Converters

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

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-elcorenn 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-elcorenn

onnx-elcorenn is a Python library that converts a model from ONNX to ElcoreNN.

Requirements:

• python3.6

1. Download onnx-elcorenn archive and extract:

   wget --no-check-certificate -O onnxparser-v1.3.0.tar.gz \
   https://box.elvees.com/index.php/s/elcorenn-converters/download?files=onnxparser-v1.3.0.tar.gz
   tar xfv onnxparser-v1.3.0.tar.gz
   

2. Install requirements (it is recommended to use a virtual environment):

   cd onnxparser-v1.3.0
   python3.6 -m venv env
   source ./env/bin/activate
   pip install --upgrade pip
   pip install -r requirements.txt
   

3. Put your onnx model to onnx_models folder.

4. Run converter:

   python onnx_converter.py -n <model-name.onnx> -at
   

   Converter will write result to json_models/<model-name>.onnx folder.

   To see more options run:

   python onnx_converter.py --help
   

   or see README.md file.

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
AveragePooling2D
BatchNormalization
Concatenate/Merge
Conv1D	dilation_rate=1, groups=1
Conv2D	dilation_rate=1, groups=1
Conv2DTranspose	dilation_rate=1, filters value is divisible by 32
Dense
DepthwiseConv2D	dilation_rate=1, depth_multiplier=1
Flatten
GlobalAveragePooling2D
InputLayer
L2Normalization
MaxPooling2D
Multiply
Permute
ReLU
Reshape
Softmax
UpSampling2D	interpolation=”nearest”
ZeroPadding2D

onnx-elcorenn supports the following ONNX Operators:

ONNX Operators	Restrictions
Add (including broadcasting)	only for 2 tensors
BatchNormalization
Cast	fixed point isn’t supported
Concat
Conv	conv_dimensions=[1,2], dilations=1, group=[1 or in_channels]
ConstantOfShape
Div
Erf
Equal
Exp
Expand
Flatten
Gather
Gemm	used as full-connected layer
GlobalAveragePool	2D
Identity
MaxPool	2D, dilations=1
Mul (including broadcasting)	only for 2 tensors
Pow
Range
ReLU
ReLU6
Reshape
Resize
Shape
Sigmoid
Slice
Softmax
Sqrt
Sub (including broadcasting)	only for 2 tensors
Transpose
Unsqueeze
Where