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From GitBox <...@apache.org>
Subject [GitHub] [incubator-tvm] liangfu commented on a change in pull request #5655: Add MicroTVM tutorial using the STM32F746 discovery board
Date Wed, 10 Jun 2020 10:24:28 GMT

liangfu commented on a change in pull request #5655:
URL: https://github.com/apache/incubator-tvm/pull/5655#discussion_r437896159



##########
File path: tutorials/micro/micro_tflite.py
##########
@@ -0,0 +1,219 @@
+# 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.
+"""
+.. _tutorial-micro-tflite:
+
+Micro TVM with TFLite Models
+============================
+**Author**: `Tom Gall <https://github.com/tom-gall>`_
+
+This tutorial is an introduction to working with MicroTVM and TFLite models with Relay.
+"""
+######################################################################
+# Setup
+# -----
+#
+# To get started, TFLite package needs to be installed as prerequisite.
+# 
+# install tflite
+# .. code-block:: bash
+#
+#   pip install tflite=2.1.0 --user
+#
+# or you could generate TFLite package yourself. The steps are the following:
+#
+#   Get the flatc compiler.
+#   Please refer to https://github.com/google/flatbuffers for details
+#   and make sure it is properly installed.
+#
+# .. code-block:: bash
+#
+#   flatc --version
+#
+# Get the TFLite schema.
+#
+# .. code-block:: bash
+#
+#   wget https://raw.githubusercontent.com/tensorflow/tensorflow/r1.13/tensorflow/lite/schema/schema.fbs
+#
+# Generate TFLite package.
+#
+# .. code-block:: bash
+#
+#   flatc --python schema.fbs
+#
+# Add current folder (which contains generated tflite module) to PYTHONPATH.
+#
+# .. code-block:: bash
+#
+#   export PYTHONPATH=${PYTHONPATH:+$PYTHONPATH:}$(pwd)
+#
+# To validate that the TFLite package was installed successfully, ``python -c "import tflite"``
+#
+# CMSIS needs to be downloaded and the CMSIS_ST_PATH environment variable setup
+# This tutorial only supports the STM32F7xx series of boards.
+# Download from : https://www.st.com/en/embedded-software/stm32cubef7.html
+# After you've expanded the zip file
+#
+# .. code-block:: bash
+#
+# export CMSIS_ST_PATH=/path/to/STM32Cube_FW_F7_V1.16.0/Drivers/CMSIS
+#
+# Next we need to download a pretrained TFLite model. When working with microcontrollers
+# you need to be mindful these are highly resource constrained devices as such standard 
+# models like Mobilenet may not fit into their modest memory. 
+#
+# For this tutorial, we'll make use of one of the TF Micro example models.
+# 
+# If you wish to replicate the training steps see:
+# https://github.com/tensorflow/tensorflow/tree/master/tensorflow/lite/micro/examples/hello_world/train
+#
+# .. code-block:: bash
+#
+# if you download the example pretrained model from
+#   wget https://storage.googleapis.com/download.tensorflow.org/models/tflite/micro/hello_world_2020_04_13.zip
+#   unzip hello_world_2020_04_13.zip
+#   this will fail due to an unimplemented opcode (114)
+#   I've saved an older version of the pre-trailed model and made it available on linaro.org
+
+######################################################################
+# Python imports for tvm, numpy etc
+# ----------------------------------------------
+import os
+import numpy as np
+import tvm
+import tvm.micro as micro
+import requests
+
+from tvm.contrib import graph_runtime, util
+from tvm import relay
+
+
+######################################################################
+# Load the pretrained TFLite model from a file in your current 
+# directory into a buffer
+model_url = 'https://people.linaro.org/~tom.gall/sine_model.tflite'
+model_file = 'sine_model.tflite'
+r = requests.get(model_url, allow_redirects=True)
+
+open(model_file,'wb').write(r.content)
+######################################################################
+# Uncomment the following code to load the model from a local 
+# directory
+# Load the pretrained TFLite model from a file in your current 
+# directory into a buffer
+# model_dir ="./"
+# tflite_model_file = os.path.join(model_dir, "sine_model.tflite")
+tflite_model_buf = open(model_file, "rb").read()
+
+######################################################################
+# Using the buffer, transform into a tflite model python object
+try:
+    import tflite
+    tflite_model = tflite.Model.GetRootAsModel(tflite_model_buf, 0)
+except AttributeError:
+    import tflite.Model
+    tflite_model = tflite.Model.Model.GetRootAsModel(tflite_model_buf, 0)
+
+######################################################################
+# Print out the version of the model
+version = tflite_model.Version()
+print ("Model Version: " + str(version))
+
+
+######################################################################
+# Setup the device config which is what will be used to communicate
+# with the microcontroller (a STM32F746 Discovery board)
+TARGET = 'c -device=micro_dev'
+dev_config = micro.device.arm.stm32f746xx.generate_config("127.0.0.1", 6666)
+
+
+######################################################################
+# Parse the python model object to convert it into a relay module
+# and weights
+# It is important to note that the input tensor name must match what
+# is contained in the model.
+# If you are unsure what that might be, this can be discovered by using
+# the visualize.py script within the Tensorflow project.
+# See : How do I inspect a .tflite file? https://www.tensorflow.org/lite/guide/faq 
+input_tensor = "dense_4_input"
+input_shape = (1,)
+input_dtype = "float32"
+
+mod, params = relay.frontend.from_tflite(tflite_model,
+                                         shape_dict={input_tensor: input_shape},
+                                         dtype_dict={input_tensor: input_dtype})
+
+######################################################################
+# You'll need to uncomment the following blocks of code for the 
+# example to run on device.
+# Next with the dev_config, we establish a micro session and create
+# a context
+#
+# .. code-block:: python
+#
+# with micro.Session(dev_config) as sess:
+#   ctx = tvm.micro_dev(0)
+
+######################################################################
+# Now we create a build config for relay. turning off two options
+# and then calling relay.build which will result in a C source
+#
+# .. code-block:: python
+#
+#    disable_vectorize = tvm.target.build_config(disable_vectorize=True)
+#    disable_fusion = relay.build_config(disabled_pass={'FuseOps'})
+#    with disable_vectorize, disable_fusion:
+#        graph, c_mod, params = relay.build(mod, target=TARGET, params=params)
+
+######################################################################
+# With the c_mod that is the handle to our c sourcecode, we create a
+# micro module, followed by a compiled object which behind the scenes
+# is linked to the microTVM runtime for running on the target board
+# .. code-block:: python
+#
+#    micro_mod = micro.create_micro_mod(c_mod, dev_config)
+#    mod = graph_runtime.create(graph, micro_mod, ctx)
+
+######################################################################
+# Pass the weights to get ready to do some inference
+# .. code-block:: python
+#
+#    ``mod.set_input(**params)``
+
+######################################################################
+# The model consumes a single float32. Construct a tvm.nd.array object
+# with a single contrived number as input. For this model values of 
+# 0 to 2Pi are acceptible.

Review comment:
       typo: acceptible -> acceptable

##########
File path: tutorials/micro/micro_tflite.py
##########
@@ -0,0 +1,219 @@
+# 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.
+"""
+.. _tutorial-micro-tflite:
+
+Micro TVM with TFLite Models
+============================
+**Author**: `Tom Gall <https://github.com/tom-gall>`_
+
+This tutorial is an introduction to working with MicroTVM and TFLite models with Relay.
+"""
+######################################################################
+# Setup
+# -----
+#
+# To get started, TFLite package needs to be installed as prerequisite.
+# 
+# install tflite
+# .. code-block:: bash
+#
+#   pip install tflite=2.1.0 --user
+#
+# or you could generate TFLite package yourself. The steps are the following:
+#
+#   Get the flatc compiler.
+#   Please refer to https://github.com/google/flatbuffers for details
+#   and make sure it is properly installed.
+#
+# .. code-block:: bash
+#
+#   flatc --version
+#
+# Get the TFLite schema.
+#
+# .. code-block:: bash
+#
+#   wget https://raw.githubusercontent.com/tensorflow/tensorflow/r1.13/tensorflow/lite/schema/schema.fbs
+#
+# Generate TFLite package.
+#
+# .. code-block:: bash
+#
+#   flatc --python schema.fbs
+#
+# Add current folder (which contains generated tflite module) to PYTHONPATH.
+#
+# .. code-block:: bash
+#
+#   export PYTHONPATH=${PYTHONPATH:+$PYTHONPATH:}$(pwd)
+#
+# To validate that the TFLite package was installed successfully, ``python -c "import tflite"``
+#
+# CMSIS needs to be downloaded and the CMSIS_ST_PATH environment variable setup
+# This tutorial only supports the STM32F7xx series of boards.
+# Download from : https://www.st.com/en/embedded-software/stm32cubef7.html
+# After you've expanded the zip file
+#
+# .. code-block:: bash
+#
+# export CMSIS_ST_PATH=/path/to/STM32Cube_FW_F7_V1.16.0/Drivers/CMSIS
+#
+# Next we need to download a pretrained TFLite model. When working with microcontrollers
+# you need to be mindful these are highly resource constrained devices as such standard 
+# models like Mobilenet may not fit into their modest memory. 
+#
+# For this tutorial, we'll make use of one of the TF Micro example models.
+# 
+# If you wish to replicate the training steps see:
+# https://github.com/tensorflow/tensorflow/tree/master/tensorflow/lite/micro/examples/hello_world/train
+#
+# .. code-block:: bash
+#
+# if you download the example pretrained model from
+#   wget https://storage.googleapis.com/download.tensorflow.org/models/tflite/micro/hello_world_2020_04_13.zip
+#   unzip hello_world_2020_04_13.zip
+#   this will fail due to an unimplemented opcode (114)
+#   I've saved an older version of the pre-trailed model and made it available on linaro.org
+
+######################################################################
+# Python imports for tvm, numpy etc
+# ----------------------------------------------
+import os
+import numpy as np
+import tvm
+import tvm.micro as micro
+import requests
+
+from tvm.contrib import graph_runtime, util
+from tvm import relay
+
+
+######################################################################
+# Load the pretrained TFLite model from a file in your current 
+# directory into a buffer
+model_url = 'https://people.linaro.org/~tom.gall/sine_model.tflite'
+model_file = 'sine_model.tflite'
+r = requests.get(model_url, allow_redirects=True)
+
+open(model_file,'wb').write(r.content)
+######################################################################
+# Uncomment the following code to load the model from a local 
+# directory
+# Load the pretrained TFLite model from a file in your current 
+# directory into a buffer
+# model_dir ="./"
+# tflite_model_file = os.path.join(model_dir, "sine_model.tflite")
+tflite_model_buf = open(model_file, "rb").read()
+
+######################################################################
+# Using the buffer, transform into a tflite model python object
+try:
+    import tflite
+    tflite_model = tflite.Model.GetRootAsModel(tflite_model_buf, 0)
+except AttributeError:
+    import tflite.Model
+    tflite_model = tflite.Model.Model.GetRootAsModel(tflite_model_buf, 0)
+
+######################################################################
+# Print out the version of the model
+version = tflite_model.Version()
+print ("Model Version: " + str(version))
+
+
+######################################################################
+# Setup the device config which is what will be used to communicate
+# with the microcontroller (a STM32F746 Discovery board)
+TARGET = 'c -device=micro_dev'
+dev_config = micro.device.arm.stm32f746xx.generate_config("127.0.0.1", 6666)
+
+
+######################################################################
+# Parse the python model object to convert it into a relay module
+# and weights
+# It is important to note that the input tensor name must match what
+# is contained in the model.
+# If you are unsure what that might be, this can be discovered by using
+# the visualize.py script within the Tensorflow project.
+# See : How do I inspect a .tflite file? https://www.tensorflow.org/lite/guide/faq 
+input_tensor = "dense_4_input"
+input_shape = (1,)
+input_dtype = "float32"
+
+mod, params = relay.frontend.from_tflite(tflite_model,
+                                         shape_dict={input_tensor: input_shape},
+                                         dtype_dict={input_tensor: input_dtype})
+
+######################################################################
+# You'll need to uncomment the following blocks of code for the 
+# example to run on device.
+# Next with the dev_config, we establish a micro session and create
+# a context
+#
+# .. code-block:: python
+#
+# with micro.Session(dev_config) as sess:
+#   ctx = tvm.micro_dev(0)
+
+######################################################################
+# Now we create a build config for relay. turning off two options
+# and then calling relay.build which will result in a C source
+#
+# .. code-block:: python
+#
+#    disable_vectorize = tvm.target.build_config(disable_vectorize=True)
+#    disable_fusion = relay.build_config(disabled_pass={'FuseOps'})
+#    with disable_vectorize, disable_fusion:
+#        graph, c_mod, params = relay.build(mod, target=TARGET, params=params)
+
+######################################################################
+# With the c_mod that is the handle to our c sourcecode, we create a
+# micro module, followed by a compiled object which behind the scenes
+# is linked to the microTVM runtime for running on the target board
+# .. code-block:: python
+#
+#    micro_mod = micro.create_micro_mod(c_mod, dev_config)
+#    mod = graph_runtime.create(graph, micro_mod, ctx)
+
+######################################################################
+# Pass the weights to get ready to do some inference
+# .. code-block:: python
+#
+#    ``mod.set_input(**params)``

Review comment:
       Please remove ``

##########
File path: tutorials/micro/micro_tflite.py
##########
@@ -0,0 +1,219 @@
+# 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.
+"""
+.. _tutorial-micro-tflite:
+
+Micro TVM with TFLite Models
+============================
+**Author**: `Tom Gall <https://github.com/tom-gall>`_
+
+This tutorial is an introduction to working with MicroTVM and TFLite models with Relay.
+"""
+######################################################################
+# Setup
+# -----
+#
+# To get started, TFLite package needs to be installed as prerequisite.
+# 
+# install tflite
+# .. code-block:: bash
+#
+#   pip install tflite=2.1.0 --user
+#
+# or you could generate TFLite package yourself. The steps are the following:
+#
+#   Get the flatc compiler.
+#   Please refer to https://github.com/google/flatbuffers for details
+#   and make sure it is properly installed.
+#
+# .. code-block:: bash
+#
+#   flatc --version
+#
+# Get the TFLite schema.
+#
+# .. code-block:: bash
+#
+#   wget https://raw.githubusercontent.com/tensorflow/tensorflow/r1.13/tensorflow/lite/schema/schema.fbs
+#
+# Generate TFLite package.
+#
+# .. code-block:: bash
+#
+#   flatc --python schema.fbs
+#
+# Add current folder (which contains generated tflite module) to PYTHONPATH.
+#
+# .. code-block:: bash
+#
+#   export PYTHONPATH=${PYTHONPATH:+$PYTHONPATH:}$(pwd)
+#
+# To validate that the TFLite package was installed successfully, ``python -c "import tflite"``
+#
+# CMSIS needs to be downloaded and the CMSIS_ST_PATH environment variable setup
+# This tutorial only supports the STM32F7xx series of boards.
+# Download from : https://www.st.com/en/embedded-software/stm32cubef7.html
+# After you've expanded the zip file
+#
+# .. code-block:: bash
+#
+# export CMSIS_ST_PATH=/path/to/STM32Cube_FW_F7_V1.16.0/Drivers/CMSIS

Review comment:
       Please follow RST syntax to put the extra indentation, otherwise it's not rendered
as code. Again, try running `make html` locally.

##########
File path: tutorials/micro/micro_tflite.py
##########
@@ -0,0 +1,219 @@
+# 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.
+"""
+.. _tutorial-micro-tflite:
+
+Micro TVM with TFLite Models
+============================
+**Author**: `Tom Gall <https://github.com/tom-gall>`_
+
+This tutorial is an introduction to working with MicroTVM and TFLite models with Relay.
+"""
+######################################################################
+# Setup
+# -----
+#
+# To get started, TFLite package needs to be installed as prerequisite.
+# 
+# install tflite
+# .. code-block:: bash
+#
+#   pip install tflite=2.1.0 --user
+#
+# or you could generate TFLite package yourself. The steps are the following:
+#
+#   Get the flatc compiler.
+#   Please refer to https://github.com/google/flatbuffers for details
+#   and make sure it is properly installed.
+#
+# .. code-block:: bash
+#
+#   flatc --version
+#
+# Get the TFLite schema.
+#
+# .. code-block:: bash
+#
+#   wget https://raw.githubusercontent.com/tensorflow/tensorflow/r1.13/tensorflow/lite/schema/schema.fbs
+#
+# Generate TFLite package.
+#
+# .. code-block:: bash
+#
+#   flatc --python schema.fbs
+#
+# Add current folder (which contains generated tflite module) to PYTHONPATH.
+#
+# .. code-block:: bash
+#
+#   export PYTHONPATH=${PYTHONPATH:+$PYTHONPATH:}$(pwd)
+#
+# To validate that the TFLite package was installed successfully, ``python -c "import tflite"``
+#
+# CMSIS needs to be downloaded and the CMSIS_ST_PATH environment variable setup
+# This tutorial only supports the STM32F7xx series of boards.
+# Download from : https://www.st.com/en/embedded-software/stm32cubef7.html
+# After you've expanded the zip file
+#
+# .. code-block:: bash
+#
+# export CMSIS_ST_PATH=/path/to/STM32Cube_FW_F7_V1.16.0/Drivers/CMSIS
+#
+# Next we need to download a pretrained TFLite model. When working with microcontrollers
+# you need to be mindful these are highly resource constrained devices as such standard 
+# models like Mobilenet may not fit into their modest memory. 
+#
+# For this tutorial, we'll make use of one of the TF Micro example models.
+# 
+# If you wish to replicate the training steps see:
+# https://github.com/tensorflow/tensorflow/tree/master/tensorflow/lite/micro/examples/hello_world/train
+#
+# .. code-block:: bash
+#
+# if you download the example pretrained model from
+#   wget https://storage.googleapis.com/download.tensorflow.org/models/tflite/micro/hello_world_2020_04_13.zip
+#   unzip hello_world_2020_04_13.zip
+#   this will fail due to an unimplemented opcode (114)
+#   I've saved an older version of the pre-trailed model and made it available on linaro.org
+
+######################################################################
+# Python imports for tvm, numpy etc
+# ----------------------------------------------
+import os
+import numpy as np
+import tvm
+import tvm.micro as micro
+import requests
+
+from tvm.contrib import graph_runtime, util
+from tvm import relay
+
+
+######################################################################
+# Load the pretrained TFLite model from a file in your current 
+# directory into a buffer
+model_url = 'https://people.linaro.org/~tom.gall/sine_model.tflite'
+model_file = 'sine_model.tflite'
+r = requests.get(model_url, allow_redirects=True)
+
+open(model_file,'wb').write(r.content)
+######################################################################
+# Uncomment the following code to load the model from a local 
+# directory
+# Load the pretrained TFLite model from a file in your current 
+# directory into a buffer
+# model_dir ="./"
+# tflite_model_file = os.path.join(model_dir, "sine_model.tflite")
+tflite_model_buf = open(model_file, "rb").read()
+
+######################################################################
+# Using the buffer, transform into a tflite model python object
+try:
+    import tflite
+    tflite_model = tflite.Model.GetRootAsModel(tflite_model_buf, 0)
+except AttributeError:
+    import tflite.Model
+    tflite_model = tflite.Model.Model.GetRootAsModel(tflite_model_buf, 0)
+
+######################################################################
+# Print out the version of the model
+version = tflite_model.Version()
+print ("Model Version: " + str(version))
+
+
+######################################################################
+# Setup the device config which is what will be used to communicate
+# with the microcontroller (a STM32F746 Discovery board)
+TARGET = 'c -device=micro_dev'
+dev_config = micro.device.arm.stm32f746xx.generate_config("127.0.0.1", 6666)
+
+
+######################################################################
+# Parse the python model object to convert it into a relay module
+# and weights
+# It is important to note that the input tensor name must match what
+# is contained in the model.
+# If you are unsure what that might be, this can be discovered by using
+# the visualize.py script within the Tensorflow project.
+# See : How do I inspect a .tflite file? https://www.tensorflow.org/lite/guide/faq 
+input_tensor = "dense_4_input"
+input_shape = (1,)
+input_dtype = "float32"
+
+mod, params = relay.frontend.from_tflite(tflite_model,
+                                         shape_dict={input_tensor: input_shape},
+                                         dtype_dict={input_tensor: input_dtype})
+
+######################################################################
+# You'll need to uncomment the following blocks of code for the 
+# example to run on device.
+# Next with the dev_config, we establish a micro session and create
+# a context
+#
+# .. code-block:: python
+#
+# with micro.Session(dev_config) as sess:
+#   ctx = tvm.micro_dev(0)

Review comment:
       I think the indentation here should be 4 spaces.

##########
File path: tutorials/micro/micro_tflite.py
##########
@@ -0,0 +1,219 @@
+# 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.
+"""
+.. _tutorial-micro-tflite:
+
+Micro TVM with TFLite Models
+============================
+**Author**: `Tom Gall <https://github.com/tom-gall>`_
+
+This tutorial is an introduction to working with MicroTVM and TFLite models with Relay.
+"""
+######################################################################
+# Setup
+# -----
+#
+# To get started, TFLite package needs to be installed as prerequisite.
+# 
+# install tflite
+# .. code-block:: bash
+#
+#   pip install tflite=2.1.0 --user
+#
+# or you could generate TFLite package yourself. The steps are the following:
+#
+#   Get the flatc compiler.
+#   Please refer to https://github.com/google/flatbuffers for details
+#   and make sure it is properly installed.
+#
+# .. code-block:: bash
+#
+#   flatc --version
+#
+# Get the TFLite schema.
+#
+# .. code-block:: bash
+#
+#   wget https://raw.githubusercontent.com/tensorflow/tensorflow/r1.13/tensorflow/lite/schema/schema.fbs
+#
+# Generate TFLite package.
+#
+# .. code-block:: bash
+#
+#   flatc --python schema.fbs
+#
+# Add current folder (which contains generated tflite module) to PYTHONPATH.
+#
+# .. code-block:: bash
+#
+#   export PYTHONPATH=${PYTHONPATH:+$PYTHONPATH:}$(pwd)
+#
+# To validate that the TFLite package was installed successfully, ``python -c "import tflite"``
+#
+# CMSIS needs to be downloaded and the CMSIS_ST_PATH environment variable setup
+# This tutorial only supports the STM32F7xx series of boards.
+# Download from : https://www.st.com/en/embedded-software/stm32cubef7.html
+# After you've expanded the zip file
+#
+# .. code-block:: bash
+#
+# export CMSIS_ST_PATH=/path/to/STM32Cube_FW_F7_V1.16.0/Drivers/CMSIS
+#
+# Next we need to download a pretrained TFLite model. When working with microcontrollers
+# you need to be mindful these are highly resource constrained devices as such standard 
+# models like Mobilenet may not fit into their modest memory. 
+#
+# For this tutorial, we'll make use of one of the TF Micro example models.
+# 
+# If you wish to replicate the training steps see:
+# https://github.com/tensorflow/tensorflow/tree/master/tensorflow/lite/micro/examples/hello_world/train
+#
+# .. code-block:: bash
+#
+# if you download the example pretrained model from
+#   wget https://storage.googleapis.com/download.tensorflow.org/models/tflite/micro/hello_world_2020_04_13.zip
+#   unzip hello_world_2020_04_13.zip
+#   this will fail due to an unimplemented opcode (114)
+#   I've saved an older version of the pre-trailed model and made it available on linaro.org
+
+######################################################################
+# Python imports for tvm, numpy etc
+# ----------------------------------------------
+import os
+import numpy as np
+import tvm
+import tvm.micro as micro
+import requests
+
+from tvm.contrib import graph_runtime, util
+from tvm import relay
+
+
+######################################################################
+# Load the pretrained TFLite model from a file in your current 
+# directory into a buffer
+model_url = 'https://people.linaro.org/~tom.gall/sine_model.tflite'
+model_file = 'sine_model.tflite'
+r = requests.get(model_url, allow_redirects=True)
+
+open(model_file,'wb').write(r.content)
+######################################################################
+# Uncomment the following code to load the model from a local 
+# directory
+# Load the pretrained TFLite model from a file in your current 
+# directory into a buffer
+# model_dir ="./"
+# tflite_model_file = os.path.join(model_dir, "sine_model.tflite")
+tflite_model_buf = open(model_file, "rb").read()
+
+######################################################################
+# Using the buffer, transform into a tflite model python object
+try:
+    import tflite
+    tflite_model = tflite.Model.GetRootAsModel(tflite_model_buf, 0)
+except AttributeError:
+    import tflite.Model
+    tflite_model = tflite.Model.Model.GetRootAsModel(tflite_model_buf, 0)
+
+######################################################################
+# Print out the version of the model
+version = tflite_model.Version()
+print ("Model Version: " + str(version))
+
+
+######################################################################
+# Setup the device config which is what will be used to communicate
+# with the microcontroller (a STM32F746 Discovery board)
+TARGET = 'c -device=micro_dev'
+dev_config = micro.device.arm.stm32f746xx.generate_config("127.0.0.1", 6666)
+
+
+######################################################################
+# Parse the python model object to convert it into a relay module
+# and weights
+# It is important to note that the input tensor name must match what
+# is contained in the model.
+# If you are unsure what that might be, this can be discovered by using
+# the visualize.py script within the Tensorflow project.
+# See : How do I inspect a .tflite file? https://www.tensorflow.org/lite/guide/faq 
+input_tensor = "dense_4_input"
+input_shape = (1,)
+input_dtype = "float32"
+
+mod, params = relay.frontend.from_tflite(tflite_model,
+                                         shape_dict={input_tensor: input_shape},
+                                         dtype_dict={input_tensor: input_dtype})
+
+######################################################################
+# You'll need to uncomment the following blocks of code for the 
+# example to run on device.
+# Next with the dev_config, we establish a micro session and create
+# a context
+#
+# .. code-block:: python
+#
+# with micro.Session(dev_config) as sess:

Review comment:
       Please follow RST syntax to put the extra indentation.

##########
File path: tutorials/micro/micro_tflite.py
##########
@@ -0,0 +1,219 @@
+# 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.
+"""
+.. _tutorial-micro-tflite:
+
+Micro TVM with TFLite Models
+============================
+**Author**: `Tom Gall <https://github.com/tom-gall>`_
+
+This tutorial is an introduction to working with MicroTVM and TFLite models with Relay.
+"""
+######################################################################
+# Setup
+# -----
+#
+# To get started, TFLite package needs to be installed as prerequisite.
+# 
+# install tflite
+# .. code-block:: bash
+#
+#   pip install tflite=2.1.0 --user

Review comment:
       Please follow RST syntax to put the extra indentation, otherwise it's not rendered
as code. 
   
   Please try reproduce the the rendered html by running
   ```bash
   make html
   ```
   in `<tvm_root>/docs` directory, so that the tutorial could be more reader friendly.

##########
File path: tutorials/micro/micro_tflite.py
##########
@@ -0,0 +1,219 @@
+# 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.
+"""
+.. _tutorial-micro-tflite:
+
+Micro TVM with TFLite Models
+============================
+**Author**: `Tom Gall <https://github.com/tom-gall>`_
+
+This tutorial is an introduction to working with MicroTVM and TFLite models with Relay.
+"""
+######################################################################
+# Setup
+# -----
+#
+# To get started, TFLite package needs to be installed as prerequisite.
+# 
+# install tflite
+# .. code-block:: bash
+#
+#   pip install tflite=2.1.0 --user
+#
+# or you could generate TFLite package yourself. The steps are the following:
+#
+#   Get the flatc compiler.
+#   Please refer to https://github.com/google/flatbuffers for details
+#   and make sure it is properly installed.
+#
+# .. code-block:: bash
+#
+#   flatc --version
+#
+# Get the TFLite schema.
+#
+# .. code-block:: bash
+#
+#   wget https://raw.githubusercontent.com/tensorflow/tensorflow/r1.13/tensorflow/lite/schema/schema.fbs
+#
+# Generate TFLite package.
+#
+# .. code-block:: bash
+#
+#   flatc --python schema.fbs
+#
+# Add current folder (which contains generated tflite module) to PYTHONPATH.
+#
+# .. code-block:: bash
+#
+#   export PYTHONPATH=${PYTHONPATH:+$PYTHONPATH:}$(pwd)
+#
+# To validate that the TFLite package was installed successfully, ``python -c "import tflite"``
+#
+# CMSIS needs to be downloaded and the CMSIS_ST_PATH environment variable setup
+# This tutorial only supports the STM32F7xx series of boards.
+# Download from : https://www.st.com/en/embedded-software/stm32cubef7.html
+# After you've expanded the zip file
+#
+# .. code-block:: bash
+#
+# export CMSIS_ST_PATH=/path/to/STM32Cube_FW_F7_V1.16.0/Drivers/CMSIS
+#
+# Next we need to download a pretrained TFLite model. When working with microcontrollers
+# you need to be mindful these are highly resource constrained devices as such standard 
+# models like Mobilenet may not fit into their modest memory. 
+#
+# For this tutorial, we'll make use of one of the TF Micro example models.
+# 
+# If you wish to replicate the training steps see:
+# https://github.com/tensorflow/tensorflow/tree/master/tensorflow/lite/micro/examples/hello_world/train
+#
+# .. code-block:: bash
+#
+# if you download the example pretrained model from

Review comment:
       This looks wield in the code block. Please put extra indentation, otherwise it's not
rendered as code.

##########
File path: tutorials/micro/README.txt
##########
@@ -0,0 +1,4 @@
+.. _tutorial-micro:

Review comment:
       if this directive is unused, please remove.

##########
File path: tutorials/micro/micro_tflite.py
##########
@@ -0,0 +1,219 @@
+# 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.
+"""
+.. _tutorial-micro-tflite:
+
+Micro TVM with TFLite Models
+============================
+**Author**: `Tom Gall <https://github.com/tom-gall>`_
+
+This tutorial is an introduction to working with MicroTVM and TFLite models with Relay.
+"""
+######################################################################
+# Setup
+# -----
+#
+# To get started, TFLite package needs to be installed as prerequisite.
+# 
+# install tflite
+# .. code-block:: bash

Review comment:
       place an empty line before this directive

##########
File path: docs/conf.py
##########
@@ -203,7 +203,8 @@
      '../tutorials/deployment',
      '../vta/tutorials/frontend',
      '../vta/tutorials/optimize',
-     '../vta/tutorials/autotvm'])
+     '../vta/tutorials/autotvm',
+     '../tutorials/micro'])

Review comment:
       move this entry above the vta tutorials

##########
File path: tutorials/micro/micro_tflite.py
##########
@@ -0,0 +1,219 @@
+# 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.
+"""
+.. _tutorial-micro-tflite:
+
+Micro TVM with TFLite Models
+============================
+**Author**: `Tom Gall <https://github.com/tom-gall>`_
+
+This tutorial is an introduction to working with MicroTVM and TFLite models with Relay.
+"""
+######################################################################
+# Setup
+# -----
+#
+# To get started, TFLite package needs to be installed as prerequisite.
+# 
+# install tflite
+# .. code-block:: bash
+#
+#   pip install tflite=2.1.0 --user
+#
+# or you could generate TFLite package yourself. The steps are the following:
+#
+#   Get the flatc compiler.
+#   Please refer to https://github.com/google/flatbuffers for details
+#   and make sure it is properly installed.
+#
+# .. code-block:: bash
+#
+#   flatc --version
+#
+# Get the TFLite schema.
+#
+# .. code-block:: bash
+#
+#   wget https://raw.githubusercontent.com/tensorflow/tensorflow/r1.13/tensorflow/lite/schema/schema.fbs
+#
+# Generate TFLite package.
+#
+# .. code-block:: bash
+#
+#   flatc --python schema.fbs
+#
+# Add current folder (which contains generated tflite module) to PYTHONPATH.
+#
+# .. code-block:: bash
+#
+#   export PYTHONPATH=${PYTHONPATH:+$PYTHONPATH:}$(pwd)
+#
+# To validate that the TFLite package was installed successfully, ``python -c "import tflite"``
+#
+# CMSIS needs to be downloaded and the CMSIS_ST_PATH environment variable setup
+# This tutorial only supports the STM32F7xx series of boards.
+# Download from : https://www.st.com/en/embedded-software/stm32cubef7.html
+# After you've expanded the zip file
+#
+# .. code-block:: bash
+#
+# export CMSIS_ST_PATH=/path/to/STM32Cube_FW_F7_V1.16.0/Drivers/CMSIS
+#
+# Next we need to download a pretrained TFLite model. When working with microcontrollers
+# you need to be mindful these are highly resource constrained devices as such standard 
+# models like Mobilenet may not fit into their modest memory. 
+#
+# For this tutorial, we'll make use of one of the TF Micro example models.
+# 
+# If you wish to replicate the training steps see:
+# https://github.com/tensorflow/tensorflow/tree/master/tensorflow/lite/micro/examples/hello_world/train
+#
+# .. code-block:: bash
+#
+# if you download the example pretrained model from
+#   wget https://storage.googleapis.com/download.tensorflow.org/models/tflite/micro/hello_world_2020_04_13.zip
+#   unzip hello_world_2020_04_13.zip
+#   this will fail due to an unimplemented opcode (114)
+#   I've saved an older version of the pre-trailed model and made it available on linaro.org
+
+######################################################################
+# Python imports for tvm, numpy etc
+# ----------------------------------------------
+import os
+import numpy as np
+import tvm
+import tvm.micro as micro
+import requests
+
+from tvm.contrib import graph_runtime, util
+from tvm import relay
+
+
+######################################################################
+# Load the pretrained TFLite model from a file in your current 
+# directory into a buffer
+model_url = 'https://people.linaro.org/~tom.gall/sine_model.tflite'
+model_file = 'sine_model.tflite'
+r = requests.get(model_url, allow_redirects=True)
+
+open(model_file,'wb').write(r.content)
+######################################################################
+# Uncomment the following code to load the model from a local 
+# directory
+# Load the pretrained TFLite model from a file in your current 
+# directory into a buffer
+# model_dir ="./"
+# tflite_model_file = os.path.join(model_dir, "sine_model.tflite")
+tflite_model_buf = open(model_file, "rb").read()
+
+######################################################################
+# Using the buffer, transform into a tflite model python object
+try:
+    import tflite
+    tflite_model = tflite.Model.GetRootAsModel(tflite_model_buf, 0)
+except AttributeError:
+    import tflite.Model
+    tflite_model = tflite.Model.Model.GetRootAsModel(tflite_model_buf, 0)
+
+######################################################################
+# Print out the version of the model
+version = tflite_model.Version()
+print ("Model Version: " + str(version))
+
+
+######################################################################
+# Setup the device config which is what will be used to communicate
+# with the microcontroller (a STM32F746 Discovery board)
+TARGET = 'c -device=micro_dev'
+dev_config = micro.device.arm.stm32f746xx.generate_config("127.0.0.1", 6666)
+
+
+######################################################################
+# Parse the python model object to convert it into a relay module
+# and weights
+# It is important to note that the input tensor name must match what
+# is contained in the model.
+# If you are unsure what that might be, this can be discovered by using
+# the visualize.py script within the Tensorflow project.
+# See : How do I inspect a .tflite file? https://www.tensorflow.org/lite/guide/faq 
+input_tensor = "dense_4_input"
+input_shape = (1,)
+input_dtype = "float32"
+
+mod, params = relay.frontend.from_tflite(tflite_model,
+                                         shape_dict={input_tensor: input_shape},
+                                         dtype_dict={input_tensor: input_dtype})
+
+######################################################################
+# You'll need to uncomment the following blocks of code for the 
+# example to run on device.
+# Next with the dev_config, we establish a micro session and create
+# a context
+#
+# .. code-block:: python
+#
+# with micro.Session(dev_config) as sess:
+#   ctx = tvm.micro_dev(0)
+
+######################################################################
+# Now we create a build config for relay. turning off two options
+# and then calling relay.build which will result in a C source
+#
+# .. code-block:: python
+#
+#    disable_vectorize = tvm.target.build_config(disable_vectorize=True)
+#    disable_fusion = relay.build_config(disabled_pass={'FuseOps'})
+#    with disable_vectorize, disable_fusion:
+#        graph, c_mod, params = relay.build(mod, target=TARGET, params=params)
+
+######################################################################
+# With the c_mod that is the handle to our c sourcecode, we create a
+# micro module, followed by a compiled object which behind the scenes
+# is linked to the microTVM runtime for running on the target board
+# .. code-block:: python
+#
+#    micro_mod = micro.create_micro_mod(c_mod, dev_config)
+#    mod = graph_runtime.create(graph, micro_mod, ctx)
+
+######################################################################
+# Pass the weights to get ready to do some inference
+# .. code-block:: python

Review comment:
       Please put an empty line above the RST directive, otherwise RST can't recognize this
directive.
   
   Again, try running `make html` locally.




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