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9c408a6c
编写于
7月 05, 2018
作者:
L
liutuo
提交者:
liuqi
7月 09, 2018
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update docs
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docs/user_guide/advanced_usage.rst
docs/user_guide/advanced_usage.rst
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docs/user_guide/basic_usage.rst
docs/user_guide/basic_usage.rst
+16
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docs/user_guide/advanced_usage.rst
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9c408a6c
...
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@@ -108,7 +108,7 @@ in one deployment file.
adb shell getprop | grep "model\|version.sdk\|manufacturer\|hardware\|platform\|brand"
# command for generating sha256_sum
sha256sum path/to/your/file
sha256sum
/
path/to/your/file
=========
...
...
docs/user_guide/basic_usage.rst
浏览文件 @
9c408a6c
...
...
@@ -36,13 +36,13 @@ Here we use the mobilenet-v2 model as an example.
git clone https://github.com/XiaoMi/mace-models.git
3. Build
MACE
.
3. Build
a general MACE library
.
.. code:: sh
cd path/to/mace
# Build library
python tools/converter.py build --config=path/to/mace-models/mobilenet-v2/mobilenet-v2.yml
python tools/converter.py build --config=
/
path/to/mace-models/mobilenet-v2/mobilenet-v2.yml
4. Convert the model to MACE format model.
...
...
@@ -51,7 +51,7 @@ Here we use the mobilenet-v2 model as an example.
cd path/to/mace
# Build library
python tools/converter.py build --config=path/to/mace-models/mobilenet-v2/mobilenet-v2.yml
python tools/converter.py build --config=
/
path/to/mace-models/mobilenet-v2/mobilenet-v2.yml
5. Run the model.
...
...
@@ -59,11 +59,11 @@ Here we use the mobilenet-v2 model as an example.
.. code:: sh
# Test model run time
python tools/converter.py run --config=path/to/mace-models/mobilenet-v2/mobilenet-v2.yml --round=100
python tools/converter.py run --config=
/
path/to/mace-models/mobilenet-v2/mobilenet-v2.yml --round=100
# Validate the correctness by comparing the results against the
# original model and framework, measured with cosine distance for similarity.
python tools/converter.py run --config=path/to/mace-models/mobilenet-v2/mobilenet-v2.yml --validate
python tools/converter.py run --config=
/
path/to/mace-models/mobilenet-v2/mobilenet-v2.yml --validate
Build your own model
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@@ -75,7 +75,7 @@ This part will show you how to use your pre-trained model in MACE.
1. Prepare your model
======================
Mace now supports models from Tensorflow and Caffe(more frameworks will be supported).
Mace now supports models from Tensorflow and Caffe
(more frameworks will be supported).
- TensorFlow
...
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@@ -143,23 +143,19 @@ Modify one of them and use it for your own case.
.. literalinclude:: models/demo_app_models_caffe.yml
:language: yaml
More details about model deployment file
, please refer to
:doc:`advanced_usage`.
More details about model deployment file
are in
:doc:`advanced_usage`.
======================
3. Convert your model
======================
When the deployment file is ready for your model, you can use MACE converter tool to convert your model(s).
To convert your pre-trained model to a MACE model, you need to set ``build_type:proto`` in your model deployment file.
And then run this command:
When the deployment file is ready, you can use MACE converter tool to convert your model(s).
.. code:: bash
python tools/converter.py convert --config=
path/to/your/model_deployment
.yml
python tools/converter.py convert --config=
/path/to/your/model_deployment_file
.yml
This command will download or load your pre-trained model and convert it to a MACE model proto file and weights file.
This command will download or load your pre-trained model and convert it to a MACE model proto file and weights
data
file.
The generated model files will be stored in ``build/${library_name}/model`` folder.
.. warning::
...
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@@ -171,22 +167,19 @@ The generated model files will be stored in ``build/${library_name}/model`` fold
4. Build MACE into a library
=============================
MACE can be built into either a static or a shared library (which is
specified by ``linkshared`` in YAML model deployment file).
Use bazel to build MACE source code into a library.
.. code:: sh
cd path/to/mace
# Build library
bazel build --config
=path/to/your/model_deployment_file.yml
bazel build --config
android mace:libmace --define neon=true --define openmp=true -cpu=arm64-v8a
The above command will generate
library files in the ``build/${library_name}/libs`` folder
.
The above command will generate
a library as ``bazel-bin/mace/libmace.so``
.
.. warning::
1. Please verify th
e target_abis params in the above command and the
deployment file are the same.
1. Please verify th
at the target_abis param in the above command and your
deployment file are the same.
2. If you want to build a library for a specific soc, please refer to :doc:`advanced_usage`.
...
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@@ -204,11 +197,11 @@ to run and validate your model.
.. code:: sh
# Test model run time
python tools/converter.py run --config=path/to/your/model_deployment_file.yml --round=100
python tools/converter.py run --config=
/
path/to/your/model_deployment_file.yml --round=100
# Validate the correctness by comparing the results against the
# original model and framework, measured with cosine distance for similarity.
python tools/converter.py run --config=path/to/your/model_deployment_file.yml --validate
python tools/converter.py run --config=
/
path/to/your/model_deployment_file.yml --validate
* **benchmark**
...
...
@@ -217,7 +210,7 @@ to run and validate your model.
.. code:: sh
# Benchmark model, get detailed statistics of each Op.
python tools/converter.py benchmark --config=path/to/your/model_deployment_file.yml
python tools/converter.py benchmark --config=
/
path/to/your/model_deployment_file.yml
=======================================
...
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