Error using dnnfpga.compiler.codegenfpga. Layer 'block2b_add' has multiple inputs.

Question

Rubén 2023-4-28

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此问题的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/1954474-error-using-dnnfpga-compiler-codegenfpga-layer-block2b_add-has-multiple-inputs

评论： Aravind Devarakonda 2023-5-12

Hello,

I have a pretrained NN imported from TensorFlow. I have imported and replaced the only one placeholder layer and the DAGNetwork seems to be ok. However, when I try to compile the NN with Deep Learning HDL Toolbox, I get the following error:

Error using dnnfpga.compiler.codegenfpga
Layer 'block2b_add' has multiple inputs. Specify which input of layer 'block2b_add' to use.
Error in dnnfpga.apis.Workflow/compileNetwork
Error in dnnfpga.apis.Workflow/compile
Error in generate_HDL_from_NN (line 18)
    hW.compile;

block2b_add has two inputs and connections are defined.

Please find the NN in the LINK.

Could anyone help me to understand the issue?

Thank you in advance

---------------------------

Software version:

Deep Learning HDL Toolbox Support Package For Intel FPGA And SoC Devices 21.2.4

Matlab R2021b Update 6 (9.11.0.2207237)

Ubuntu 20.04

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Rubén 2023-5-4

编辑：Rubén 2023-5-5

Hello @Aravind Devarakonda,

I'm sorry, but that message is not prompted while importing savedModel or NN.mat (at least with my Matlab version).

I have replaced EfficienB0_p1_C1.kRescalingLayer246310 by a scalingLayer - which has the same behaviour. However, I get the same error message while compiling.

Please, find in the following link the new NN: NN_RescalingLayer_replaced.mat

Aravind Devarakonda 2023-5-12

Hi Ruben

Thank you for replacing the network originally attached.

Regarding your inquiry, I have observed that the network given has a few layers that are not yet supported in the Deep Learning HDL Toolbox and are planned to be integrated in future updates.

These layers include :

1) Scaling Layer

2) Layer Normalization Layer

3) Swish Layer and,

4) FlattenCStyleLayer immediately followed by Convolution2DLayer is also not supported.

Currently, a flatten layer must be followed by either fully connected layer(s) or a join layer (eg. depth concatenation, addition) followed by fully connected layer(s).

As a workaround, I would suggest implementing the Scaling Layer as a custom layer by following the steps available on this documentation link -

https://in.mathworks.com/help/deep-learning-hdl/ug/create-deep-learning-processor-configuration-for-custom-layers.html

Regarding FlattenCStyleLayer immediately followed by Convolution2DLayer, I suggest replacing it with a fully connected layer.

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