From 5529ff59270f6c649c8110fbe7b73f2d00e0ee77 Mon Sep 17 00:00:00 2001
From: Yaman Umuroglu <yamanu@xilinx.com>
Date: Fri, 28 Feb 2020 17:05:11 +0000
Subject: [PATCH] [Doc] change dev links to master
---
docs/finn/index.rst | 2 +-
docs/finn/tutorials.rst | 22 +++++++++----------
notebooks/basics/0_getting_started.ipynb | 10 ++++-----
.../end2end_example/tfc_end2end_example.ipynb | 2 +-
notebooks/internals/2_custom_op.ipynb | 4 ++--
5 files changed, 19 insertions(+), 21 deletions(-)
diff --git a/docs/finn/index.rst b/docs/finn/index.rst
index 969b8afad..4a9452c72 100644
--- a/docs/finn/index.rst
+++ b/docs/finn/index.rst
@@ -25,7 +25,7 @@ What is FINN?
More FINN Resources
===================
-* `List of publications <https://github.com/Xilinx/finn/blob/dev/docs/publications.md>`_
+* `List of publications <https://github.com/Xilinx/finn/blob/master/docs/publications.md>`_
* `Roadmap <https://github.com/Xilinx/finn/projects/1>`_
diff --git a/docs/finn/tutorials.rst b/docs/finn/tutorials.rst
index 961fea47d..cd852d82b 100644
--- a/docs/finn/tutorials.rst
+++ b/docs/finn/tutorials.rst
@@ -13,19 +13,19 @@ Basics
The notebooks in this folder should give a basic insight into FINN, how to get started and the basic concepts.
-* `0_getting_started <https://github.com/Xilinx/finn/blob/dev/notebooks/basics/0_getting_started.ipynb>`_
-
+* `0_getting_started <https://github.com/Xilinx/finn/blob/master/notebooks/basics/0_getting_started.ipynb>`_
+
* This notebook corresponds to the chapter :ref:`getting_started` and gives an overview how to start working with FINN.
-* `1_how_to_work_with_onnx <https://github.com/Xilinx/finn/blob/dev/notebooks/basics/1_how_to_work_with_onnx.ipynb>`_
+* `1_how_to_work_with_onnx <https://github.com/Xilinx/finn/blob/master/notebooks/basics/1_how_to_work_with_onnx.ipynb>`_
* This notebook can help you to learn how to create and manipulate a simple ONNX model, also by using FINN
-* `2_modelwrapper <https://github.com/Xilinx/finn/blob/dev/notebooks/basics/2_modelwrapper.ipynb>`_
+* `2_modelwrapper <https://github.com/Xilinx/finn/blob/master/notebooks/basics/2_modelwrapper.ipynb>`_
* This notebook corresponds to the section :ref:`modelwrapper` in the chapter about internals.
-* `3_brevitas_network_import <https://github.com/Xilinx/finn/blob/dev/notebooks/basics/3_brevitas_network_import.ipynb>`_
+* `3_brevitas_network_import <https://github.com/Xilinx/finn/blob/master/notebooks/basics/3_brevitas_network_import.ipynb>`_
* This notebook shows how to import a brevitas network and prepare it for the FINN flow.
@@ -34,19 +34,19 @@ Internals
The notebooks in this folder are more developer oriented. They should help you to get familiar with the principles in FINN and how to add new content regarding these concepts.
-* `0_custom_analysis_pass <https://github.com/Xilinx/finn/blob/dev/notebooks/internals/0_custom_analysis_pass.ipynb>`_
+* `0_custom_analysis_pass <https://github.com/Xilinx/finn/blob/master/notebooks/internals/0_custom_analysis_pass.ipynb>`_
* This notebook explains what an analysis pass is and how to write one for FINN.
-* `1_custom_transformation_pass <https://github.com/Xilinx/finn/blob/dev/notebooks/internals/1_custom_transformation_pass.ipynb>`_
+* `1_custom_transformation_pass <https://github.com/Xilinx/finn/blob/master/notebooks/internals/1_custom_transformation_pass.ipynb>`_
* This notebook explains what a transformation pass is and how to write one for FINN.
-* `2_custom_op <https://github.com/Xilinx/finn/blob/dev/notebooks/internals/2_custom_op.ipynb>`_
+* `2_custom_op <https://github.com/Xilinx/finn/blob/master/notebooks/internals/2_custom_op.ipynb>`_
* This notebooks explains what a custom operation/node is and how to create one for FINN.
-* `3_verify_hls_custom_op <https://github.com/Xilinx/finn/blob/dev/notebooks/internals/3_verify_hls_custom_op.ipynb>`_
+* `3_verify_hls_custom_op <https://github.com/Xilinx/finn/blob/master/notebooks/internals/3_verify_hls_custom_op.ipynb>`_
* This notebook shows the functional verification flow for hls custom operations/nodes.
@@ -55,6 +55,4 @@ End-to-End Flow
This notebook shows the FINN end-to-end flow step by step using an example of a simple, binarized, fully-connected network trained on the MNIST data set. Starting with the brevitas export and taking this particular network all the way down to hardware by using a specific sequence of transformations.
-* `tfc_end2end_example <https://github.com/Xilinx/finn/blob/dev/notebooks/end2end_example/tfc_end2end_example.ipynb>`_
-
-
+* `tfc_end2end_example <https://github.com/Xilinx/finn/blob/master/notebooks/end2end_example/tfc_end2end_example.ipynb>`_
diff --git a/notebooks/basics/0_getting_started.ipynb b/notebooks/basics/0_getting_started.ipynb
index 57f8e5947..07b2a2ba6 100644
--- a/notebooks/basics/0_getting_started.ipynb
+++ b/notebooks/basics/0_getting_started.ipynb
@@ -72,13 +72,13 @@
"\n",
"FINN uses ONNX is a specific way that we refer to as FINN-ONNX, and not all ONNX graphs are supported by FINN (and vice versa). Here is a list of key points to keep in mind:\n",
"\n",
- "* *Custom quantization annotations but data stored as float.* ONNX does not support datatypes smaller than 8-bit integers, whereas in FINN we are interested in smaller integers down to ternary and bipolar. To make this work, FINN uses the `quantization_annotation` field in ONNX to annotate tensors with their [FINN DataType](https://github.com/Xilinx/finn/blob/dev/src/finn/core/datatype.py) information. However, all tensors are expected to use single-precision floating point (float32) storage in FINN. This means we store even a 1-bit value as floating point for the purposes of representation. The FINN compiler flow is responsible for eventually producing a packed representation for the target hardware, where the 1-bit is actually stored as 1-bit.\n",
+ "* *Custom quantization annotations but data stored as float.* ONNX does not support datatypes smaller than 8-bit integers, whereas in FINN we are interested in smaller integers down to ternary and bipolar. To make this work, FINN uses the `quantization_annotation` field in ONNX to annotate tensors with their [FINN DataType](https://github.com/Xilinx/finn/blob/master/src/finn/core/datatype.py) information. However, all tensors are expected to use single-precision floating point (float32) storage in FINN. This means we store even a 1-bit value as floating point for the purposes of representation. The FINN compiler flow is responsible for eventually producing a packed representation for the target hardware, where the 1-bit is actually stored as 1-bit.\n",
"\n",
"* *Custom operations/nodes.* FINN uses many custom operations (`op_type` in ONNX NodeProto) that are not defined in the ONNX operator schema. These custom nodes are marked with `domain=\"finn\"` in the protobuf to identify them as such. These nodes can represent specific operations that we need for low-bit networks, or operations that are specific to a particular hardware backend.\n",
"\n",
- "* *Custom ONNX execution flow* To verify correct operation of FINN-ONNX graphs, FINN provides its own [ONNX execution flow](https://github.com/Xilinx/finn/blob/dev/src/finn/core/onnx_exec.py). This flow supports the standard set of ONNX operations as well as the custom FINN operations. *Important:* this execution flow is *only* meant for checking the correctness of models after applying transformations, and *not* for high performance inference. \n",
+ "* *Custom ONNX execution flow* To verify correct operation of FINN-ONNX graphs, FINN provides its own [ONNX execution flow](https://github.com/Xilinx/finn/blob/master/src/finn/core/onnx_exec.py). This flow supports the standard set of ONNX operations as well as the custom FINN operations. *Important:* this execution flow is *only* meant for checking the correctness of models after applying transformations, and *not* for high performance inference. \n",
"\n",
- "* *ModelWrapper* FINN provides a [`ModelWrapper`](https://github.com/Xilinx/finn/blob/dev/src/finn/core/modelwrapper.py) class as a thin wrapper around ONNX to make it easier to analyze and manipulate ONNX graphs. This wrapper provides many helper functions, while still giving full access to the ONNX protobuf representation. \n",
+ "* *ModelWrapper* FINN provides a [`ModelWrapper`](https://github.com/Xilinx/finn/blob/master/src/finn/core/modelwrapper.py) class as a thin wrapper around ONNX to make it easier to analyze and manipulate ONNX graphs. This wrapper provides many helper functions, while still giving full access to the ONNX protobuf representation. \n",
"\n",
"[Netron](https://lutzroeder.github.io/netron/) is very useful for visualizing ONNX models, including FINN-ONNX models."
]
@@ -89,9 +89,9 @@
"source": [
"## More FINN Resources\n",
"\n",
- "* **[List of publications](https://github.com/Xilinx/finn/blob/dev/docs/publications.md)**\n",
+ "* **[List of publications](https://github.com/Xilinx/finn/blob/master/docs/publications.md)**\n",
"* **[Roadmap](https://github.com/Xilinx/finn/projects/1)**\n",
- "* **[Status of example networks](https://github.com/Xilinx/finn/blob/dev/docs/example-networks.md)**\n",
+ "* **[Status of example networks](https://github.com/Xilinx/finn/blob/master/docs/example-networks.md)**\n",
"\n",
"\n",
"\n"
diff --git a/notebooks/end2end_example/tfc_end2end_example.ipynb b/notebooks/end2end_example/tfc_end2end_example.ipynb
index 3505d6f51..6fad8193c 100644
--- a/notebooks/end2end_example/tfc_end2end_example.ipynb
+++ b/notebooks/end2end_example/tfc_end2end_example.ipynb
@@ -713,7 +713,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "We can use the higher-level [HLSCustomOp](https://github.com/Xilinx/finn/blob/dev/src/finn/custom_op/fpgadataflow/__init__.py) wrappers for these nodes. These wrappers provide easy access to specific properties of these nodes, such as the folding factors (PE and SIMD). Let's have a look at which node attributes are defined by the CustomOp wrapper, and adjust the SIMD and PE attributes."
+ "We can use the higher-level [HLSCustomOp](https://github.com/Xilinx/finn/blob/master/src/finn/custom_op/fpgadataflow/__init__.py) wrappers for these nodes. These wrappers provide easy access to specific properties of these nodes, such as the folding factors (PE and SIMD). Let's have a look at which node attributes are defined by the CustomOp wrapper, and adjust the SIMD and PE attributes."
]
},
{
diff --git a/notebooks/internals/2_custom_op.ipynb b/notebooks/internals/2_custom_op.ipynb
index 7e91d8c40..9aaef9d42 100644
--- a/notebooks/internals/2_custom_op.ipynb
+++ b/notebooks/internals/2_custom_op.ipynb
@@ -306,7 +306,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "<font size=\"3\">`make_shape_compatible_op`: To use the flow of FINN, the transformation pass [infer_shapes](https://github.com/Xilinx/finn/blob/dev/src/finn/transformation/infer_shapes.py) is applied to the graphs in various places. In order for this transformation to be applied to CustomOps, they must first be converted to standard ONNX nodes with the same shape behavior. This means, nodes where the relationship between input and output shape is the same. \n",
+ "<font size=\"3\">`make_shape_compatible_op`: To use the flow of FINN, the transformation pass [infer_shapes](https://github.com/Xilinx/finn/blob/master/src/finn/transformation/infer_shapes.py) is applied to the graphs in various places. In order for this transformation to be applied to CustomOps, they must first be converted to standard ONNX nodes with the same shape behavior. This means, nodes where the relationship between input and output shape is the same. \n",
"\n",
"This is done at this point. Since the output shape of a multithreshold node is the same as the input shape, it can be replaced by a `\"Relu\"` node from the standard node library of onnx.</font>"
]
@@ -322,7 +322,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "<font size=\"3\">`execute_node`: This function allows the execution of the node, depending on the CustomOp a different functionality has to be implemented. In the case of the multithreshold node the input values and the thresholds are first extracted and after the attributes for the output scaling have been retrieved, the output is calculated with the help of a separate function. For more details regarding this function please take a look in the code [here](https://github.com/Xilinx/finn/blob/dev/src/finn/custom_op/multithreshold.py). </font>"
+ "<font size=\"3\">`execute_node`: This function allows the execution of the node, depending on the CustomOp a different functionality has to be implemented. In the case of the multithreshold node the input values and the thresholds are first extracted and after the attributes for the output scaling have been retrieved, the output is calculated with the help of a separate function. For more details regarding this function please take a look in the code [here](https://github.com/Xilinx/finn/blob/master/src/finn/custom_op/multithreshold.py). </font>"
]
},
{
--
GitLab