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. 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