--- /dev/null
+# 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.
+"""
+Micro TVM with TFLite Models
+============================
+**Author**: `Tom Gall <https://github.com/tom-gall>`_
+
+This tutorial is an introduction to working with MicroTVM and a TFLite
+model 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 the 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
+
+# %%
+# Recreating your own Pre-Trained TFLite model
+# --------------------------------------------
+#
+# The tutorial downloads 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
+#
+# .. note::
+#
+# If you accidentally download the example pretrained model from:
+# wget https://storage.googleapis.com/download.tensorflow.org/models/tflite/micro/hello_world_2020_04_13.zip
+# this will fail due to an unimplemented opcode (114)
+
+import os
+import numpy as np
+import tvm
+import tvm.micro as micro
+from tvm.contrib.download import download_testdata
+from tvm.contrib import graph_runtime, util
+from tvm import relay
+
+# %%
+# Load and prepare the Pre-Trained Model
+# --------------------------------------
+#
+# 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'
+model_path = download_testdata(model_url, model_file, module='data')
+
+tflite_model_buf = open(model_path, "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))
+
+######################################################################
+# 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})
+
+# %%
+# Running on device
+# ----------------------------------------------
+#
+# 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)
+
+######################################################################
+# 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
+# file.
+#
+# .. code-block:: python
+#
+# with tvm.transform.PassContext(opt_level=3, config={'tir.disable_vectorize': True},disabled_pass=['FuseOps']):
+# graph, c_mod, params = relay.build(mod, target=TARGET, params=params)
+
+######################################################################
+# With the c_mod that is the handle to our C source code, 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 perform inference
+#
+# .. code-block:: python
+#
+# mod.set_input(**params)
+
+######################################################################
+# The model consumes a single float32 value and returns a predicted
+# sine value.
+# To pass the input value we construct a tvm.nd.array object
+# with a single contrived number as input. For this model values of
+# 0 to 2Pi are acceptable.
+#
+# .. code-block:: python
+#
+# mod.set_input(input_tensor, tvm.nd.array(np.array([0.5], dtype="float32")))
+
+######################################################################
+# Run the model on device
+#
+# .. code-block:: python
+#
+# mod.run()
+
+######################################################################
+# Get output from the run and print
+#
+# .. code-block:: python
+#
+# tvm_output = mod.get_output(0).asnumpy()
+# print("result is: "+str(tvm_output))
projects / platform / upstream / tvm.git / commitdiff