在代码中添加以下内容：

from tensorflow.compat.v1 import ConfigProto
from tensorflow.compat.v1 import InteractiveSession

config = ConfigProto()
config.gpu_options.allow_growth = True
session = InteractiveSession(config=config)

然后重新启动Python内核。

有同样的问题。
MaxPooling的padding='same'不适合我。
我将训练和测试生成器中的color_mode参数从'rgb'更改为'grayscale'，然后它对我有效。

这对我很有效：

import tensorflow as tf
    physical_devices = tf.config.list_physical_devices('GPU')
    tf.config.experimental.set_memory_growth(physical_devices[0], True)

在我的例子中，这是通过结束所有进程来解决的，这些进程仍然在其中一个GPU上分配内存。显然，其中一个没有完成（正确）。我不需要修改任何代码。

我的问题是，我调用了模型的input_shape（？28.第28章：你叫他什么？、28、28、3）。

作为参考，修复错误的完整代码如下：

import tensorflow.keras
from tensorflow.keras.models import *
from tensorflow.keras.layers import *

IMAGE_ORDERING = 'channels_last'

# take vgg-16 pretrained model from "https://github.com/fchollet/deep-learning-models" here
pretrained_url = "https://github.com/fchollet/deep-learning-models/" \
                 "releases/download/v0.1/" \
                 "vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5"

pretrained = 'imagenet'  # 'imagenet' if weights need to be initialized!

"""
Function Name: get_vgg_encoder()
Functionalities: This function defines the VGG encoder part of the FCN network
                 and initialize this encoder part with VGG pretrained weights.
Parameter:input_height=224,  input_width=224, pretrained=pretrained
Returns: final layer of every blocks as f1,f2,f3,f4,f5
"""

def get_vgg_encoder(input_height=224, input_width=224, pretrained=pretrained):
    pad = 1

    # heights and weights must be divided by 32, for fcn
    assert input_height % 32 == 0
    assert input_width % 32 == 0

    img_input = Input(shape=(input_height, input_width, 3))

    # Unlike base paper, stride=1 has not been used here, because
    # Keras has default stride=1

    x = (ZeroPadding2D((pad, pad), data_format=IMAGE_ORDERING))(img_input)
    x = Conv2D(64, (3, 3), activation='relu', padding='valid', name='block1_conv1', data_format=IMAGE_ORDERING)(x)
    x = Conv2D(64, (3, 3), activation='relu', padding='same', name='block1_conv2', data_format=IMAGE_ORDERING)(x)
    x = MaxPooling2D((2, 2), strides=(2, 2), name='block1_pool', data_format=IMAGE_ORDERING)(x)
    f1 = x
    # Block 2
    x = Conv2D(128, (3, 3), activation='relu', padding='same', name='block2_conv1', data_format=IMAGE_ORDERING)(x)
    x = Conv2D(128, (3, 3), activation='relu', padding='same', name='block2_conv2', data_format=IMAGE_ORDERING)(x)
    x = MaxPooling2D((2, 2), strides=(2, 2), name='block2_pool', data_format=IMAGE_ORDERING)(x)
    f2 = x

    # Block 3
    x = Conv2D(256, (3, 3), activation='relu', padding='same', name='block3_conv1', data_format=IMAGE_ORDERING)(x)
    x = Conv2D(256, (3, 3), activation='relu', padding='same', name='block3_conv2', data_format=IMAGE_ORDERING)(x)
    x = Conv2D(256, (3, 3), activation='relu', padding='same', name='block3_conv3', data_format=IMAGE_ORDERING)(x)
    x = MaxPooling2D((2, 2), strides=(2, 2), name='block3_pool', data_format=IMAGE_ORDERING)(x)
    x = Dropout(0.5)(x)
    f3 = x

    # Block 4
    x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block4_conv1', data_format=IMAGE_ORDERING)(x)
    x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block4_conv2', data_format=IMAGE_ORDERING)(x)
    x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block4_conv3', data_format=IMAGE_ORDERING)(x)
    x = MaxPooling2D((2, 2), strides=(2, 2), name='block4_pool', data_format=IMAGE_ORDERING)(x)
    f4 = x

    # Block 5
    x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block5_conv1', data_format=IMAGE_ORDERING)(x)
    x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block5_conv2', data_format=IMAGE_ORDERING)(x)
    x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block5_conv3', data_format=IMAGE_ORDERING)(x)
    x = MaxPooling2D((2, 2), strides=(2, 2), name='block5_pool', data_format=IMAGE_ORDERING)(x)
    # x= Dropout(0.5)(x)

    f5 = x

    # Check if weights are initialised, model is learning!
    if pretrained == 'imagenet':
        VGG_Weights_path = tensorflow.keras.utils.get_file(
            pretrained_url.split("/")[-1], pretrained_url)

        Model(img_input, x).load_weights(VGG_Weights_path)

    return img_input, [f1, f2, f3, f4, f5]

"""
Function Name: fcn_16()
Functionalities: This function defines the Fully Convolutional part of the FCN network
                 and adds skip connections to build FCN-16 network
Parameter:n_classes, encoder=get_vgg_encoder, input_height=224,input_width=224
Returns: model
"""

def fcn_16(n_classes, encoder=get_vgg_encoder, input_height=224, input_width=224):
    # Take levels from the base model, i.e. vgg
    img_input, levels = encoder(input_height=input_height, input_width=input_width)
    [f1, f2, f3, f4, f5] = levels

    o = f5

    # fcn6
    o = (Conv2D(4096, (7, 7), activation='relu', padding='same', data_format=IMAGE_ORDERING))(o)
    o = Dropout(0.5)(o)

    # fc7
    o = (Conv2D(4096, (1, 1), activation='relu', padding='same', data_format=IMAGE_ORDERING))(o)
    o = Dropout(0.3)(o)

    conv7 = (Conv2D(1, (1, 1), activation='relu', padding='same', name="score_sal", data_format=IMAGE_ORDERING))(o)

    conv7_4 = Conv2DTranspose(1, kernel_size=(4, 4), strides=(2, 2), padding='same', name="upscore_sal2",
                              use_bias=False, data_format=IMAGE_ORDERING)(conv7)

    pool411 = (
        Conv2D(1, (1, 1), activation='relu', padding='same', name="score_pool4", data_format=IMAGE_ORDERING))(f4)

    # Add a crop layer 
    o, o2 = crop(pool411, conv7_4, img_input)

    # add skip connection
    o = Add()([o, o2])

    # 16 x upsample
    o = Conv2DTranspose(n_classes, kernel_size=(32, 32), strides=(16, 16), use_bias=False, data_format=IMAGE_ORDERING)(
        o)

    # crop layer
    ## Caffe calls crop layer that takes o and img_input as argument, it takes their difference and crops
    ## But keras takes it as touple, I checked the size diff and put this value manually.
    ## output dim was 240 , input dim was 224. 240-224=16. so 16/2=8

    score = Cropping2D(cropping=((8, 8), (8, 8)), data_format=IMAGE_ORDERING)(o)

    o = (Activation('sigmoid'))(score)
    model = Model(img_input, o)

    model.model_name = "fcn_16"

    return model

这个错误是相当普遍的，基本上表明“某些东西”出错了。因为，各种各样的答案表明，错误可能是由于实现与keras/tensorflow的底层版本不兼容，或者过滤器大小不正确，或者......
没有单一的解决办法。对我来说，这也是一个输入形状的问题。而不是使用rgb使用grayscale作为网络预期的1个通道。

就像@Soren说的，这个错误取决于各种情况。这可能是由于VRAM缺陷、v1和v2不兼容、调用conv或pool时的形状问题等。
在我的例子中，出现这个错误是因为我在Python 3.6（和TF 2.4.2）中调用保存的模型进行推理，而我的模型是在Python 3.10（和TF 2.8）中训练的，并且Keras/TF的函数API中完成的一些灵活的形状操作与旧版本的TF不向后兼容。
因此，我还建议检查您的训练和推理环境，以确保没有错误或不匹配。