C++ 从darknet使用resnet50.cfg时，opencv dnn显示错误

我用darknet dnn训练我的模型，我用我自己的图片，我用教程

在darknet上测试我的模型时，一切正常，但在dnn opencv上测试时，会显示下一个错误：

错误：在getMemoryShapes文件C:\Users\opencv\sources\modules\dnn\src\layers\eltwise\u layer.cpp的第115行，断言失败（输入[0]==inputs[i]）

错误发生在下面

为什么会发生错误

我的opencv代码：

String modelConfiguration_class = "resnet50.cfg";
String modelWeights_class =  "resnet50_last.weights";
clasificacion = readNetFromDarknet(modelConfiguration_class, modelWeights_class);
clasificacion.setPreferableBackend(DNN_BACKEND_OPENCV);
clasificacion.setPreferableTarget(DNN_TARGET_CPU);
frame = cv::imread("file_path");

Mat blob=blobFromImage(frame, 1.0/255.0, cvSize(256, 256), Scalar(0,0,0), true, false);
clasificacion.setInput(blob);
Mat prob = clasificacion.forward(); #error occurs here

My resnet50.cfg：

[net]
# Training
#batch=128
#subdivisions=64

# Testing
batch=1
subdivisions=1

height=256
width=256
max_crop=448
channels=3
momentum=0.9
decay=0.0005
flip=0
rotate=0

burn_in=1000
learning_rate=0.1
policy=poly
power=4
max_batches=30000

angle=0
hue=.1
saturation=.75
exposure=.75
aspect=.75

[convolutional]
batch_normalize=1
filters=64
size=7
stride=2
pad=1
activation=leaky

[maxpool]
size=2
stride=2

[convolutional]
batch_normalize=1
filters=64
size=1
stride=1
pad=1
activation=leaky

[convolutional]
batch_normalize=1
filters=64
size=3
stride=1
pad=1
activation=leaky

[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=linear

[shortcut]
from=-4
activation=leaky

[convolutional]
batch_normalize=1
filters=64
size=1
stride=1
pad=1
activation=leaky

[convolutional]
batch_normalize=1
filters=64
size=3
stride=1
pad=1
activation=leaky

[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=linear

[shortcut]
from=-4
activation=leaky

[convolutional]
batch_normalize=1
filters=64
size=1
stride=1
pad=1
activation=leaky

[convolutional]
batch_normalize=1
filters=64
size=3
stride=1
pad=1
activation=leaky

[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=linear

[shortcut]
from=-4
activation=leaky

[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky

[convolutional]
batch_normalize=1
filters=128
size=3
stride=2
pad=1
activation=leaky

[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=linear

[shortcut]
from=-4
activation=leaky

[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky

[convolutional]
batch_normalize=1
filters=128
size=3
stride=1
pad=1
activation=leaky

[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=linear

[shortcut]
from=-4
activation=leaky

[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky

[convolutional]
batch_normalize=1
filters=128
size=3
stride=1
pad=1
activation=leaky

[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=linear

[shortcut]
from=-4
activation=leaky

[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky

[convolutional]
batch_normalize=1
filters=128
size=3
stride=1
pad=1
activation=leaky

[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=linear

[shortcut]
from=-4
activation=leaky


# Conv 4
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky

[convolutional]
batch_normalize=1
filters=256
size=3
stride=2
pad=1
activation=leaky

[convolutional]
batch_normalize=1
filters=1024
size=1
stride=1
pad=1
activation=linear

[shortcut]
from=-4
activation=leaky

[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky

[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky

[convolutional]
batch_normalize=1
filters=1024
size=1
stride=1
pad=1
activation=linear

[shortcut]
from=-4
activation=leaky

[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky

[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky

[convolutional]
batch_normalize=1
filters=1024
size=1
stride=1
pad=1
activation=linear

[shortcut]
from=-4
activation=leaky

[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky

[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky

[convolutional]
batch_normalize=1
filters=1024
size=1
stride=1
pad=1
activation=linear

[shortcut]
from=-4
activation=leaky

[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky

[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky

[convolutional]
batch_normalize=1
filters=1024
size=1
stride=1
pad=1
activation=linear

[shortcut]
from=-4
activation=leaky

[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky

[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky

[convolutional]
batch_normalize=1
filters=1024
size=1
stride=1
pad=1
activation=linear

[shortcut]
from=-4
activation=leaky

#Conv 5
[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=leaky

[convolutional]
batch_normalize=1
filters=512
size=3
stride=2
pad=1
activation=leaky

[convolutional]
batch_normalize=1
filters=2048
size=1
stride=1
pad=1
activation=linear

[shortcut]
from=-4
activation=leaky

[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=leaky

[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=leaky

[convolutional]
batch_normalize=1
filters=2048
size=1
stride=1
pad=1
activation=linear

[shortcut]
from=-4
activation=leaky

[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=leaky

[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=leaky

[convolutional]
batch_normalize=1
filters=2048
size=1
stride=1
pad=1
activation=linear

[shortcut]
from=-4
activation=leaky

[convolutional]
filters=4
size=1
stride=1
pad=1
activation=linear

[avgpool]

[softmax]
groups=1

---

您的图像（帧）是灰度格式的，对吗？不是，它是rgb格式的。您是否尝试将其转换为灰度格式？如果我没记错的话，它应该是灰度的来处理。你有没有读过这个？