TypeError：“函数”接受1个位置参数，但给出了2个

如何解决TypeError：“函数”接受1个位置参数，但给出了2个

我想应用下面的函数，它负责放大每个图像并对其进行转换：

def color_distortion(image,s=1.0):
    # image is a tensor with value range in [0,1].
    # s is the strength of color distortion.

    def color_jitter(x):
        # one can also shuffle the order of following augmentations
        # each time they are applied.
        x = tf.image.random_brightness(x,max_delta=0.8 * s)
        x = tf.image.random_contrast(x,lower=1 - 0.8 * s,upper=1 + 0.8 * s)
        x = tf.image.random_saturation(x,upper=1 + 0.8 * s)
        x = tf.image.random_hue(x,max_delta=0.2 * s)
        x = tf.clip_by_value(x,1)
        return x

    def color_drop(x):
        x = tf.image.rgb_to_grayscale(x)
        x = tf.tile(x,[1,1,3])
        return x

    rand_ = tf.random.uniform(shape=(),minval=0,maxval=1)
    # randomly apply transformation with probability p.
    if rand_ < 0.8:
        image = color_jitter(image)

    rand_ = tf.random.uniform(shape=(),maxval=1)
    if rand_ < 0.2:
        image = color_drop(image)
    return image

def distort_simclr(image):
    image = tf.cast(image,tf.float32)
    v1 = color_distortion(image / 255.)
    v2 = color_distortion(image / 255.)
    return v1,v2

在像波纹管一样导入的数据集上

training_set = tf.data.Dataset.from_generator(path,output_types=(tf.float32,tf.float32),output_shapes = ([2,224,3],[2,2]))

所以我写这个：

training_set = training_set.map(distort_simclr,num_parallel_calls=tf.data.experimental.AUTOTUNE)

我找到了：

tf__distort_simclr() takes 1 positional argument but 2 were given

这是我的数据集的一个示例：

img_gen = tf.keras.preprocessing.image.ImageDataGenerator()
gen = img_gen.flow_from_directory('/train/',(224,224),'rgb',batch_size = 2)
training_set = tf.data.Dataset.from_generator(lambda : gen,2]))

解决方法

由于training_set有2个元素，而只向功能distort_simclr传递了一个元素，因此您收到此错误。

下面是一个简单的代码，可以重现您的错误-

错误代码-

import itertools
import tensorflow as tf

def gen():
  for i in itertools.count(1):
    yield (i,[1] * i)

dataset = tf.data.Dataset.from_generator(
     gen,(tf.int64,tf.int64),(tf.TensorShape([]),tf.TensorShape([None])))

print(dataset)

def doNothing(i):
    return i

dataset = dataset.map(doNothing)

list(dataset.take(3).as_numpy_iterator())

输出-

<FlatMapDataset shapes: ((),(None,)),types: (tf.int64,tf.int64)>
---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-24-27a58aace75c> in <module>()
     15     return i
     16 
---> 17 dataset = dataset.map(doNothing)
     18 
     19 list(dataset.take(3).as_numpy_iterator())

10 frames
/usr/local/lib/python3.6/dist-packages/tensorflow/python/autograph/impl/api.py in wrapper(*args,**kwargs)
    256       except Exception as e:  # pylint:disable=broad-except
    257         if hasattr(e,'ag_error_metadata'):
--> 258           raise e.ag_error_metadata.to_exception(e)
    259         else:
    260           raise

TypeError: in user code:


    TypeError: tf__doNothing() takes 1 positional argument but 2 were given

要纠正错误，请将两个元素都传递给函数。

固定代码-

import itertools
import tensorflow as tf

def gen():
  for i in itertools.count(1):
    yield (i,tf.TensorShape([None])))

print(dataset)

def doNothing(i,j):
    return i,j

dataset = dataset.map(doNothing)

list(dataset.take(3).as_numpy_iterator())

输出-

<FlatMapDataset shapes: ((),tf.int64)>
[(1,array([1])),(2,array([1,1])),(3,1,1]))]