Converters

Converters are Python libraries that run on x86 and are used to convert Keras or ONNX models to ElcoreNN format.

ElcoreNN converters

ElcoreNN model format

ElcoreNN model format is represented by two files. The first is a JSON file that describes the layers of the model. The second is a binary file that contains the weights of the model.

keras-elcorenn and onnx-converter convert Keras and ONNX model to ElcoreNN model format.

keras-elcorenn

keras-elcorenn is a Python library that converts a model from Keras to ElcoreNN.

Requirements:

• python version >=3.7

1. Download keras-elcorenn archive:

   wget --no-check-certificate -O keras-elcorenn-1.0.0.tar.gz \
   https://box.elvees.com/index.php/s/elcorenn_converters/download?files=keras-elcorenn-1.0.0.tar.gz
   

2. Activate virtual environment:

   python3 -m venv env
   source ./env/bin/activate
   

3. Installation from archive:

   pip install keras-elcorenn-1.0.0.tar.gz
   

4. Convert model:

   python -m keras2elcorenn.convert --keras-model=<path-to-keras-model>
   

   To see more options run:

   python -m keras2elcorenn.convert --help
   

ONNX converter

ONNX converter is a Python library that converts a model from ONNX to ElcoreNN.

Requirements:

• python version >= 3.8, <= 3.12.

Instructions to convert ONNX model to ElcoreNN:

1. Download ONNX converter from Elvees Box.

2. Install wheel file:

   python3 -m venv .venv
   source .venv/bin/activate
   pip install --upgrade pip
   pip install onnx_converter-*-py3-none-any.whl
   

3. Prepare JSON configuration file according to ONNX converter instructions (to see all configuration options see README.md from Elvees Box):

   {
        "model_path": "/path/to/model.onnx",
        "result_path": "/path/to/the/folder/where/to/save/elcorenn/model"
   }
   

4. Run:

   onnx_converter --config_file conf.json
   

Supported operators

For all Keras layers keras-elcorenn supports only NHWC data format.

keras-elcorenn supports the following Keras layers:

Keras layer	Restrictions
Activation	only linear, relu, relu6, softmax, leakyrelu, sigmoid
Add
AveragePooling1D
AveragePooling2D
BatchNormalization
Concatenate/Merge
Conv1D	dilation_rate=1, groups=1
Conv2D	dilation_rate=1
Conv3D	reference version (fp32 only)
Conv1DTranspose	reference version (fp32 only)
Conv2DTranspose	dilation_rate=1, filters value is divisible by 32
Dense
DepthwiseConv2D	dilation_rate=1, depth_multiplier=1
Flatten
GlobalAveragePooling2D
InputLayer
L2Normalization
LSTM
MaxPooling2D
Multiply
Permute
ReLU
Reshape
Softmax
UpSampling2D	interpolation=”nearest”
ZeroPadding2D

onnx-converter supports the following ONNX Operators:

ONNX Operators	Restrictions
Abs
Add (including broadcasting)	only for 2 tensors
ArgMax
AveragePool	1D, 2D, dilation=1
BatchNormalization
Cast
Clip
Concat
Conv	conv_dimensions=[1,2,3], dilations=1, Conv3d float32 only
ConvTranspose	conv_dimensions=[1, 2]
ConstantOfShape
CumSum
Div (including broadcasting)
Einsum	only for 2 tensors
Erf
Equal
Exp
Expand
Flatten
Floor
Gather
GatherElements
Gemm	used as full-connected layer
GlobalAveragePool	2D
GlobalMaxPool
Identity
InstanceNormalization
LayerNorm
Less
LSTM
MatMul (including broadcasting)	Support 2D..5D tensors
MaxPool	1D, 2D, dilations=1
Mod
Mul (including broadcasting)	only for 2 tensors
Neg
NonMaxSuppression
NonZero	(Allowed only precomputed inputs)
Not
Pad
Pow
PRelu
Range
ReduceL2
ReduceMax
ReduceMean	last dimension only
ReduceSum
ReLU
ReLU6
Reshape
Resize
ScatterND
Shape
Sigmoid
Slice
Softmax
Split
Squeeze
Sqrt
Sub (including broadcasting)	only for 2 tensors
Tanh
Tile
Transpose
Unsqueeze
Where