"* <font size=\"3\">returns the changed model and flag `model_was_changed`</font>"
"* <font size=\"3\">returns the changed model (ModelWrapper) and flag `model_was_changed`</font>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## General Information\n",
"-----------------------------\n",
"<font size=\"3\">Transformation passes have a base class and must inherit from that. Transformations are meant to be applied using .transform function from the ModelWrapper. This function makes a deep copy of the input model by default. The next cell shows .transform of ModelWrapper. </font>\n"
"<font size=\"3\">When the function is called, the model, the name of the transformation and, if required, the flag make_deepcopy are passed. It is also possible not to make a copy of the model. In this case `make_deepcopy` must be set to False. Then the branch `if make_deepcopy:` would not be taken and no copy of the model would be made. \n",
"\n",
"The unchanged model is first passed to the variable `transformed_model` to pass this variable on to the transformation later. \n",
"\n",
"`model_was_changed` indicates whether the transformation needs to be applied more then once. Because it needs to be applied at least one time `model_was_changed` is first set to True and then depending on the return values of the transformation function the transformation can be applied more then once. \n",
"\n",
"**Important**: Due to the structure of this function, `model_was_changed` must be set to False at some point. Otherwise the loop is infinite.\n",
" \n",
"\n",
"Each new transformation must correspond to the scheme of the base class and contain at least the function `apply(model)`, which returns the changed model and a bool value for `model_was_changed`.\n",
"</font>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Example - \n",
"-----------------------------\n",
"<font size=\"3\">text</font>"
]
},
{
...
...
%% Cell type:markdown id: tags:
# FINN - Transformation passes
--------------------------------------
*<fontsize="3">changes (transforms) the given graph</font>
*<fontsize="3">input: ModelWrapper</font>
*<fontsize="3">returns the changed model and flag `model_was_changed`</font>
*<fontsize="3">returns the changed model (ModelWrapper) and flag `model_was_changed`</font>
%% Cell type:markdown id: tags:
## General Information
-----------------------------
<fontsize="3">Transformation passes have a base class and must inherit from that. Transformations are meant to be applied using .transform function from the ModelWrapper. This function makes a deep copy of the input model by default. The next cell shows .transform of ModelWrapper. </font>
<fontsize="3">When the function is called, the model, the name of the transformation and, if required, the flag make_deepcopy are passed. It is also possible not to make a copy of the model. In this case `make_deepcopy` must be set to False. Then the branch `if make_deepcopy:` would not be taken and no copy of the model would be made.
The unchanged model is first passed to the variable `transformed_model` to pass this variable on to the transformation later.
`model_was_changed` indicates whether the transformation needs to be applied more then once. Because it needs to be applied at least one time `model_was_changed` is first set to True and then depending on the return values of the transformation function the transformation can be applied more then once.
**Important**: Due to the structure of this function, `model_was_changed` must be set to False at some point. Otherwise the loop is infinite.
Each new transformation must correspond to the scheme of the base class and contain at least the function `apply(model)`, which returns the changed model and a bool value for `model_was_changed`.
