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Posted to commits@tvm.apache.org by GitBox <gi...@apache.org> on 2021/12/03 22:24:33 UTC
[GitHub] [tvm] hogepodge commented on a change in pull request #9633: Tvmc python tutorial
hogepodge commented on a change in pull request #9633:
URL: https://github.com/apache/tvm/pull/9633#discussion_r762321000
##########
File path: gallery/how_to/use_tvms_python_api/tvmc_python.py
##########
@@ -0,0 +1,252 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements. See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership. The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied. See the License for the
+# specific language governing permissions and limitations
+# under the License.
+"""
+Getting Starting using TVMC Python: a high-level API for TVM
+=============================================================
+**Author**:
+`Jocelyn Shiue <https://github.com/CircleSpin>`_,
+
+Welcome to TVMC Python
+======================
+Hi! Here we explain the scripting tool designed for the complete TVM beginner. 🙂
+
+Before we get started let's get an example model if you don't already have one.
+Follow the steps to download a resnet model via the terminal:
+
+ .. code-block:: bash
+
+ mkdir myscripts
+ cd myscripts
+ wget https://github.com/onnx/models/raw/master/vision/classification/resnet/model/resnet50-v2-7.onnx
+ mv resnet50-v2-7.onnx my_model.onnx
+ touch tvmcpythonintro.py
+
+Let's start editing the python file in your favorite text editor.
+
+################################################################################
+# Step 0: Imports
+# ---------------
+#
+# .. code-block:: python
+#
+# from tvm.driver import tvmc
+#
+
+################################################################################
+# Step 1: Load a model
+# --------------------
+#
+# Let's import our model into tvmc. This step converts a machine learning model from
+# a supported framework into TVM's high level graph representation language called relay.
+# This is to have a unified starting point for all models in tvm. The frameworks we currently
+# support are: Keras, ONNX, Tensorflow, TFLite, and PyTorch.
+#
+# .. code-block:: python
+# model = tvmc.load('my_model.onnx') #Step 1: Load
+#
+# If you'd like to see the relay, you can run:
+# ``model.summary()``
+#
+# All frameworks support over writing the input shapes with a shape_dict argument.
+# For most frameworks this is optional but for Pytorch this is necessary.
+#
+# .. code-block:: python
+# ### Step 1: Load shape_dict Style
+# # shape_dict = {'model_input_name1': [1, 3, 224, 224], 'input2': [1, 2, 3, 4], ...} #example format with random numbers
+# # model = tvmc.load(model_path, shape_dict=shape_dict) #Step 1: Load + shape_dict
+#
+# One way to see the model's input/shape_dict is via `netron <https://netron.app/>`_, . After opening the model,
+# click the first node to see the name(s) and shape(s) in the inputs section.
+
+
+################################################################################
+# Step 2: Compile
+# ----------------
+# Now that our model is in relay, our next step is to compile it to a desired
Review comment:
Thinking out loud here, for documentation we should starting thinking about a style guide for named parts of TVM. Should it be `Relay` or `relay`? Our choice should be consistently applied throughout the project.
##########
File path: gallery/how_to/use_tvms_python_api/tvmc_python.py
##########
@@ -0,0 +1,252 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements. See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership. The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied. See the License for the
+# specific language governing permissions and limitations
+# under the License.
+"""
+Getting Starting using TVMC Python: a high-level API for TVM
+=============================================================
+**Author**:
+`Jocelyn Shiue <https://github.com/CircleSpin>`_,
+
+Welcome to TVMC Python
+======================
+Hi! Here we explain the scripting tool designed for the complete TVM beginner. 🙂
+
+Before we get started let's get an example model if you don't already have one.
+Follow the steps to download a resnet model via the terminal:
+
+ .. code-block:: bash
+
+ mkdir myscripts
+ cd myscripts
+ wget https://github.com/onnx/models/raw/master/vision/classification/resnet/model/resnet50-v2-7.onnx
+ mv resnet50-v2-7.onnx my_model.onnx
+ touch tvmcpythonintro.py
+
+Let's start editing the python file in your favorite text editor.
+
+################################################################################
+# Step 0: Imports
+# ---------------
+#
+# .. code-block:: python
+#
+# from tvm.driver import tvmc
+#
+
+################################################################################
+# Step 1: Load a model
+# --------------------
+#
+# Let's import our model into tvmc. This step converts a machine learning model from
+# a supported framework into TVM's high level graph representation language called relay.
+# This is to have a unified starting point for all models in tvm. The frameworks we currently
+# support are: Keras, ONNX, Tensorflow, TFLite, and PyTorch.
+#
+# .. code-block:: python
+# model = tvmc.load('my_model.onnx') #Step 1: Load
+#
+# If you'd like to see the relay, you can run:
+# ``model.summary()``
+#
+# All frameworks support over writing the input shapes with a shape_dict argument.
+# For most frameworks this is optional but for Pytorch this is necessary.
+#
+# .. code-block:: python
+# ### Step 1: Load shape_dict Style
+# # shape_dict = {'model_input_name1': [1, 3, 224, 224], 'input2': [1, 2, 3, 4], ...} #example format with random numbers
+# # model = tvmc.load(model_path, shape_dict=shape_dict) #Step 1: Load + shape_dict
+#
+# One way to see the model's input/shape_dict is via `netron <https://netron.app/>`_, . After opening the model,
+# click the first node to see the name(s) and shape(s) in the inputs section.
+
+
+################################################################################
+# Step 2: Compile
+# ----------------
+# Now that our model is in relay, our next step is to compile it to a desired
+# hardware to run on. We refer to this hardware as a target. This compilation process
+# translates the model from relay into a lower-level language that the
+# target machine can understand.
Review comment:
I like this sentence. :-)
##########
File path: gallery/how_to/use_tvms_python_api/tvmc_python.py
##########
@@ -0,0 +1,252 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements. See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership. The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied. See the License for the
+# specific language governing permissions and limitations
+# under the License.
+"""
+Getting Starting using TVMC Python: a high-level API for TVM
+=============================================================
+**Author**:
+`Jocelyn Shiue <https://github.com/CircleSpin>`_,
+
+Welcome to TVMC Python
+======================
+Hi! Here we explain the scripting tool designed for the complete TVM beginner. 🙂
+
+Before we get started let's get an example model if you don't already have one.
+Follow the steps to download a resnet model via the terminal:
+
+ .. code-block:: bash
+
+ mkdir myscripts
+ cd myscripts
+ wget https://github.com/onnx/models/raw/master/vision/classification/resnet/model/resnet50-v2-7.onnx
+ mv resnet50-v2-7.onnx my_model.onnx
+ touch tvmcpythonintro.py
+
+Let's start editing the python file in your favorite text editor.
+
+################################################################################
+# Step 0: Imports
+# ---------------
+#
+# .. code-block:: python
+#
+# from tvm.driver import tvmc
+#
+
+################################################################################
+# Step 1: Load a model
+# --------------------
+#
+# Let's import our model into tvmc. This step converts a machine learning model from
+# a supported framework into TVM's high level graph representation language called relay.
+# This is to have a unified starting point for all models in tvm. The frameworks we currently
+# support are: Keras, ONNX, Tensorflow, TFLite, and PyTorch.
+#
+# .. code-block:: python
+# model = tvmc.load('my_model.onnx') #Step 1: Load
+#
+# If you'd like to see the relay, you can run:
+# ``model.summary()``
+#
+# All frameworks support over writing the input shapes with a shape_dict argument.
+# For most frameworks this is optional but for Pytorch this is necessary.
Review comment:
I would explain why a bit here
##########
File path: gallery/how_to/use_tvms_python_api/tvmc_python.py
##########
@@ -0,0 +1,252 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements. See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership. The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied. See the License for the
+# specific language governing permissions and limitations
+# under the License.
+"""
+Getting Starting using TVMC Python: a high-level API for TVM
+=============================================================
+**Author**:
+`Jocelyn Shiue <https://github.com/CircleSpin>`_,
+
+Welcome to TVMC Python
+======================
+Hi! Here we explain the scripting tool designed for the complete TVM beginner. 🙂
+
+Before we get started let's get an example model if you don't already have one.
+Follow the steps to download a resnet model via the terminal:
+
+ .. code-block:: bash
+
+ mkdir myscripts
+ cd myscripts
+ wget https://github.com/onnx/models/raw/master/vision/classification/resnet/model/resnet50-v2-7.onnx
+ mv resnet50-v2-7.onnx my_model.onnx
+ touch tvmcpythonintro.py
+
+Let's start editing the python file in your favorite text editor.
+
+################################################################################
+# Step 0: Imports
+# ---------------
+#
+# .. code-block:: python
+#
+# from tvm.driver import tvmc
+#
+
+################################################################################
+# Step 1: Load a model
+# --------------------
+#
+# Let's import our model into tvmc. This step converts a machine learning model from
+# a supported framework into TVM's high level graph representation language called relay.
+# This is to have a unified starting point for all models in tvm. The frameworks we currently
+# support are: Keras, ONNX, Tensorflow, TFLite, and PyTorch.
+#
+# .. code-block:: python
+# model = tvmc.load('my_model.onnx') #Step 1: Load
+#
+# If you'd like to see the relay, you can run:
+# ``model.summary()``
+#
+# All frameworks support over writing the input shapes with a shape_dict argument.
Review comment:
overwriting
##########
File path: gallery/how_to/use_tvms_python_api/tvmc_python.py
##########
@@ -0,0 +1,252 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements. See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership. The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied. See the License for the
+# specific language governing permissions and limitations
+# under the License.
+"""
+Getting Starting using TVMC Python: a high-level API for TVM
+=============================================================
+**Author**:
+`Jocelyn Shiue <https://github.com/CircleSpin>`_,
+
+Welcome to TVMC Python
+======================
+Hi! Here we explain the scripting tool designed for the complete TVM beginner. 🙂
+
+Before we get started let's get an example model if you don't already have one.
+Follow the steps to download a resnet model via the terminal:
+
+ .. code-block:: bash
+
+ mkdir myscripts
+ cd myscripts
+ wget https://github.com/onnx/models/raw/master/vision/classification/resnet/model/resnet50-v2-7.onnx
+ mv resnet50-v2-7.onnx my_model.onnx
+ touch tvmcpythonintro.py
+
+Let's start editing the python file in your favorite text editor.
+
+################################################################################
+# Step 0: Imports
+# ---------------
+#
+# .. code-block:: python
+#
+# from tvm.driver import tvmc
+#
+
+################################################################################
+# Step 1: Load a model
+# --------------------
+#
+# Let's import our model into tvmc. This step converts a machine learning model from
+# a supported framework into TVM's high level graph representation language called relay.
+# This is to have a unified starting point for all models in tvm. The frameworks we currently
+# support are: Keras, ONNX, Tensorflow, TFLite, and PyTorch.
+#
+# .. code-block:: python
+# model = tvmc.load('my_model.onnx') #Step 1: Load
+#
+# If you'd like to see the relay, you can run:
+# ``model.summary()``
+#
+# All frameworks support over writing the input shapes with a shape_dict argument.
+# For most frameworks this is optional but for Pytorch this is necessary.
+#
+# .. code-block:: python
+# ### Step 1: Load shape_dict Style
+# # shape_dict = {'model_input_name1': [1, 3, 224, 224], 'input2': [1, 2, 3, 4], ...} #example format with random numbers
+# # model = tvmc.load(model_path, shape_dict=shape_dict) #Step 1: Load + shape_dict
+#
+# One way to see the model's input/shape_dict is via `netron <https://netron.app/>`_, . After opening the model,
Review comment:
Add something like "you can use Netron to investigate the model's input shape if you're unsure of what it is..."
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