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Release notes
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1.5.0
#####

Main changes
------------

* New ElcoreNN CPU API functions.
* ElcoreNN DSP library optimizations.
* Update onnx-elcorenn to v1.3.3.
* Support new ONNX operators and Keras layers.
* elcorenn-examples: Installation path has been changed.

ElcoreNN CPU API
----------------

* InvokeModel:
    - Supported different types of model input data.
    - Added multiple outputs support for input dmabuf (for models with one input).

      .. code-block:: cpp
        :caption: dmabuf_input_user_pointer_output.cpp

        #include "elcorenn/elcorenn.h"

        int main() {
          // Init ElcoreNN resources
          InitBackend();
          // Load model from files
          auto id = LoadModel("path-to-model-json-file", "path-to-weights-bin-file");

          // Read output layers shapes
          int outputs_number = GetOutputsNumber(id);
          std::vector<std::vector<int>> outputs_shapes;
          for (int i = 0; i < outputs_number; i++){
            uint32_t shape_arr[MAX_NDIM_VALUE + 1];
            GetOutputShape(id, i, shape_arr);
            std::vector<int> shape(shape_arr[0]);
            for (int j = 0; j < shape_arr[0]; j++) {
              shape[j] = shape_arr[j + 1];
            }
            outputs_shapes.push_back(shape);
          }

          std::vector<int> buffers;
          uint32_t batch_size;
          // Create and fill or use already existing dmabufs to push it back to
          // buffers vector. Number of dmabufs in buffers should be equal to
          // batch_size.
          // ...
          // batch_size is a batch dimension value in input_data, output_data

          // Allocate memory for outputs data
          std::vector<std::vector<float>> outputs_data;
          for (int i = 0; i < outputs_number; i++){
            auto shape = outputs_shapes[i];
            int size = 1;
            for (auto dim_size: shape){
              size = size * dim_size;
            }
            std::vector<float> output_data(size * batch_size);
            outputs_data.push_back(output_data);
          }

          // Prediction
          float* outputs[outputs_number];
          for (int j = 0; j < outputs_number; j++) {
            outputs[j] = outputs_data[j].data();
          }
          InvokeModel(id, buffers.data(), outputs, batch_size);
        }

    - Added support for input dmabuf and output dmabuf (for models with one input and one output).

      .. code-block:: cpp
        :caption: dmabuf_input_dmabuf_output.cpp

        #include "elcorenn/elcorenn.h"

        int main() {
          // Init ElcoreNN resources
          InitBackend();
          // Load model from files
          auto id = LoadModel("path-to-model-json-file", "path-to-weights-bin-file");

          std::vector<int> input_buffers;
          std::vector<int> output_buffers;
          uint32_t batch_size;
          // Create or use already existing dmabufs to push it back to buffers
          // vector. Number of dmabufs in buffers should be equal to batch_size.
          // ...
          // batch_size is a batch dimension value in input_data, output_data

          // Prediction
          InvokeModel(id, input_buffers.data(), output_buffers.data(), batch_size);
        }

* InitBackend: Add argument `dsp_heap` to set dsp heap size.
* LoadModel: Add argument `optimization` to set data type of computation.

ElcoreNN DSP
------------

* Add fp16 support for Pad layer.
* Increase performance of sigmoid by using precalculated table values.
* Increase performance by fusing layers:

  .. graphviz::

     digraph R141MergeDigraph1 {
       node [ shape = box, style = filled, width=0.5, height=0.2]
       A [label = "X"];
       B [label = "Sigmoid", fillcolor = "#f5c884"];
       C [label = "Mul", fillcolor = "#fcffc4"];
       D [label = "Y"];

       A -> B -> C -> D;
       A -> C;
     }

  into:

  .. graphviz::

     digraph R141MergeDigraph2 {
       node [ shape = box, style = filled, width=0.5, height=0.2]
       A [label = "X"];
       B [label = "X * Sigmoid(X)", fillcolor = "#98fab2"];
       C [label = "Y"];

       A -> B -> C;
     }

  X * Sigmoid(X) function uses precalculated table values.

onnx-elcorenn
-------------

* Update to v1.3.3 version.
* Save converter version to model json file.
* Support python3.8.
* Support Dropout and Cast layers.
* Bug fixes.

ONNX Operators
--------------

* AveragePool: 2D, dilations=1.
* Clip.
* Conv: support dimension=3 for fp32.
* ConvTranspose: support dimension=1.
* LSTM: reference version (fp32 only).
* MatMul.

Keras layers
------------

* Conv1DTranspose: reference version (fp32).
* Conv3D: reference version (fp32).
* LSTM: reference version (fp32).

elcorenn-examples
-----------------

* Installation path has been changed to ``/usr/bin``.
* ``prepare-examples-env.sh`` has been renamed to ``elcorenn-examples-get-data.sh``.

Examples look for models relative to ``$HOME`` directory:

#. Go into ``$HOME`` directory.
#. Run ``elcorenn-examples-get-data.sh`` script to get models.

1.4.0
######

Main changes
------------

* Add new layers support.
* Add YOLOv5 example.
* Update onnx-elcorenn converter to 1.3.0 version.
* Update ElcoreNN CPU API.
* Create web storage of `converters`_ and `elcorenn-examples-data`_ releases.


ElcoreNN CPU
------------

* Support for models with multiple inputs and outputs.
* Support dma_buf as input of model.
* Add functions to get name and shapes of model inputs or outputs.

ElcoreNN DSP
------------

* Add Div, Erf, Conv1D, Sqrt layers.

elcorenn-examples
-----------------

* Add new example of YOLOv5s usage.
* Update archive with video and models.

onnx-elcorenn
-------------

* Update onnx-elcorenn to 1.3.0 version.
* Support models converted from Tensorflow to ONNX.
* Support attribute sizes for Resize operator.
  It represents target size of the output tensor.

Compatibility: elcorenn == v1.4.0

.. _converters: https://box.elvees.com/index.php/s/elcorenn-converters
.. _elcorenn-examples-data: https://box.elvees.com/index.php/s/elcorenn-examples-data