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From GitBox <...@apache.org>
Subject [GitHub] [incubator-tvm] tqchen commented on a change in pull request #5655: Add MicroTVM tutorial using the STM32F746 discovery board
Date Wed, 10 Jun 2020 23:33:50 GMT

tqchen commented on a change in pull request #5655:
URL: https://github.com/apache/incubator-tvm/pull/5655#discussion_r438462908



##########
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'

Review comment:
       It is fine, we need to follow this function https://github.com/apache/incubator-tvm/blob/master/tutorials/frontend/from_mxnet.py#L60
 so the content is cached.




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