python之使用 tensorflow.data.Dataset.from_generator 时出现 InvalidArgumentError

我正在尝试生成我自己的图像数据集以在单个 GPU 上使用 Tensorflow Dataset API 测量推理性能

resolutions = [ 
    (2048, 1080) 
] 
 
def generate_image(size, channels): 
    image_value = random.random() 
    image_shape = [1, size[1], size[0], channels] 
    return tf.constant( 
        value=image_value, 
        shape=image_shape, 
        dtype=tf.float32) 
 
def generate_single_input(size): 
    source = generate_image(size, 3) 
    target = generate_image(size, 3) 
    return source, target 
 
def input_generator_fn(): 
    for res in resolutions: 
        for i in range(10): 
            yield generate_single_input(res) 
 
 
def benchmark(): 
    ... 
    ds = tf.data.Dataset.from_generator( 
        generator=input_generator_fn, 
        output_types=(tf.float32, tf.float32), 
        output_shapes=(tf.TensorShape([1, 1080, 2048, 3]), 
                       tf.TensorShape([1, 1080, 2048, 3]))) 
    iterator = ds.make_one_shot_iterator() 
    next_record = iterator.get_next() 
 
    inputs = next_record[0] 
    outputs = next_record[1] 
 
    predictions = { 
        'input_images': inputs 
        'output_images': outputs 
    } 
    session = tf.Session() 
    with session: 
        tf.global_variables_initializer() 
        for res in resolutions: 
           for i in range(10): 
               session.run(predictions) 
               ..... 

但我在运行后观察到以下异常:

2018-04-06 13:38:44.050448: W tensorflow/core/framework/op_kernel.cc:1198] Invalid argument: ValueError: setting an array element with a sequence. 
 
2018-04-06 13:38:44.050581: W tensorflow/core/framework/op_kernel.cc:1198]   Invalid argument: ValueError: setting an array element with a sequence. 
     [[Node: PyFunc = PyFunc[Tin=[DT_INT64], Tout=[DT_FLOAT, DT_FLOAT], token="pyfunc_1"](arg0)]] 
 
Traceback (most recent call last): 
File "tensorflow/lib/python3.5/site-packages/tensorflow/python/client/session.py", line 1350, in _do_call 
    return fn(*args) 
 
File "tensorflow/lib/python3.5/site-packages/tensorflow/python/client/session.py", line 1329, in _run_fn 
    status, run_metadata) 
 
File "tensorflow/lib/python3.5/site-packages/tensorflow/python/framework/errors_impl.py", line 473, in __exit__ 
    c_api.TF_GetCode(self.status.status)) 
    tensorflow.python.framework.errors_impl.InvalidArgumentError: ValueError: setting an array element with a sequence. 
     [[Node: PyFunc = PyFunc[Tin=[DT_INT64], Tout=[DT_FLOAT, DT_FLOAT], token="pyfunc_1"](arg0)]] 
     [[Node: IteratorGetNext = IteratorGetNext[output_shapes=[[1,1080,2048,3], [1,1080,2048,3]], output_types=[DT_FLOAT, DT_FLOAT], _device="/job:localhost/replica:0/task:0/device:CPU:0"](OneShotIterator)]] 

请您参考如下方法:

简而言之，原因是 from_generator 可以展平 NumPy 数组，但不能展平 Tensor。

这是一个较短的代码，可以重现错误:

import tensorflow as tf 
import numpy as np 
 
print(tf.__version__) 
def g(): 
  img = tf.random_uniform([3]) 
  # img = np.random.rand(3) 
  # img = tf.convert_to_tensor(img) 
  yield img 
 
dataset = tf.data.Dataset.from_generator(g, tf.float64, tf.TensorShape([3])) 
iterator = dataset.make_one_shot_iterator() 
next_iterator = iterator.get_next() 
 
sess = tf.Session() 
sess.run(next_iterator) 

1.14 版本中的错误信息很有帮助。 (确切的代码行会因版本不同而有所不同，但我检查过我使用的1.12和1.13原因是一样的。)

InvalidArgumentError: TypeError: `generator` yielded an element that could not be converted to the expected type. The expected type was float64, but the yielded element was Tensor("random_uniform:0", shape=(3,), dtype=float32). 
Traceback (most recent call last): 
 
  File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/data/ops/dataset_ops.py", line 530, in generator_py_func 
    ret, dtype=dtype.as_numpy_dtype)) 
 
  File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/script_ops.py", line 169, in _convert 
    result = np.asarray(value, dtype=dtype, order="C") 
 
  File "/usr/local/lib/python3.6/dist-packages/numpy/core/numeric.py", line 538, in asarray 
    return array(a, dtype, copy=False, order=order) 
 
ValueError: setting an array element with a sequence.  

当生成的元素是 Tensor 时，from_generator 会将其展平为 output_types。转换功能不起作用。

要解决此问题，只需在生成器生成张量时不要使用 from_generator 即可。您可以使用 from_tensors 或 from_tensor_slices。

img = tf.random_uniform([3]) 
 
dataset = tf.data.Dataset.from_tensors(img).repeat() 
iterator = dataset.make_initializable_iterator() 
next_iterator = iterator.get_next() 
 
sess = tf.Session() 
sess.run(iterator.initializer) 
sess.run(next_iterator)