Export and interop update.

- Separate calibration and export in helper function.
- Add a PTQ module in the Python API for calibration !
- Add qat sat example with the python API.
- Torch interop incomplete batch size are now handled.
- Add tiny ml networks to keras interop tests.
- Fix bug with multithread in `Histogram.cpp`
- Add tuto for stimuliprovider
- DeepNet name now follow the Python API convention
- Provider no longer load data to check data size.

docs/python_api/cells.rst

@@ -19,6 +19,14 @@ Cells are organize with the following logic :
 .. figure:: ../_static/n2d2_cell_diagram.png
    :alt: Cell class diagram
 
+Block
+~~~~~
+
+.. autoclass:: n2d2.cells.Block
+        :members:
+        :inherited-members:
+
+
 Sequence
 ~~~~~~~~
 

docs/python_api/example.rst

@@ -8,6 +8,7 @@ If you find an example not up to date, please consider leaving an issue here : h
 .. toctree::
    :maxdepth: 1
 
+   example/data_augmentation
    example/performance_analysis
    example/load_onnx
    example/graph_manipulation

docs/python_api/example/data_augmentation.rst

@@ -0,0 +1,139 @@
+Data augmentation
+=================
+
+In this example, we will see how to use :py:class:`n2d2.provider.DataProvider` and :py:class:`n2d2.transform.Transformation` to load data and do some data augmentation.
+
+You can find the full python script here :download:`data_augmentation.py</../python/examples/data_augmentation.py>`.
+
+Preliminary
+-----------
+
+
+For this tutorial, we will use n2d2 for data augmentation, and numpy and matplotlib for the visualization.
+
+We will create a method plot_tensor to save the generated images from an :py:class:`n2d2.Tensor`
+
+
+.. code-block::
+
+    import n2d2
+    import matplotlib.pyplot as plt
+
+    def plot_tensor(tensor, path):
+        plt.imshow(tensor[0,0,:], cmap='gray', vmin=0, vmax=255)
+        plt.savefig(path)
+
+Loading data
+------------
+
+We will begin by creating a :py:class:`n2d2.database.MNIST` driver to load the MNIST dataset.
+We will then create a provider to get the images, we use a batch size of 1 to get only one image.
+
+
+.. code-block::
+
+    database = n2d2.database.MNIST(data_path="/local/DATABASE/mnist", validation=0.1)
+    provider = n2d2.provider.DataProvider(database, [28, 28, 1], batch_size=1)
+
+
+You can get the number of data per partition by using the method :py:meth:`n2d2.database.Database.get_partition_summary` which will print the paritionement of data.
+
+.. code-block::
+
+    database.get_partition_summary()
+
+
+**Output :**
+
+.. testoutput::
+
+    Number of stimuli : 70000
+    Learn         : 54000 stimuli (77.14%)
+    Test          : 10000 stimuli (14.29%)
+    Validation    : 6000 stimuli (8.57%)
+    Unpartitioned : 0 stimuli (0.0%)
+
+
+To select which partition you want to read from you need to use the method :py:meth:`n2d2.provider.DataProvider.set_partition`
+
+To read data from a :py:class:`n2d2.provider.DataProvider` you can use multiple methods.
+
+You can use the methods :py:meth:`n2d2.provider.DataProvider.read_batch` or :py:meth:`n2d2.provider.DataProvider.read_random_batch`. 
+
+
+.. note::
+
+    Since :py:class:`n2d2.provider.DataProvider` is an `iterable`, so you can use the ``next()`` function or a for loop !
+
+    .. code-block::
+
+        # for loop example
+        for data in provider:
+            pass
+        # next example
+        data = next(provider)
+
+For this tutorial we will use :py:meth:`n2d2.provider.DataProvider.read_batch` !
+
+With this code we will get the first image and plot it :
+
+.. code-block::
+
+    image = provider.read_batch(idx=0).to_numpy() * 255
+    plot_tensor(image, "first_stimuli.png")
+
+
+.. figure:: /_static/first_stimuli.png
+   :alt: First stimuli of the MNIST dataset.
+
+Data augmentation
+-----------------
+
+To do data augmentation with N2D2 we use :py:class:`n2d2.transform.Transformation`.
+You can add transformation to provider with the method :py:meth:`n2d2.provider.DataProvider.add_on_the_fly_transformation` and :py:meth:`n2d2.provider.DataProvider.add_transformation`.
+
+.. warning::
+
+    Since we already loaded the first image the method :py:meth:`n2d2.provider.DataProvider.add_transformation` would not apply the transformation to the image.
+
+By using the transformation :py:class:`n2d2.transform.Flip` we will flip vertically our image.
+
+.. code-block::
+
+    provider.add_on_the_fly_transformation(n2d2.transform.Flip(vertical_flip=True))
+
+    image = provider.read_batch(idx=0).to_numpy() * 255
+    plot_tensor(image, "first_stimuli_fliped.png")
+
+
+.. figure:: /_static/first_stimuli_fliped.png
+   :alt: First stimuli of the MNIST dataset but flipped.
+
+We will negate the first transformation with another :py:class:`n2d2.transform.Flip` which we will add with the method :py:meth:`n2d2.provider.DataProvider.add_transformation`.
+
+.. code-block::
+
+    # negating the first transformation with another one
+    provider.add_transformation(n2d2.transform.Flip(vertical_flip=True))
+    image = provider.read_batch(idx=1).to_numpy() * 255
+    plot_tensor(image, "second_stimuli.png")
+
+.. figure:: /_static/second_stimuli.png
+   :alt: Second stimuli of the MNIST dataset.
+
+
+Getting labels
+--------------
+
+To get the labels 
+
+.. code-block::
+
+
+    print("Second stimuli label : ", provider.get_labels()[0])
+
+**Output :**
+
+.. testoutput::
+
+    Second stimuli label : 5

docs/python_api/export.rst

@@ -73,4 +73,16 @@ Example
         DEEPNET_CELL,
         nb_bits=8,
         export_nb_stimuli_max=-1,
-        calibration=-1)
+        calibration=-1)
+
+Export CPP TensorRT
+-------------------
+
+.. autofunction:: n2d2.export.export_tensor_rt
+
+Example
+^^^^^^^
+
+.. code-block:: python
+
+    n2d2.export.export_tensor_rt(DEEPNET_CELL)