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Failed to compile the VGG face model, always get this error: *** Error in `python3': free(): invalid pointer: 0x0000000002784b80 ***.
Using this command: mvNCCompile VGG_FACE_deploy.prototxt -w VGG_FACE.caffemodel -s 12 -in conv1_1 -on fc8 -is 224 224 -o VGGNet.graph
And here follows the prototxt file:
==========VGG_FACE_deploy.prototxt==========
name: "VGG_FACE_16_layers"
input: "data"
input_shape {
dim: 1
dim: 3
dim: 224
dim: 224
}
layers {
bottom: "data"
top: "conv1_1"
name: "conv1_1"
type: CONVOLUTION
convolution_param {
num_output: 64
pad: 1
kernel_size: 3
}
}
layers {
bottom: "conv1_1"
top: "conv1_1"
name: "relu1_1"
type: RELU
}
layers {
bottom: "conv1_1"
top: "conv1_2"
name: "conv1_2"
type: CONVOLUTION
convolution_param {
num_output: 64
pad: 1
kernel_size: 3
}
}
layers {
bottom: "conv1_2"
top: "conv1_2"
name: "relu1_2"
type: RELU
}
layers {
bottom: "conv1_2"
top: "pool1"
name: "pool1"
type: POOLING
pooling_param {
pool: MAX
kernel_size: 2
stride: 2
}
}
layers {
bottom: "pool1"
top: "conv2_1"
name: "conv2_1"
type: CONVOLUTION
convolution_param {
num_output: 128
pad: 1
kernel_size: 3
}
}
layers {
bottom: "conv2_1"
top: "conv2_1"
name: "relu2_1"
type: RELU
}
layers {
bottom: "conv2_1"
top: "conv2_2"
name: "conv2_2"
type: CONVOLUTION
convolution_param {
num_output: 128
pad: 1
kernel_size: 3
}
}
layers {
bottom: "conv2_2"
top: "conv2_2"
name: "relu2_2"
type: RELU
}
layers {
bottom: "conv2_2"
top: "pool2"
name: "pool2"
type: POOLING
pooling_param {
pool: MAX
kernel_size: 2
stride: 2
}
}
layers {
bottom: "pool2"
top: "conv3_1"
name: "conv3_1"
type: CONVOLUTION
convolution_param {
num_output: 256
pad: 1
kernel_size: 3
}
}
layers {
bottom: "conv3_1"
top: "conv3_1"
name: "relu3_1"
type: RELU
}
layers {
bottom: "conv3_1"
top: "conv3_2"
name: "conv3_2"
type: CONVOLUTION
convolution_param {
num_output: 256
pad: 1
kernel_size: 3
}
}
layers {
bottom: "conv3_2"
top: "conv3_2"
name: "relu3_2"
type: RELU
}
layers {
bottom: "conv3_2"
top: "conv3_3"
name: "conv3_3"
type: CONVOLUTION
convolution_param {
num_output: 256
pad: 1
kernel_size: 3
}
}
layers {
bottom: "conv3_3"
top: "conv3_3"
name: "relu3_3"
type: RELU
}
layers {
bottom: "conv3_3"
top: "pool3"
name: "pool3"
type: POOLING
pooling_param {
pool: MAX
kernel_size: 2
stride: 2
}
}
layers {
bottom: "pool3"
top: "conv4_1"
name: "conv4_1"
type: CONVOLUTION
convolution_param {
num_output: 512
pad: 1
kernel_size: 3
}
}
layers {
bottom: "conv4_1"
top: "conv4_1"
name: "relu4_1"
type: RELU
}
layers {
bottom: "conv4_1"
top: "conv4_2"
name: "conv4_2"
type: CONVOLUTION
convolution_param {
num_output: 512
pad: 1
kernel_size: 3
}
}
layers {
bottom: "conv4_2"
top: "conv4_2"
name: "relu4_2"
type: RELU
}
layers {
bottom: "conv4_2"
top: "conv4_3"
name: "conv4_3"
type: CONVOLUTION
convolution_param {
num_output: 512
pad: 1
kernel_size: 3
}
}
layers {
bottom: "conv4_3"
top: "conv4_3"
name: "relu4_3"
type: RELU
}
layers {
bottom: "conv4_3"
top: "pool4"
name: "pool4"
type: POOLING
pooling_param {
pool: MAX
kernel_size: 2
stride: 2
}
}
layers {
bottom: "pool4"
top: "conv5_1"
name: "conv5_1"
type: CONVOLUTION
convolution_param {
num_output: 512
pad: 1
kernel_size: 3
}
}
layers {
bottom: "conv5_1"
top: "conv5_1"
name: "relu5_1"
type: RELU
}
layers {
bottom: "conv5_1"
top: "conv5_2"
name: "conv5_2"
type: CONVOLUTION
convolution_param {
num_output: 512
pad: 1
kernel_size: 3
}
}
layers {
bottom: "conv5_2"
top: "conv5_2"
name: "relu5_2"
type: RELU
}
layers {
bottom: "conv5_2"
top: "conv5_3"
name: "conv5_3"
type: CONVOLUTION
convolution_param {
num_output: 512
pad: 1
kernel_size: 3
}
}
layers {
bottom: "conv5_3"
top: "conv5_3"
name: "relu5_3"
type: RELU
}
layers {
bottom: "conv5_3"
top: "pool5"
name: "pool5"
type: POOLING
pooling_param {
pool: MAX
kernel_size: 2
stride: 2
}
}
layers {
bottom: "pool5"
top: "fc6"
name: "fc6"
type: INNER_PRODUCT
inner_product_param {
num_output: 4096
}
}
layers {
bottom: "fc6"
top: "fc6"
name: "relu6"
type: RELU
}
layers {
bottom: "fc6"
top: "fc6"
name: "drop6"
type: DROPOUT
dropout_param {
dropout_ratio: 0.5
}
}
layers {
bottom: "fc6"
top: "fc7"
name: "fc7"
type: INNER_PRODUCT
inner_product_param {
num_output: 4096
}
}
layers {
bottom: "fc7"
top: "fc7"
name: "relu7"
type: RELU
}
layers {
bottom: "fc7"
top: "fc7"
name: "drop7"
type: DROPOUT
dropout_param {
dropout_ratio: 0.5
}
}
layers {
bottom: "fc7"
top: "fc8"
name: "fc8"
type: INNER_PRODUCT
inner_product_param {
num_output: 2622
}
}
layers {
bottom: "fc8"
top: "prob"
name: "prob"
type: SOFTMAX
}
- Tags:
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@fengping I would like to help you troubleshoot this issue further and to do so it would be helpful if you could provide a link to the caffe model weights file that you used to compile your graph file. Thanks.
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I met the same issue. It 's related to Python3 and OpenCV 3.3.
Please make your openCV again with this parameter as below again.
-D BUILD_opencv_cnn_3dobj=OFF
-D BUILD_opencv_dnn_modern=OFF
sudo cp cv2.cpython-35m-x86_64-linux-gnu.so /usr/local/lib/python3.5/dist-packages/cv2/
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@Tom_at_Intel I think it's about my Python environment, I can successfully compile the pre-trained model in a new installed machine. So, it's not such an emergency. And I will try to follow @rouyunpan 's suggestion to reinstall opencv.
I guess it may caused by Anaconda which is suggested when Caffe installation.
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@fengping In the past, there has been a compatibility issue with Anaconda. Try removing Anaconda and see if that helps and please let us know if you fixed your issue. Thanks.
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@Tome_at_Intel I didn't fixed this issue no matter using recompile opencv or uninstall Anaconda, but I noticed that the graph file had already generated. There maybe some confliction but I haven't found it out. I will let you know as soon as I figure it out. Thank you.
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@fengping I ran into the same issue and it's indeed related to Python and OpenCV.
I used another NUC to install everything fresh and I don't get this error anymore.
Let us know, if it's solved for you. Otherwise, I have a VGG graph file compiled without an error. So, if you need any help, just let me know! :)
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