有效。所以更改后的代码看起来像

def input_parser(img_path):
    # convert the label to one-hot encoding
    # one_hot = tf.one_hot(label, 2)
    # read the img from file
    img_file = tf.read_file(img_path)
    img_decoded = tf.image.decode_png(img_file, channels=3)
    return img_decoded

def get_dataset():
    dataset = Dataset.from_tensor_slices(["/tmp/images/training/1000010.png"]).map(input_parser).shuffle(1000).batch(100)
    return dataset

不过这个很好用

2 回复 | 直到 7 年前

1

0

sdcbr 7 年前

def get_dataset():
    generator = lambda: enumerate_all_files("/tmp/images/training/")
    dataset = Dataset.from_generator(generator, (tf.string,tf.int32)).map(input_parser)
    dataset = dataset.shuffle(1000).batch(100)
    return dataset

2

0

Colonder 7 年前

Dataset.from_generator Dataset.from_tensor_slices

import tensorflow as tf
from tensorflow.data import Dataset
import os
tf.enable_eager_execution()

def enumerate_all_files(rootdir):
    for subdir, dir, files in os.walk(rootdir):
        for file in files:
            # return path to the file and its label
            # label is simply a 1 or 0 depending on whether an image is in the "Genuine" folder or not
            yield os.path.join(subdir, file), int(subdir.split(os.path.sep)[-1] == "Genuine")

def input_parser(img_path, label):
    # convert the label to one-hot encoding
    one_hot = tf.one_hot(label, 2)
    # read the img from file
    img_file = tf.read_file(img_path)
    img_decoded = tf.image.decode_png(img_file, channels=3)
    return img_decoded, one_hot

def get_dataset():

    file_paths = []
    labels = []

    for i in enumerate_all_files("/media/kuba/Seagate Expansion Drive/MGR/Spektrogramy/FFT/training/"):
        file_paths.append(i[0])
        labels.append(i[1])
    dataset = Dataset.from_tensor_slices((file_paths, labels)).map(input_parser).shuffle(1000).batch(100)
    return dataset

d = get_dataset()
for f in d.make_one_shot_iterator():
    print(type(f))