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[Error 25] Myriad Error: "Major or Minor Slices of MatMul are zero".

name: "j"
layer {
name: "data"
type: "Input"
top: "data"
input_param {
shape {
dim: 1
dim: 3
dim: 224
dim: 224
}
}
}
layer {
name: "conv1"
type: "Convolution"
bottom: "data"
top: "conv1"
convolution_param {
num_output: 64
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
}
pad_h: 4
pad_w: 4
kernel_h: 7
kernel_w: 7
stride_h: 2
stride_w: 2
}
}
layer {
name: "relu2"
type: "ReLU"
bottom: "conv1"
top: "conv1"
}
layer {
name: "pool3"
type: "Pooling"
bottom: "conv1"
top: "pool3"
pooling_param {
pool: MAX
kernel_h: 3
kernel_w: 3
stride_h: 3
stride_w: 3
pad_h: 0
pad_w: 0
}
}
layer {
name: "conv4"
type: "Convolution"
bottom: "pool3"
top: "conv4"
convolution_param {
num_output: 128
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
}
pad_h: 1
pad_w: 1
kernel_h: 2
kernel_w: 2
stride_h: 1
stride_w: 1
}
}
layer {
name: "relu5"
type: "ReLU"
bottom: "conv4"
top: "conv4"
}
layer {
name: "conv6"
type: "Convolution"
bottom: "conv4"
top: "conv6"
convolution_param {
num_output: 128
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
}
pad_h: 0
pad_w: 0
kernel_h: 2
kernel_w: 2
stride_h: 1
stride_w: 1
}
}
layer {
name: "relu7"
type: "ReLU"
bottom: "conv6"
top: "conv6"
}
layer {
name: "conv8"
type: "Convolution"
bottom: "conv6"
top: "conv8"
convolution_param {
num_output: 128
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
}
pad_h: 0
pad_w: 0
kernel_h: 2
kernel_w: 2
stride_h: 1
stride_w: 1
}
}
layer {
name: "relu9"
type: "ReLU"
bottom: "conv8"
top: "conv8"
}
layer {
name: "conv10"
type: "Convolution"
bottom: "conv8"
top: "conv10"
convolution_param {
num_output: 128
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
}
pad_h: 0
pad_w: 0
kernel_h: 2
kernel_w: 2
stride_h: 1
stride_w: 1
}
}
layer {
name: "relu11"
type: "ReLU"
bottom: "conv10"
top: "conv10"
}
layer {
name: "pool12"
type: "Pooling"
bottom: "conv10"
top: "pool12"
pooling_param {
pool: MAX
kernel_h: 2
kernel_w: 2
stride_h: 2
stride_w: 2
pad_h: 0
pad_w: 0
}
}
layer {
name: "conv13"
type: "Convolution"
bottom: "pool12"
top: "conv13"
convolution_param {
num_output: 256
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
}
pad_h: 1
pad_w: 1
kernel_h: 2
kernel_w: 2
stride_h: 1
stride_w: 1
}
}
layer {
name: "relu14"
type: "ReLU"
bottom: "conv13"
top: "conv13"
}
layer {
name: "conv15"
type: "Convolution"
bottom: "conv13"
top: "conv15"
convolution_param {
num_output: 256
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
}
pad_h: 1
pad_w: 1
kernel_h: 2
kernel_w: 2
stride_h: 1
stride_w: 1
}
}
layer {
name: "relu16"
type: "ReLU"
bottom: "conv15"
top: "conv15"
}
layer {
name: "conv17"
type: "Convolution"
bottom: "conv15"
top: "conv17"
convolution_param {
num_output: 256
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
}
pad_h: 0
pad_w: 0
kernel_h: 2
kernel_w: 2
stride_h: 1
stride_w: 1
}
}
layer {
name: "relu18"
type: "ReLU"
bottom: "conv17"
top: "conv17"
}
layer {
name: "conv19"
type: "Convolution"
bottom: "conv17"
top: "conv19"
convolution_param {
num_output: 256
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
}
pad_h: 0
pad_w: 0
kernel_h: 2
kernel_w: 2
stride_h: 1
stride_w: 1
}
}
layer {
name: "relu20"
type: "ReLU"
bottom: "conv19"
top: "conv19"
}
layer {
name: "pool21"
type: "Pooling"
bottom: "conv19"
top: "pool21"
pooling_param {
pool: MAX
kernel_h: 3
kernel_w: 3
stride_h: 3
stride_w: 3
pad_h: 0
pad_w: 0
}
}
layer {
name: "conv22"
type: "Convolution"
bottom: "pool21"
top: "conv22"
convolution_param {
num_output: 2304
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
}
pad_h: 0
pad_w: 0
kernel_h: 2
kernel_w: 2
stride_h: 1
stride_w: 1
}
}
layer {
name: "relu23"
type: "ReLU"
bottom: "conv22"
top: "conv22"
}
layer {
name: "conv24"
type: "Convolution"
bottom: "conv22"
top: "conv24"
convolution_param {
num_output: 256
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
}
pad_h: 1
pad_w: 1
kernel_h: 2
kernel_w: 2
stride_h: 1
stride_w: 1
}
}
layer {
name: "relu25"
type: "ReLU"
bottom: "conv24"
top: "conv24"
}
layer {
name: "fc26"
type: "InnerProduct"
bottom: "conv24"
top: "fc26"
inner_product_param {
num_output: 4096
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
}
}
}
layer {
name: "relu27"
type: "ReLU"
bottom: "fc26"
top: "fc26"
}
layer {
name: "fc28"
type: "InnerProduct"
bottom: "fc26"
top: "fc28"
inner_product_param {
num_output: 4096
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
}
}
}
layer {
name: "relu29"
type: "ReLU"
bottom: "fc28"
top: "fc28"
}
layer {
name: "output"
type: "InnerProduct"
bottom: "fc28"
top: "output"
inner_product_param {
num_output: 1000
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
}
}
}

Comments

  • 4 Comments sorted by Votes Date Added
  • @csarron We are still investigating the root cause but I have a work around for you in the mean time. Here are the steps to create a conf file that should be placed in the same directory as your prototxt file.

    1) Create a new text file named "j.conf"
    2) Add the following lines to the "j.conf" text file:

    conv15
    im2col_v2

    conv24
    im2col_v2

    3) Save the file and place it in the same directory as your "j.prototxt". You should be able to use this network as long as the conf file accompanies it.

  • @Tome_at_Intel Thank you for the reply. I tried the conf workaround as you said, still gave the same error, I also got the following log:
    /usr/local/bin/ncsdk/Controllers/FileIO.py:52: UserWarning: [93mYou are using a large type. Consider reducing your data sizes for best performance[0m
    "Consider reducing your data sizes for best performance\033[0m")
    [91m[Error 25] Myriad Error: "Major or Minor Slices of MatMul are zero".[0m
    [1mmvNCProfile v02.00, Copyright @ Movidius Ltd 2016[0m

    0 0x80000000
    Layer conv1 use the generic optimisations which is: 0x80000000
    0 0x80000000
    Layer pool3 use the generic optimisations which is: 0x80000000
    0 0x80000000
    Layer conv4 use the generic optimisations which is: 0x80000000
    0 0x80000000
    Layer conv6 use the generic optimisations which is: 0x80000000
    0 0x80000000
    Layer conv8 use the generic optimisations which is: 0x80000000
    0 0x80000000
    Layer conv10 use the generic optimisations which is: 0x80000000
    0 0x80000000
    Layer pool12 use the generic optimisations which is: 0x80000000
    0 0x80000000
    Layer conv13 use the generic optimisations which is: 0x80000000
    Spec opt found opt_conv_im2col_v2 1<< 2
    Layer (a) conv15 use the optimisation mask which is: 0x4
    0 0x80000000
    Layer conv17 use the generic optimisations which is: 0x80000000
    0 0x80000000
    Layer conv19 use the generic optimisations which is: 0x80000000
    0 0x80000000
    Layer pool21 use the generic optimisations which is: 0x80000000
    0 0x80000000
    Layer conv22 use the generic optimisations which is: 0x80000000
    Spec opt found opt_conv_im2col_v2 1<< 2
    4 0x80000004
    Layer conv24 use the generic optimisations which is: 0x80000004
    0 0x80000000
    Layer fc26 use the generic optimisations which is: 0x80000000
    0 0x80000000
    Layer fc28 use the generic optimisations which is: 0x80000000
    0 0x80000000
    Layer output use the generic optimisations which is: 0x80000000
    USB: Transferring Data...
    [Error 25] Myriad Error: "Major or Minor Slices of MatMul are zero".

  • @Tome_at_Intel , given the optimization configuration you mentioned, can you share more about how to configure the optimization mask if possible?

  • @csarron Make sure you are using the latest version of the SDK and please try this again. Thanks.

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