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new 101dfb5 [Doc] Misc doc fix (#5672)
101dfb5 is described below
commit 101dfb5a5ad2578cd786fc9c9e93e181bfe0f868
Author: Zhao Wu <[email protected]>
AuthorDate: Tue May 26 23:15:44 2020 +0800
[Doc] Misc doc fix (#5672)
---
docs/dev/convert_layout.rst | 2 +-
tutorials/frontend/deploy_prequantized_tflite.py | 1 +
2 files changed, 2 insertions(+), 1 deletion(-)
diff --git a/docs/dev/convert_layout.rst b/docs/dev/convert_layout.rst
index ee5350c..07ebc20 100644
--- a/docs/dev/convert_layout.rst
+++ b/docs/dev/convert_layout.rst
@@ -246,7 +246,7 @@ In order to specify the layouts to convert to, we create a
mapping of heavily-la
# RemoveUnunsedFunctions is used to clean up the graph.
seq = tvm.transform.Sequential([relay.transform.RemoveUnusedFunctions(),
relay.transform.ConvertLayout(desired_layouts)])
- with relay.transform.PassContext(opt_level=3):
+ with tvm.transform.PassContext(opt_level=3):
mod = seq(mod)
# Call relay compilation
diff --git a/tutorials/frontend/deploy_prequantized_tflite.py
b/tutorials/frontend/deploy_prequantized_tflite.py
index 3cdd423..5fd6837 100644
--- a/tutorials/frontend/deploy_prequantized_tflite.py
+++ b/tutorials/frontend/deploy_prequantized_tflite.py
@@ -18,6 +18,7 @@
Deploy a Framework-prequantized Model with TVM - Part 3 (TFLite)
================================================================
**Author**: `Siju Samuel <https://github.com/siju-samuel>`_
+
Welcome to part 3 of the Deploy Framework-Prequantized Model with TVM tutorial.
In this part, we will start with a Quantized TFLite graph and then compile and
execute it via TVM.
