Troubleshooting Neural Network Conversion Errors

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Application note description

This application note describes some common errors that can occur when converting neural network files and provides a list of supported layers.

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Common Errors

Whether using either the FLIR NeuroUtility (Windows) or mvNCCompile (Linux) for converting your inference network files, here are some common errors and ways to fix them:

Toolkit Error: Stage Details Not Supported

This error can occur if at least one of the layers being used in the network is unsupported.

  1. Check the layer name for type (the error gives the name).
  2. Check of list of accepted layer types (listed at end of this application note).

It can also mean that not all training code or placeholders were properly removed before doing the final conversion.

Toolkit error: Provided OutputNode/InputNode name does not exist or does not match with one contained in caffemodel file provided

This error can occur when at least one of the node names provided is incorrect. This can be as simple as having an incorrect capitalization or spelling, or the wrong node name entirely.

Toolkit Error: Parser not supported

This error can occur when an incorrect file location is provided, for example the inference network file.

Setup Error: Not enough resources on Myriad to process this network

This error can occur when there is not enough memory for the number of layers for the inference network file.

  • Reduce the number of layers, or
  • Reduce the channels per layer

List of Supported Layers

The following list of Supported layers are separated by what they have been tested on; Tensorflow, Classification, and Caffe.  Where appropriate, we have added any restrictions we have found when using a particular layer type.


    Depth Convolution
    Restrictions : Input/output channel dimensions must match
    Dilated convolution
    Max Pooling Radix NxM with Stride S
    Note : 2x2 and 3x3 are optimized
    Average Pooling: Radix NxM with Stride S, Global average pooling
    Note : 3x3 and 7x7 are optimized
    Local Response Normalization
    Batch Normalization (fused)
    L2 Normalization
    Input Layer
    Fully Connected Layers (limited support)
    Relu-X, , Leaky-Relu
    Restrictions : Input/output storage order must be the same




    Restrictions : Input stride must (in channels x 2)
    ElmWise unit : supported operations - sum, prod, max
    Restrictions : Input/output must have the same storage order, input/output tensors must be the same size, only channel minor (YXZ) and interleaved (YZX) storage orders supported
    Regular Convolution - 1x1s1, 3x3s1, 5x5s1, 7x7s1, 7x7s2, 7x7s4
    Restrictions (not applicable to 1x1s1):
    Width and height must be >= 8 pixels.
    output channels must be >=8.
    input channels must be < K_MAX
    Group Convolution - <1024 groups total
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