From c24cd1850ad1f7dfc1b09bb2ab10c2c92acc40cd Mon Sep 17 00:00:00 2001
From: auphelia <jakobapk@web.de>
Date: Wed, 8 Feb 2023 09:03:30 +0000
Subject: [PATCH] [Notebooks] Update text in advanced and basics nbs
---
notebooks/advanced/2_custom_op.ipynb | 8 ++++----
notebooks/basics/0_how_to_work_with_onnx.ipynb | 4 ++--
2 files changed, 6 insertions(+), 6 deletions(-)
diff --git a/notebooks/advanced/2_custom_op.ipynb b/notebooks/advanced/2_custom_op.ipynb
index 1ef60fd11..636da64dd 100644
--- a/notebooks/advanced/2_custom_op.ipynb
+++ b/notebooks/advanced/2_custom_op.ipynb
@@ -8,14 +8,14 @@
"\n",
"Suppose that you want to introduce a new (custom) operation type into the FINN compiler. Custom operations in FINN are useful for a variety of things ranging from code generation to functional verification. This is achieved by creating a new Python module for your custom operation that fulfills certain interface specifications.\n",
"\n",
- "One thing to point out before we start is that **these custom operations are generic** and not really tied to e.g. Vivado HLS or few-bit quantization. As you will see in this notebook, it's possible to provide arbitrary Python/C/C++/... execution and code generation paths for custom nodes.\n",
+ "One thing to point out before we start is that **these custom operations are generic** and not really tied to e.g. Vitis HLS or few-bit quantization. As you will see in this notebook, it's possible to provide arbitrary Python/C/C++/... execution and code generation paths for custom nodes.\n",
"\n",
"## The CustomOp base class\n",
"\n",
"Subclasses of `CustomOp` provide a way of providing custom functionality for ONNX nodes in FINN.\n",
"This is the base class for every custom op node used in the framework, so you must create subclasses of `CustomOp` to provide execution, code generation and other functionalities in FINN.\n",
"\n",
- "Let's start by looking at the `CustomOp` base class itself, which lives in the `finn-base` repository. You can view it [here](https://github.com/Xilinx/finn-base/blob/dev/src/finn/custom_op/base.py). Note that the `finn` Docker container already has `finn-base` set up as a dependency.\n",
+ "Let's start by looking at the `CustomOp` base class itself, which lives in the `qonnx` repository. You can view it [here](https://github.com/fastmachinelearning/qonnx/blob/main/src/qonnx/custom_op/base.py). Note that the `finn` Docker container already has `qonnx` set up as a dependency.\n",
"\n",
"Some points of importance:\n",
"\n",
@@ -23,7 +23,7 @@
"\n",
"2. `CustomOp` subclasses need to implement the methods below (those not starting with underscore).\n",
"\n",
- "3. To be discoverable in the custom op register, `CustomOp` subclasses must set the `domain` field to the name of the Python module they appear in. For instance, to use the custom `Im2Col` op type from [here](https://github.com/Xilinx/finn-base/blob/dev/src/finn/custom_op/general/im2col.py), the ONNX node must use `domain=qonnx.custom_op.general` since its module is located at `finn/custom_op/general/im2col.py`."
+ "3. To be discoverable in the custom op register, `CustomOp` subclasses must set the `domain` field to the name of the Python module they appear in. For instance, to use the custom `Im2Col` op type from [here](https://github.com/fastmachinelearning/qonnx/blob/main/src/qonnx/custom_op/general/im2col.py), the ONNX node must use `domain=qonnx.custom_op.general` since its module is located at `qonnx/custom_op/general/im2col.py`."
]
},
{
@@ -130,7 +130,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "To make sure our custom op is available, it needs to be registered. The best practice for this is to create a submodule under `finn.custom_op` which includes a `custom_op` dictionary that maps strings (op names) to classes (op implementations). Since we're in a Jupyter notebook we'll just hijack it at runtime like this:"
+ "To make sure our custom op is available, it needs to be registered. The best practice for this is to create a submodule under `qonnx.custom_op` which includes a `custom_op` dictionary that maps strings (op names) to classes (op implementations). Since we're in a Jupyter notebook we'll just hijack it at runtime like this:"
]
},
{
diff --git a/notebooks/basics/0_how_to_work_with_onnx.ipynb b/notebooks/basics/0_how_to_work_with_onnx.ipynb
index ed36aa109..35a83ea97 100644
--- a/notebooks/basics/0_how_to_work_with_onnx.ipynb
+++ b/notebooks/basics/0_how_to_work_with_onnx.ipynb
@@ -24,7 +24,7 @@
"source": [
"### How to create a simple ONNX model\n",
"\n",
- "To explain how to create an ONNX model a simple example with mathematical operations is used. All nodes are from the [standard operations library of ONNX](https://github.com/onnx/onnx/blob/master/docs/Operators.md).\n",
+ "To explain how to create an ONNX model a simple example with mathematical operations is used. All nodes are from the [standard operations library of ONNX](https://github.com/onnx/onnx/blob/main/docs/Operators.md).\n",
"\n",
"First ONNX is imported, then the helper function can be used to make a node."
]
@@ -305,7 +305,7 @@
"source": [
"### How to manipulate an ONNX model\n",
"\n",
- "In the model there are two successive adder nodes. An adder node in ONNX can only add two inputs, but there is also the [**sum**](https://github.com/onnx/onnx/blob/master/docs/Operators.md#Sum) node, which can process more than two inputs. So it would be a reasonable change of the graph to combine the two successive adder nodes to one sum node."
+ "In the model there are two successive adder nodes. An adder node in ONNX can only add two inputs, but there is also the [**sum**](https://github.com/onnx/onnx/blob/main/docs/Operators.md#Sum) node, which can process more than two inputs. So it would be a reasonable change of the graph to combine the two successive adder nodes to one sum node."
]
},
{
--
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