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Posted to commits@tvm.apache.org by GitBox <gi...@apache.org> on 2022/04/06 21:05:51 UTC
[GitHub] [tvm] guberti opened a new pull request, #10921: Model training tutorial for microTVM
guberti opened a new pull request, #10921:
URL: https://github.com/apache/tvm/pull/10921
This PR adds a new tutorial to the microTVM how_to gallery, showing how a MobileNet V1 model can be trained with transfer learning and modified to fit on embedded devices (in this case, the Arduino Nano 33 BLE).
This tutorial was [originally designed as a Google Colab notebook](https://colab.research.google.com/drive/1JZJFJVM56N1C8DfGyKx6lJ-I0-nLa-Ar), but I'm in the process of modifying it so it works with the existing TVM tutorials. I'm also working on adding a button to let users open this tutorial in Google Colab (see #10706) - this new tutorial will also serve as a testing bed for that feature.
For now, the best way to view the tutorial is by going to https://colab.research.google.com/drive/1JZJFJVM56N1C8DfGyKx6lJ-I0-nLa-Ar.
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[GitHub] [tvm] guberti commented on a diff in pull request #10921: [docs] microTVM model training tutorial with Colab support
Posted by GitBox <gi...@apache.org>.
guberti commented on code in PR #10921:
URL: https://github.com/apache/tvm/pull/10921#discussion_r865205437
##########
gallery/how_to/work_with_microtvm/micro_train.py:
##########
@@ -0,0 +1,638 @@
+# 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.
+"""
+.. _microtvm-train-arduino:
+
+Training Vision Models for microTVM on Arduino
+==============================================
+**Author**: `Gavin Uberti <https://github.com/guberti>`_
+
+This tutorial shows how MobileNetV1 models can be trained
+to fit on embedded devices, and how those models can be
+deployed to Arduino using TVM.
+"""
+
+######################################################################
+# .. note::
+#
+# This tutorial is best viewed as a Jupyter Notebook. You can download and run it locally
+# using the link at the bottom of this page, or open it online for free using Google Colab.
+# Click the icon below to open in Google Colab.
+#
+# .. image:: https://raw.githubusercontent.com/guberti/web-data/micro-train-tutorial-data/images/utilities/colab_button.png
+# :align: center
+# :target: https://colab.research.google.com/github/guberti/tvm-site/blob/asf-site/docs/_downloads/a7c7ea4b5017ae70db1f51dd8e6dcd82/micro_train.ipynb
+# :width: 600px
+#
+# Motivation
+# ----------
+# When building IOT devices, we often want them to **see and understand** the world around them.
+# This can take many forms, but often times a device will want to know if a certain **kind of
+# object** is in its field of vision.
+#
+# For example, a security camera might look for **people**, so it can decide whether to save a video
+# to memory. A traffic light might look for **cars**, so it can judge which lights should change
+# first. Or a forest camera might look for a **kind of animal**, so they can estimate how large
+# the animal population is.
+#
+# To make these devices affordable, we would like them to need only a low-cost processor like the
+# `nRF52840 <https://www.nordicsemi.com/Products/nRF52840>`_ (costing five dollars each on Mouser) or the `RP2040 <https://www.raspberrypi.com/products/rp2040/>`_ (just $1.45 each!).
+#
+# These devices have very little memory (~250 KB RAM), meaning that no conventional edge AI
+# vision model (like MobileNet or EfficientNet) will be able to run. In this tutorial, we will
+# show how these models can be modified to work around this requirement. Then, we will use TVM
+# to compile and deploy it for an Arduino that uses one of these processors.
+#
+# Installing the Prerequisites
+# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+#
+# To run this tutorial, we will need Tensorflow and TFLite to train our model, pyserial and tlcpack
+# (a community build of TVM) to compile and test it, and imagemagick and curl to preprocess data.
+# We will also need to install the Arduino CLI and the mbed_nano package to test our model.
+#
+# .. code-block:: bash
+#
+# %%bash
+# pip install -q tensorflow tflite pyserial
+# pip install -q tlcpack-nightly -f https://tlcpack.ai/wheels
+# apt-get -qq install imagemagick curl
+#
+# # Install Arduino CLI and library for Nano 33 BLE
+# curl -fsSL https://raw.githubusercontent.com/arduino/arduino-cli/master/install.sh | sh
+# /content/bin/arduino-cli core update-index
+# /content/bin/arduino-cli core install arduino:mbed_nano
+#
+# Using the GPU
+# ^^^^^^^^^^^^^
+#
+# This tutorial demonstrates training a neural network, which is requires a lot of computing power
+# and will go much faster if you have a GPU. If you are viewing this tutorial on Google Colab, you
+# can enable a GPU by going to **Runtime->Change runtime type** and selecting "GPU" as our hardware
+# accelerator. If you are running locally, you can `follow Tensorflow's guide <https://www.tensorflow.org/guide/gpu>`_ instead.
+#
+# We can test our GPU installation with the following code:
+
+import tensorflow as tf
+
+if not tf.test.gpu_device_name():
+ print("No GPU was detected!")
+ print("Model training will take much longer (~30 minutes instead of ~5)")
+else:
+ print("GPU detected - you're good to go.")
+
+######################################################################
+# Choosing Our Work Dir
+# ^^^^^^^^^^^^^^^^^^^^^
+# We need to pick a directory where our image datasets, trained model, and eventual Arduino sketch
+# will all live. If running on Google Colab, we'll save everything in ``/root`` (aka ``~``) but you'll
+# probably want to store it elsewhere if running locally. Note that this variable only affects Python
+# scripts - you'll have to adjust the Bash commands too.
+
+import os
+
+FOLDER = "/root"
+# sphinx_gallery_start_ignore
+import tempfile
+
+FOLDER = tempfile.mkdtemp()
+# sphinx_gallery_end_ignore
+
+######################################################################
+# Downloading the Data
+# --------------------
+# Convolutional neural networks usually learn by looking at many images, along with labels telling
+# the network what those images are. To get these images, we'll need a publicly available dataset
+# with thousands of images of all sorts of objects and labels of what's in each image. We'll also
+# need a bunch of images that **aren't** of cars, as we're trying to distinguish these two classes.
+#
+# In this tutorial, we'll create a model to detect if an image contains a **car**, but you can use
+# whatever category you like! Just change the source URL below to one containing images of another
+# type of object.
+#
+# To get our car images, we'll be downloading the `Stanford Cars dataset <http://ai.stanford.edu/~jkrause/cars/car_dataset.html>`_,
+# which contains 16,185 full color images of cars. We'll also need images of random things that
+# aren't cars, so we'll use the `COCO 2017 <https://cocodataset.org/#home>`_ validation set (it's
+# smaller, and thus faster to download than the full training set. Training on the full data set
+# would yield better results). Note that there are some cars in the COCO 2017 data set, but it's
+# a small enough fraction not to matter - just keep in mind that this will drive down our percieved
+# accuracy slightly.
+#
+# We could use the Tensorflow dataloader utilities, but we'll instead do it manually to make sure
+# it's easy to change the datasets being used. We'll end up with the following file hierarchy:
+#
+# .. code-block::
+#
+# /root
+# ├── images
+# │ ├── object
+# │ │ ├── 000001.jpg
+# │ │ │ ...
+# │ │ └── 016185.jpg
+# │ ├── object.tgz
+# │ ├── random
+# │ │ ├── 000000000139.jpg
+# │ │ │ ...
+# │ │ └── 000000581781.jpg
+# │ └── random.zip
+#
+# We should also note that Stanford cars has 8k images, while the COCO 2017 validation set is 5k
+# images - it is not a 50/50 split! If we wanted to, we could weight these classes differently
+# during training to correct for this, but training will still work if we ignore it. It should
+# take about **2 minutes** to download the Stanford Cars, while COCO 2017 validation will take
+# **1 minute**.
+
+import os
+import shutil
+import urllib.request
+
+# Download datasets
+os.makedirs(f"{FOLDER}/images")
+urllib.request.urlretrieve(
+ "http://ai.stanford.edu/~jkrause/car196/cars_train.tgz", f"{FOLDER}/images/target.tgz"
+)
+urllib.request.urlretrieve(
+ "http://images.cocodataset.org/zips/val2017.zip", f"{FOLDER}/images/random.zip"
+)
+
+# Extract them and rename their folders
+shutil.unpack_archive(f"{FOLDER}/images/target.tgz", f"{FOLDER}/images")
+shutil.unpack_archive(f"{FOLDER}/images/random.zip", f"{FOLDER}/images")
+shutil.move(f"{FOLDER}/images/cars_train", f"{FOLDER}/images/target")
+shutil.move(f"{FOLDER}/images/val2017", f"{FOLDER}/images/random")
+
+######################################################################
+# Loading the Data
+# ----------------
+# Currently, our data is stored on-disk as JPG files of various sizes. To train with it, we'll have
+# to load the images into memory, resize them to be 64x64, and convert them to raw, uncompressed
+# data. Keras's ``image_dataset_from_directory`` will take care of most of this, though it loads
+# images such that each pixel value is a float from 0 to 255.
+#
+# We'll also need to load labels, though Keras will help with this. From our subdirectory structure,
+# it knows the images in ``/objects`` are one class, and those in ``/random`` another. Setting
+# ``label_mode='categorical'`` tells Keras to convert these into **categorical labels** - a 2x1 vector
+# that's either ``[1, 0]`` for an object of our target class, or ``[0, 1]`` vector for anything else.
+# We'll also set ``shuffle=True`` to randomize the order of our examples.
+#
+# We will also **batch** the data - grouping samples into clumps to make our training go faster.
+# Setting ``batch_size = 32`` is a decent number.
+#
+# Lastly, in machine learning we generally want our inputs to be small numbers. We'll thus use a
+# ``Rescaling`` layer to change our images such that each pixel is a float between ``0.0`` and ``1.0``,
+# instead of ``0`` to ``255``. We need to be careful not to rescale our categorical labels though, so
+# we'll use a ``lambda`` function.
+
+IMAGE_SIZE = (64, 64, 3)
+unscaled_dataset = tf.keras.utils.image_dataset_from_directory(
+ f"{FOLDER}/images",
+ batch_size=32,
+ shuffle=True,
+ label_mode="categorical",
+ image_size=IMAGE_SIZE[0:2],
+)
+rescale = tf.keras.layers.Rescaling(scale=1.0 / 255)
+full_dataset = unscaled_dataset.map(lambda im, lbl: (rescale(im), lbl))
+
+######################################################################
+# What's Inside Our Dataset?
+# ^^^^^^^^^^^^^^^^^^^^^^^^^^
+# Before giving this data set to our neural network, we ought to give it a quick visual inspection.
+# Does the data look properly transformed? Do the labels seem appropriate? And what's our ratio of
+# objects to other stuff? We can display some examples from our datasets using ``matplotlib``:
+
+import matplotlib.pyplot as plt
+
+num_target_class = len(os.listdir(f"{FOLDER}/images/target/"))
+num_random_class = len(os.listdir(f"{FOLDER}/images/random/"))
+print(f"{FOLDER}/images/target contains {num_target_class} images")
+print(f"{FOLDER}/images/random contains {num_random_class} images")
+
+# Show some samples and their labels
+SAMPLES_TO_SHOW = 10
+plt.figure(figsize=(20, 10))
+for i, (image, label) in enumerate(unscaled_dataset.unbatch()):
+ if i >= SAMPLES_TO_SHOW:
+ break
+ ax = plt.subplot(1, SAMPLES_TO_SHOW, i + 1)
+ plt.imshow(image.numpy().astype("uint8"))
+ plt.title(list(label.numpy()))
+ plt.axis("off")
+
+######################################################################
+# Validating our Accuracy
+# ^^^^^^^^^^^^^^^^^^^^^^^
+# While developing our model, we'll often want to check how accurate it is (e.g. to see if it
+# improves during training). How do we do this? We could just train it on *all* of the data, and
+# then ask it to classify that same data. However, our model could cheat by just memorizing all of
+# the samples, which would make it *appear* to have very high accuracy, but perform very badly in
+# reality. In practice, this "memorizing" is called **overfitting**.
+#
+# To prevent this, we will set aside some of the data (we'll use 20%) as a **validation set**. Our
+# model will never be trained on validation data - we'll only use it to check our model's accuracy.
+
+num_batches = len(full_dataset)
+train_dataset = full_dataset.take(int(num_batches * 0.8))
+validation_dataset = full_dataset.skip(len(train_dataset))
+
+######################################################################
+# Loading the Data
+# ----------------
+# In the past decade, `convolutional neural networks <https://en.wikipedia.org/wiki/Convolutional_neural_network>`_ have been widely
+# adopted for image classification tasks. State-of-the-art models like `EfficientNet V2 <https://arxiv.org/abs/2104.00298>`_ are able
+# to perform image classification better than even humans! Unfortunately, these models have tens of
+# millions of parameters, and thus won't fit on cheap security camera computers.
+#
+# Our applications generally don't need perfect accuracy - 90% is good enough. We can thus use the
+# older and smaller MobileNet V1 architecture. But this *still* won't be small enough - by default,
+# MobileNet V1 with 224x224 inputs and depth 1.0 takes ~50 MB to just **store**. To reduce the size
+# of the model, there are three knobs we can turn. First, we can reduce the size of the input images
+# from 224x224 to 96x96 or 64x64, and Keras makes it easy to do this. We can also reduce the **depth**
Review Comment:
Fixed! I also now refer to it correctly as "alpha".
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[GitHub] [tvm] guberti commented on a diff in pull request #10921: [docs] microTVM model training tutorial with Colab support
Posted by GitBox <gi...@apache.org>.
guberti commented on code in PR #10921:
URL: https://github.com/apache/tvm/pull/10921#discussion_r865211999
##########
tests/scripts/ci.py:
##########
@@ -267,7 +267,7 @@ def docs(
"tlcpack-sphinx-addon==0.2.1",
"synr==0.5.0",
"image==1.5.33",
- "sphinx-gallery==0.4.0",
+ "git+https://github.com/guberti/sphinx-gallery.git@ipynb-include-bash",
Review Comment:
No, this is based on the latest sphinx-gallery version. You're right that this could potentially cause problems, but I haven't ran into any. We should definitely verify nothing breaks, and if there is an issue we can discuss backporting.
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[GitHub] [tvm] guberti commented on a diff in pull request #10921: [docs] microTVM model training tutorial with Colab support
Posted by GitBox <gi...@apache.org>.
guberti commented on code in PR #10921:
URL: https://github.com/apache/tvm/pull/10921#discussion_r865195926
##########
gallery/how_to/work_with_microtvm/micro_train.py:
##########
@@ -0,0 +1,638 @@
+# 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.
+"""
+.. _microtvm-train-arduino:
+
+Training Vision Models for microTVM on Arduino
+==============================================
+**Author**: `Gavin Uberti <https://github.com/guberti>`_
+
+This tutorial shows how MobileNetV1 models can be trained
+to fit on embedded devices, and how those models can be
+deployed to Arduino using TVM.
+"""
+
+######################################################################
+# .. note::
+#
+# This tutorial is best viewed as a Jupyter Notebook. You can download and run it locally
+# using the link at the bottom of this page, or open it online for free using Google Colab.
+# Click the icon below to open in Google Colab.
+#
+# .. image:: https://raw.githubusercontent.com/guberti/web-data/micro-train-tutorial-data/images/utilities/colab_button.png
+# :align: center
+# :target: https://colab.research.google.com/github/guberti/tvm-site/blob/asf-site/docs/_downloads/a7c7ea4b5017ae70db1f51dd8e6dcd82/micro_train.ipynb
+# :width: 600px
Review Comment:
Yea, 600px is pretty big. Fixed.
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[GitHub] [tvm] guberti commented on pull request #10921: [docs] microTVM model training tutorial with Colab support
Posted by GitBox <gi...@apache.org>.
guberti commented on PR #10921:
URL: https://github.com/apache/tvm/pull/10921#issuecomment-1117785901
> thanks @guberti, few questions
Thanks for taking a look! Addressed your comments and merged `main` into this branch to fix the tests.
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[GitHub] [tvm] areusch commented on pull request #10921: [docs] Google Colab compatible microTVM model training tutorial
Posted by GitBox <gi...@apache.org>.
areusch commented on PR #10921:
URL: https://github.com/apache/tvm/pull/10921#issuecomment-1115225788
@guberti could you try pushing an empty commit? i think retriggering somehow didn't pick up the right Jenkinsfile changes.
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[GitHub] [tvm] guberti commented on a diff in pull request #10921: [docs] microTVM model training tutorial with Colab support
Posted by GitBox <gi...@apache.org>.
guberti commented on code in PR #10921:
URL: https://github.com/apache/tvm/pull/10921#discussion_r883956625
##########
apps/microtvm/pyproject.toml:
##########
@@ -129,7 +129,7 @@ importer-tflite = ["tflite", "tensorflow", "tensorflow-estimator"]
autodocsumm = "^0.1"
black = "^19.10b0"
sphinx = "^3.0"
-sphinx-gallery = "^0.8"
+sphinx-gallery = { git = "https://github.com/sphinx-gallery/sphinx-gallery.git", branch = "master" }
Review Comment:
Agreed, fixed.
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[GitHub] [tvm] guberti commented on a diff in pull request #10921: [docs] microTVM model training tutorial with Colab support
Posted by GitBox <gi...@apache.org>.
guberti commented on code in PR #10921:
URL: https://github.com/apache/tvm/pull/10921#discussion_r864420869
##########
gallery/how_to/work_with_microtvm/micro_train.py:
##########
@@ -0,0 +1,638 @@
+# 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.
+"""
+.. _microtvm-train-arduino:
+
+Training Vision Models for microTVM on Arduino
+==============================================
+**Author**: `Gavin Uberti <https://github.com/guberti>`_
+
+This tutorial shows how MobileNetV1 models can be trained
+to fit on embedded devices, and how those models can be
+deployed to Arduino using TVM.
+"""
+
+######################################################################
+# .. note::
+#
+# This tutorial is best viewed as a Jupyter Notebook. You can download and run it locally
+# using the link at the bottom of this page, or open it online for free using Google Colab.
+# Click the icon below to open in Google Colab.
+#
+# .. image:: https://raw.githubusercontent.com/guberti/web-data/micro-train-tutorial-data/images/utilities/colab_button.png
+# :align: center
+# :target: https://colab.research.google.com/github/guberti/tvm-site/blob/asf-site/docs/_downloads/a7c7ea4b5017ae70db1f51dd8e6dcd82/micro_train.ipynb
Review Comment:
Nope! It looks like a hash, but is not. It does not change.
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[GitHub] [tvm] areusch commented on a diff in pull request #10921: [docs] microTVM model training tutorial with Colab support
Posted by GitBox <gi...@apache.org>.
areusch commented on code in PR #10921:
URL: https://github.com/apache/tvm/pull/10921#discussion_r883730195
##########
apps/microtvm/pyproject.toml:
##########
@@ -129,7 +129,7 @@ importer-tflite = ["tflite", "tensorflow", "tensorflow-estimator"]
autodocsumm = "^0.1"
black = "^19.10b0"
sphinx = "^3.0"
-sphinx-gallery = "^0.8"
+sphinx-gallery = { git = "https://github.com/sphinx-gallery/sphinx-gallery.git", branch = "master" }
Review Comment:
Let's at least pin to a deterministic revision so we don't have to handle churn in Sphinx-gallery in our ci rebuild process
##########
tests/scripts/task_python_docs.sh:
##########
@@ -84,6 +84,7 @@ IGNORED_WARNINGS=(
'autotvm:Cannot find config for target=llvm -keys=cpu -link-params=0'
'autotvm:One or more operators have not been tuned. Please tune your model for better performance. Use DEBUG logging level to see more details.'
'autotvm:Cannot find config for target=cuda -keys=cuda,gpu'
+ 'absl:For model inputs containing unsupported operations'
Review Comment:
Can you link me to an example log line?
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[GitHub] [tvm] guberti commented on a diff in pull request #10921: [docs] microTVM model training tutorial with Colab support
Posted by GitBox <gi...@apache.org>.
guberti commented on code in PR #10921:
URL: https://github.com/apache/tvm/pull/10921#discussion_r883958414
##########
tests/scripts/task_python_docs.sh:
##########
@@ -84,6 +84,7 @@ IGNORED_WARNINGS=(
'autotvm:Cannot find config for target=llvm -keys=cpu -link-params=0'
'autotvm:One or more operators have not been tuned. Please tune your model for better performance. Use DEBUG logging level to see more details.'
'autotvm:Cannot find config for target=cuda -keys=cuda,gpu'
+ 'absl:For model inputs containing unsupported operations'
Review Comment:
The full error is:
```WARNING:absl:For model inputs containing unsupported operations which cannot be quantized, the `inference_input_type` attribute will default to the original type.```
This warning also occurs in the official TensorFlow tutorial - https://www.tensorflow.org/lite/performance/post_training_integer_quant.
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[GitHub] [tvm] guberti commented on a diff in pull request #10921: [docs] microTVM model training tutorial with Colab support
Posted by GitBox <gi...@apache.org>.
guberti commented on code in PR #10921:
URL: https://github.com/apache/tvm/pull/10921#discussion_r865206005
##########
gallery/how_to/work_with_microtvm/micro_train.py:
##########
@@ -0,0 +1,638 @@
+# 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.
+"""
+.. _microtvm-train-arduino:
+
+Training Vision Models for microTVM on Arduino
+==============================================
+**Author**: `Gavin Uberti <https://github.com/guberti>`_
+
+This tutorial shows how MobileNetV1 models can be trained
+to fit on embedded devices, and how those models can be
+deployed to Arduino using TVM.
+"""
+
+######################################################################
+# .. note::
+#
+# This tutorial is best viewed as a Jupyter Notebook. You can download and run it locally
+# using the link at the bottom of this page, or open it online for free using Google Colab.
+# Click the icon below to open in Google Colab.
+#
+# .. image:: https://raw.githubusercontent.com/guberti/web-data/micro-train-tutorial-data/images/utilities/colab_button.png
+# :align: center
+# :target: https://colab.research.google.com/github/guberti/tvm-site/blob/asf-site/docs/_downloads/a7c7ea4b5017ae70db1f51dd8e6dcd82/micro_train.ipynb
+# :width: 600px
+#
+# Motivation
+# ----------
+# When building IOT devices, we often want them to **see and understand** the world around them.
+# This can take many forms, but often times a device will want to know if a certain **kind of
+# object** is in its field of vision.
+#
+# For example, a security camera might look for **people**, so it can decide whether to save a video
+# to memory. A traffic light might look for **cars**, so it can judge which lights should change
+# first. Or a forest camera might look for a **kind of animal**, so they can estimate how large
+# the animal population is.
+#
+# To make these devices affordable, we would like them to need only a low-cost processor like the
+# `nRF52840 <https://www.nordicsemi.com/Products/nRF52840>`_ (costing five dollars each on Mouser) or the `RP2040 <https://www.raspberrypi.com/products/rp2040/>`_ (just $1.45 each!).
+#
+# These devices have very little memory (~250 KB RAM), meaning that no conventional edge AI
+# vision model (like MobileNet or EfficientNet) will be able to run. In this tutorial, we will
+# show how these models can be modified to work around this requirement. Then, we will use TVM
+# to compile and deploy it for an Arduino that uses one of these processors.
+#
+# Installing the Prerequisites
+# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+#
+# To run this tutorial, we will need Tensorflow and TFLite to train our model, pyserial and tlcpack
+# (a community build of TVM) to compile and test it, and imagemagick and curl to preprocess data.
+# We will also need to install the Arduino CLI and the mbed_nano package to test our model.
+#
+# .. code-block:: bash
+#
+# %%bash
+# pip install -q tensorflow tflite pyserial
+# pip install -q tlcpack-nightly -f https://tlcpack.ai/wheels
+# apt-get -qq install imagemagick curl
+#
+# # Install Arduino CLI and library for Nano 33 BLE
+# curl -fsSL https://raw.githubusercontent.com/arduino/arduino-cli/master/install.sh | sh
+# /content/bin/arduino-cli core update-index
+# /content/bin/arduino-cli core install arduino:mbed_nano
+#
+# Using the GPU
+# ^^^^^^^^^^^^^
+#
+# This tutorial demonstrates training a neural network, which is requires a lot of computing power
+# and will go much faster if you have a GPU. If you are viewing this tutorial on Google Colab, you
+# can enable a GPU by going to **Runtime->Change runtime type** and selecting "GPU" as our hardware
+# accelerator. If you are running locally, you can `follow Tensorflow's guide <https://www.tensorflow.org/guide/gpu>`_ instead.
+#
+# We can test our GPU installation with the following code:
+
+import tensorflow as tf
+
+if not tf.test.gpu_device_name():
+ print("No GPU was detected!")
+ print("Model training will take much longer (~30 minutes instead of ~5)")
+else:
+ print("GPU detected - you're good to go.")
+
+######################################################################
+# Choosing Our Work Dir
+# ^^^^^^^^^^^^^^^^^^^^^
+# We need to pick a directory where our image datasets, trained model, and eventual Arduino sketch
+# will all live. If running on Google Colab, we'll save everything in ``/root`` (aka ``~``) but you'll
+# probably want to store it elsewhere if running locally. Note that this variable only affects Python
+# scripts - you'll have to adjust the Bash commands too.
+
+import os
+
+FOLDER = "/root"
+# sphinx_gallery_start_ignore
+import tempfile
+
+FOLDER = tempfile.mkdtemp()
+# sphinx_gallery_end_ignore
+
+######################################################################
+# Downloading the Data
+# --------------------
+# Convolutional neural networks usually learn by looking at many images, along with labels telling
+# the network what those images are. To get these images, we'll need a publicly available dataset
+# with thousands of images of all sorts of objects and labels of what's in each image. We'll also
+# need a bunch of images that **aren't** of cars, as we're trying to distinguish these two classes.
+#
+# In this tutorial, we'll create a model to detect if an image contains a **car**, but you can use
+# whatever category you like! Just change the source URL below to one containing images of another
+# type of object.
+#
+# To get our car images, we'll be downloading the `Stanford Cars dataset <http://ai.stanford.edu/~jkrause/cars/car_dataset.html>`_,
+# which contains 16,185 full color images of cars. We'll also need images of random things that
+# aren't cars, so we'll use the `COCO 2017 <https://cocodataset.org/#home>`_ validation set (it's
+# smaller, and thus faster to download than the full training set. Training on the full data set
+# would yield better results). Note that there are some cars in the COCO 2017 data set, but it's
+# a small enough fraction not to matter - just keep in mind that this will drive down our percieved
+# accuracy slightly.
+#
+# We could use the Tensorflow dataloader utilities, but we'll instead do it manually to make sure
+# it's easy to change the datasets being used. We'll end up with the following file hierarchy:
+#
+# .. code-block::
+#
+# /root
+# ├── images
+# │ ├── object
+# │ │ ├── 000001.jpg
+# │ │ │ ...
+# │ │ └── 016185.jpg
+# │ ├── object.tgz
+# │ ├── random
+# │ │ ├── 000000000139.jpg
+# │ │ │ ...
+# │ │ └── 000000581781.jpg
+# │ └── random.zip
+#
+# We should also note that Stanford cars has 8k images, while the COCO 2017 validation set is 5k
+# images - it is not a 50/50 split! If we wanted to, we could weight these classes differently
+# during training to correct for this, but training will still work if we ignore it. It should
+# take about **2 minutes** to download the Stanford Cars, while COCO 2017 validation will take
+# **1 minute**.
+
+import os
+import shutil
+import urllib.request
+
+# Download datasets
+os.makedirs(f"{FOLDER}/images")
+urllib.request.urlretrieve(
+ "http://ai.stanford.edu/~jkrause/car196/cars_train.tgz", f"{FOLDER}/images/target.tgz"
+)
+urllib.request.urlretrieve(
+ "http://images.cocodataset.org/zips/val2017.zip", f"{FOLDER}/images/random.zip"
+)
+
+# Extract them and rename their folders
+shutil.unpack_archive(f"{FOLDER}/images/target.tgz", f"{FOLDER}/images")
+shutil.unpack_archive(f"{FOLDER}/images/random.zip", f"{FOLDER}/images")
+shutil.move(f"{FOLDER}/images/cars_train", f"{FOLDER}/images/target")
+shutil.move(f"{FOLDER}/images/val2017", f"{FOLDER}/images/random")
+
+######################################################################
+# Loading the Data
+# ----------------
+# Currently, our data is stored on-disk as JPG files of various sizes. To train with it, we'll have
+# to load the images into memory, resize them to be 64x64, and convert them to raw, uncompressed
+# data. Keras's ``image_dataset_from_directory`` will take care of most of this, though it loads
+# images such that each pixel value is a float from 0 to 255.
+#
+# We'll also need to load labels, though Keras will help with this. From our subdirectory structure,
+# it knows the images in ``/objects`` are one class, and those in ``/random`` another. Setting
+# ``label_mode='categorical'`` tells Keras to convert these into **categorical labels** - a 2x1 vector
+# that's either ``[1, 0]`` for an object of our target class, or ``[0, 1]`` vector for anything else.
+# We'll also set ``shuffle=True`` to randomize the order of our examples.
+#
+# We will also **batch** the data - grouping samples into clumps to make our training go faster.
+# Setting ``batch_size = 32`` is a decent number.
+#
+# Lastly, in machine learning we generally want our inputs to be small numbers. We'll thus use a
+# ``Rescaling`` layer to change our images such that each pixel is a float between ``0.0`` and ``1.0``,
+# instead of ``0`` to ``255``. We need to be careful not to rescale our categorical labels though, so
+# we'll use a ``lambda`` function.
+
+IMAGE_SIZE = (64, 64, 3)
+unscaled_dataset = tf.keras.utils.image_dataset_from_directory(
+ f"{FOLDER}/images",
+ batch_size=32,
+ shuffle=True,
+ label_mode="categorical",
+ image_size=IMAGE_SIZE[0:2],
+)
+rescale = tf.keras.layers.Rescaling(scale=1.0 / 255)
+full_dataset = unscaled_dataset.map(lambda im, lbl: (rescale(im), lbl))
+
+######################################################################
+# What's Inside Our Dataset?
+# ^^^^^^^^^^^^^^^^^^^^^^^^^^
+# Before giving this data set to our neural network, we ought to give it a quick visual inspection.
+# Does the data look properly transformed? Do the labels seem appropriate? And what's our ratio of
+# objects to other stuff? We can display some examples from our datasets using ``matplotlib``:
+
+import matplotlib.pyplot as plt
+
+num_target_class = len(os.listdir(f"{FOLDER}/images/target/"))
+num_random_class = len(os.listdir(f"{FOLDER}/images/random/"))
+print(f"{FOLDER}/images/target contains {num_target_class} images")
+print(f"{FOLDER}/images/random contains {num_random_class} images")
+
+# Show some samples and their labels
+SAMPLES_TO_SHOW = 10
+plt.figure(figsize=(20, 10))
+for i, (image, label) in enumerate(unscaled_dataset.unbatch()):
+ if i >= SAMPLES_TO_SHOW:
+ break
+ ax = plt.subplot(1, SAMPLES_TO_SHOW, i + 1)
+ plt.imshow(image.numpy().astype("uint8"))
+ plt.title(list(label.numpy()))
+ plt.axis("off")
+
+######################################################################
+# Validating our Accuracy
+# ^^^^^^^^^^^^^^^^^^^^^^^
+# While developing our model, we'll often want to check how accurate it is (e.g. to see if it
+# improves during training). How do we do this? We could just train it on *all* of the data, and
+# then ask it to classify that same data. However, our model could cheat by just memorizing all of
+# the samples, which would make it *appear* to have very high accuracy, but perform very badly in
+# reality. In practice, this "memorizing" is called **overfitting**.
+#
+# To prevent this, we will set aside some of the data (we'll use 20%) as a **validation set**. Our
+# model will never be trained on validation data - we'll only use it to check our model's accuracy.
+
+num_batches = len(full_dataset)
+train_dataset = full_dataset.take(int(num_batches * 0.8))
+validation_dataset = full_dataset.skip(len(train_dataset))
+
+######################################################################
+# Loading the Data
+# ----------------
+# In the past decade, `convolutional neural networks <https://en.wikipedia.org/wiki/Convolutional_neural_network>`_ have been widely
+# adopted for image classification tasks. State-of-the-art models like `EfficientNet V2 <https://arxiv.org/abs/2104.00298>`_ are able
+# to perform image classification better than even humans! Unfortunately, these models have tens of
+# millions of parameters, and thus won't fit on cheap security camera computers.
+#
+# Our applications generally don't need perfect accuracy - 90% is good enough. We can thus use the
+# older and smaller MobileNet V1 architecture. But this *still* won't be small enough - by default,
+# MobileNet V1 with 224x224 inputs and depth 1.0 takes ~50 MB to just **store**. To reduce the size
+# of the model, there are three knobs we can turn. First, we can reduce the size of the input images
+# from 224x224 to 96x96 or 64x64, and Keras makes it easy to do this. We can also reduce the **depth**
+# of the model, from 1.0 to 0.25. And if we were really strapped for space, we could reduce the
+# number of **channels** by making our model take grayscale images instead of RGB ones.
+#
+# In this tutorial, we will use an RGB 64x64 input image and 0.25 depth scale. This is not quite
+# ideal, but it allows the finished model to fit in 192 KB of RAM, while still letting us perform
+# transfer learning using the official Tensorflow source models (if we used depth scale <0.25 or
+# a grayscale input, we wouldn't be able to do this).
+#
+# What is Transfer Learning?
+# ^^^^^^^^^^^^^^^^^^^^^^^^^^
+# Deep learning has `dominated image classification <https://paperswithcode.com/sota/image-classification-on-imagenet>`_ for a long time,
+# but training neural networks takes a lot of time. When a neural network is trained "from scratch",
+# its parameters start out randomly initialized, forcing it to learn very slowly how to tell images
+# apart.
+#
+# With transfer learning, we instead start with a neural network that's **already** good at a
+# specific task. In this example, that task is classifying images from `the ImageNet database <https://www.image-net.org/>`_. This
+# means the network already has some object detection capabilities, and is likely closer to what you
+# want then a random model would be.
+#
+# This works especially well with image processing neural networks like MobileNet. In practice, it
+# turns out the convolutional layers of the model (i.e. the first 90% of the layers) are used for
+# identifying low-level features like lines and shapes - only the last few fully connected layers
+# are used to determine how those shapes make up the objects the network is trying to detect.
+#
+# We can take advantage of this by starting training with a MobileNet model that was trained on
+# ImageNet, and already knows how to identify those lines and shapes. We can then just remove the
+# last few layers from this pretrained model, and add our own final layers. We'll then train this
+# conglomerate model for a few epochs on our cars vs non-cars dataset, to fine tune the first layers
+# and train from scratch the last layers.
+#
+# Source MobileNets for transfer learning have been `pretrained by the Tensorflow folks <https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet_v1.md>`_, so we
+# can just download the one closest to what we want (the 128x128 input model with 0.25 depth scale).
+
+os.makedirs(f"{FOLDER}/models")
+WEIGHTS_PATH = f"{FOLDER}/models/mobilenet_2_5_128_tf.h5"
+urllib.request.urlretrieve(
+ "https://storage.googleapis.com/tensorflow/keras-applications/mobilenet/mobilenet_2_5_128_tf.h5",
+ WEIGHTS_PATH,
+)
+
+pretrained = tf.keras.applications.MobileNet(
+ input_shape=IMAGE_SIZE, weights=WEIGHTS_PATH, alpha=0.25
+)
+
+######################################################################
+# Modifying Our Network
+# ^^^^^^^^^^^^^^^^^^^^^
+# As mentioned above, our pretrained model is designed to classify the 1,000 ImageNet categories,
+# but we want to convert it to classify cars. Since only the bottom few layers are task-specific,
+# we'll **cut off the last five layers** of our original model. In their place we'll build our own
+# "tail" to the model by performing respape, dropout, flatten, and softmax operations.
+
+model = tf.keras.models.Sequential()
+
+model.add(tf.keras.layers.InputLayer(input_shape=IMAGE_SIZE))
+model.add(tf.keras.Model(inputs=pretrained.inputs, outputs=pretrained.layers[-5].output))
+
+model.add(tf.keras.layers.Reshape((-1,)))
+model.add(tf.keras.layers.Dropout(0.1))
+model.add(tf.keras.layers.Flatten())
+model.add(tf.keras.layers.Dense(2, activation="softmax"))
+
+######################################################################
+# Training Our Network
+# ^^^^^^^^^^^^^^^^^^^^
+# When training neural networks, we must set a parameter called the **learning rate** that controls
+# how fast our network learns. It must be set carefully - too slow, and our network will take
+# forever to train; too fast, and our network won't be able to learn some fine details. Generally
+# for Adam (the optimizer we're using), ``0.001`` is a pretty good learning rate (and is what's
+# recommended in the `original paper <https://arxiv.org/abs/1412.6980>`_). However, in this case
+# ``0.0005`` seems to work a little better.
+#
+# We'll also pass the validation set from earlier to ``model.fit``. This will evaluate how good our
+# model is each time we train it, and let us track how our model is improving. Once training is
+# finished, the model should have a validation accuracy around ``0.98`` (meaning it was right 98% of
+# the time on our validation set).
+
+model.compile(
+ optimizer=tf.keras.optimizers.Adam(learning_rate=0.0005),
+ loss="categorical_crossentropy",
+ metrics=["accuracy"],
+)
+model.fit(train_dataset, validation_data=validation_dataset, epochs=3, verbose=2)
+
+######################################################################
+# Quantization
+# ------------
+# We've done a decent job of reducing our model's size so far - changing the input dimension,
+# along with removing the bottom layers reduced the model to just 219k parameters. However, each of
+# these parameters is a ``float32`` that takes four bytes, so our model will take up almost one MB!
+#
+# Additionally, it might be the case that our hardware doesn't have built-in support for floating
+# point numbers. While most high-memory Arduinos (like the Nano 33 BLE) do have hardware support,
+# some others (like the Arduino Due) do not. On any boards *without* dedicated hardware support,
+# floating point multiplication will be extremely slow.
+#
+# To address both issues we will **quantize** the model - representing the weights as eight bit
+# integers. It's more complex than just rounding, though - to get the best performance, TensorFlow
+# tracks how each neuron in our model activates, so we can figure out how to best represent the
+# while being relatively truthful to the original model.
+#
+# We will help TensorFlow do this by creating a representative dataset - a subset of the original
+# that is used for tracking how those neurons activate. We'll then pass this into a ``TFLiteConverter``
+# (Keras itself does not have quantization support) with an ``Optimize`` flag to tell TFLite to perform
+# the conversion. By default, TFLite keeps the inputs and outputs of our model as floats, so we must
+# explicitly tell it to avoid this behavior.
+
+converter = tf.lite.TFLiteConverter.from_keras_model(model)
+
+
+def representative_dataset():
+ for image_batch, label_batch in full_dataset.take(10):
+ yield [image_batch]
+
+
+converter.optimizations = [tf.lite.Optimize.DEFAULT]
+converter.representative_dataset = representative_dataset
+converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8]
+converter.inference_input_type = tf.uint8
+converter.inference_output_type = tf.uint8
+
+quantized_model = converter.convert()
+
+######################################################################
+# Download the Model if Desired
+# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+# We've now got a finished model, that you can use locally or in other tutorials (try autotuning
+# this model or viewing it on `https://netron.app/ <https://netron.app/>`_). But before we do
+# those things, we'll have to write it to a file (``quantized.tflite``). If you're running this
+# tutorial on Google Colab, you'll have to uncomment the last two lines to download the file
+# after writing it.
+
+QUANTIZED_MODEL_PATH = f"{FOLDER}/models/quantized.tflite"
+with open(QUANTIZED_MODEL_PATH, "wb") as f:
+ f.write(quantized_model)
+# from google.colab import files
+# files.download(QUANTIZED_MODEL_PATH)
+
+######################################################################
+# Compiling With TVM For Arduino
+# ------------------------------
+# Tensorflow has a built-in framework for deploying to microcontrollers - `TFLite Micro <https://www.tensorflow.org/lite/microcontrollers>`_. However,
+# it's poorly supported by development boards, and does not support autotuning. We will use Apache
+# TVM instead.
+#
+# TVM can be used either with its command line interface (``tvmc``) or with its Python interface. The
+# Python interface is fully-featured and more stable, so we'll use it here.
+#
+# TVM is an optimizing compiler, and optimizations to our model are performed in stages via
+# **intermediate representations**. The first of these is `Relay <https://arxiv.org/abs/1810.00952>`_ a high-level intermediate
+# representation emphasizing portability. The conversion from ``.tflite`` to Relay is done without any
+# knowledge of our "end goal" - the fact we intend to run this model on an Arduino.
+#
+# Choosing an Arduino Board
+# ^^^^^^^^^^^^^^^^^^^^^^^^^
+# Next, we'll have to decide exactly which Arduino board to use. The Arduino sketch that we
+# ultimately generate should be compatible with any board, but knowing which board we are using in
+# advance allows TVM to adjust its compilation strategy to get better performance.
+#
+# There is one catch - we need enough **memory** (flash and RAM) to be able to run our model. We
+# won't ever be able to run a complex vision model like a MobileNet on an Arduino Uno - that board
+# only has 2 kB of RAM and 32 kB of flash! Our model has ~200,000 parameters, so there is just no
+# way it could fit.
+#
+# For this tutorial, we will use the Nano 33 BLE, which has 1 MB of flash memory and 256 KB of RAM.
+# However, any other Arduino with those specs or better should also work.
+#
+# Generating our project
+# ^^^^^^^^^^^^^^^^^^^^^^
+# Next, we'll compile the model to TVM's MLF (machine learning format) intermediate representation,
Review Comment:
Fixed
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[GitHub] [tvm] areusch merged pull request #10921: [docs] microTVM model training tutorial with Colab support
Posted by GitBox <gi...@apache.org>.
areusch merged PR #10921:
URL: https://github.com/apache/tvm/pull/10921
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[GitHub] [tvm] guberti commented on a diff in pull request #10921: [docs] microTVM model training tutorial with Colab support
Posted by GitBox <gi...@apache.org>.
guberti commented on code in PR #10921:
URL: https://github.com/apache/tvm/pull/10921#discussion_r883958414
##########
tests/scripts/task_python_docs.sh:
##########
@@ -84,6 +84,7 @@ IGNORED_WARNINGS=(
'autotvm:Cannot find config for target=llvm -keys=cpu -link-params=0'
'autotvm:One or more operators have not been tuned. Please tune your model for better performance. Use DEBUG logging level to see more details.'
'autotvm:Cannot find config for target=cuda -keys=cuda,gpu'
+ 'absl:For model inputs containing unsupported operations'
Review Comment:
The full error is:
```
WARNING:absl:For model inputs containing unsupported operations which cannot be quantized, the `inference_input_type` attribute will default to the original type.
```
This warning also occurs in the official TensorFlow tutorial - https://www.tensorflow.org/lite/performance/post_training_integer_quant.
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[GitHub] [tvm] guberti commented on pull request #10921: Model training tutorial for microTVM
Posted by GitBox <gi...@apache.org>.
guberti commented on PR #10921:
URL: https://github.com/apache/tvm/pull/10921#issuecomment-1099903109
# How does "Open in Colab" work?
Google Colab has a feature where using a URL, `.ipynb` files can be loaded from GitHub links. It just so happens that TVM's doc files are already held at https://github.com/apache/tvm-site/tree/asf-site, so we only need to point Google Colab to that. Since this tutorial is not yet merged, all the "Open in Colab" button is is a link to the following address:
https://colab.research.google.com/github/guberti/tvm-site/blob/asf-site/docs/_downloads/a7c7ea4b5017ae70db1f51dd8e6dcd82/micro_train.ipynb
# What had to be changed to get this working?
In theory, nothing about Sphinx Gallery would have to be changed to make this work. However, in practice the generation of `.ipynb` files is extremely buggy, so the output Python notebooks do not work anyway. I made two pull requests to add features to Sphinx Gallery to make this possible:
- https://github.com/sphinx-gallery/sphinx-gallery/pull/940
- https://github.com/sphinx-gallery/sphinx-gallery/pull/941
If these features are merged into `sphinx-gallery`, then we only need to update the version of `sphinx-gallery` to make opening tutorials in Colab possible.
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[GitHub] [tvm] driazati commented on pull request #10921: [WIP] Proof of concept for Google Colab compatible tutorials
Posted by GitBox <gi...@apache.org>.
driazati commented on PR #10921:
URL: https://github.com/apache/tvm/pull/10921#issuecomment-1100261970
This is fantastic! Colab will be great to have in the docs. If the sphinx-gallery PRs don’t get merged in a timely manner we can apply your patches and build it from source in the Docker images so we don’t need to wait
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[GitHub] [tvm] areusch commented on pull request #10921: [docs] Google Colab compatible microTVM model training tutorial
Posted by GitBox <gi...@apache.org>.
areusch commented on PR #10921:
URL: https://github.com/apache/tvm/pull/10921#issuecomment-1113775853
retriggered now that https://github.com/apache/tvm/pull/11164 has landed
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[GitHub] [tvm] areusch commented on a diff in pull request #10921: [docs] microTVM model training tutorial with Colab support
Posted by GitBox <gi...@apache.org>.
areusch commented on code in PR #10921:
URL: https://github.com/apache/tvm/pull/10921#discussion_r863213004
##########
gallery/how_to/work_with_microtvm/micro_train.py:
##########
@@ -0,0 +1,638 @@
+# 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.
+"""
+.. _microtvm-train-arduino:
+
+Training Vision Models for microTVM on Arduino
+==============================================
+**Author**: `Gavin Uberti <https://github.com/guberti>`_
+
+This tutorial shows how MobileNetV1 models can be trained
+to fit on embedded devices, and how those models can be
+deployed to Arduino using TVM.
+"""
+
+######################################################################
+# .. note::
+#
+# This tutorial is best viewed as a Jupyter Notebook. You can download and run it locally
+# using the link at the bottom of this page, or open it online for free using Google Colab.
+# Click the icon below to open in Google Colab.
+#
+# .. image:: https://raw.githubusercontent.com/guberti/web-data/micro-train-tutorial-data/images/utilities/colab_button.png
+# :align: center
+# :target: https://colab.research.google.com/github/guberti/tvm-site/blob/asf-site/docs/_downloads/a7c7ea4b5017ae70db1f51dd8e6dcd82/micro_train.ipynb
Review Comment:
does this URL need to get updated each time the tutorial is changed?
##########
gallery/how_to/work_with_microtvm/micro_train.py:
##########
@@ -0,0 +1,638 @@
+# 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.
+"""
+.. _microtvm-train-arduino:
+
+Training Vision Models for microTVM on Arduino
+==============================================
+**Author**: `Gavin Uberti <https://github.com/guberti>`_
+
+This tutorial shows how MobileNetV1 models can be trained
+to fit on embedded devices, and how those models can be
+deployed to Arduino using TVM.
+"""
+
+######################################################################
+# .. note::
+#
+# This tutorial is best viewed as a Jupyter Notebook. You can download and run it locally
+# using the link at the bottom of this page, or open it online for free using Google Colab.
+# Click the icon below to open in Google Colab.
+#
+# .. image:: https://raw.githubusercontent.com/guberti/web-data/micro-train-tutorial-data/images/utilities/colab_button.png
+# :align: center
+# :target: https://colab.research.google.com/github/guberti/tvm-site/blob/asf-site/docs/_downloads/a7c7ea4b5017ae70db1f51dd8e6dcd82/micro_train.ipynb
+# :width: 600px
+#
+# Motivation
+# ----------
+# When building IOT devices, we often want them to **see and understand** the world around them.
+# This can take many forms, but often times a device will want to know if a certain **kind of
+# object** is in its field of vision.
+#
+# For example, a security camera might look for **people**, so it can decide whether to save a video
+# to memory. A traffic light might look for **cars**, so it can judge which lights should change
+# first. Or a forest camera might look for a **kind of animal**, so they can estimate how large
+# the animal population is.
+#
+# To make these devices affordable, we would like them to need only a low-cost processor like the
+# `nRF52840 <https://www.nordicsemi.com/Products/nRF52840>`_ (costing five dollars each on Mouser) or the `RP2040 <https://www.raspberrypi.com/products/rp2040/>`_ (just $1.45 each!).
+#
+# These devices have very little memory (~250 KB RAM), meaning that no conventional edge AI
+# vision model (like MobileNet or EfficientNet) will be able to run. In this tutorial, we will
+# show how these models can be modified to work around this requirement. Then, we will use TVM
+# to compile and deploy it for an Arduino that uses one of these processors.
+#
+# Installing the Prerequisites
+# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+#
+# To run this tutorial, we will need Tensorflow and TFLite to train our model, pyserial and tlcpack
+# (a community build of TVM) to compile and test it, and imagemagick and curl to preprocess data.
+# We will also need to install the Arduino CLI and the mbed_nano package to test our model.
+#
+# .. code-block:: bash
+#
+# %%bash
+# pip install -q tensorflow tflite pyserial
+# pip install -q tlcpack-nightly -f https://tlcpack.ai/wheels
+# apt-get -qq install imagemagick curl
+#
+# # Install Arduino CLI and library for Nano 33 BLE
+# curl -fsSL https://raw.githubusercontent.com/arduino/arduino-cli/master/install.sh | sh
+# /content/bin/arduino-cli core update-index
+# /content/bin/arduino-cli core install arduino:mbed_nano
+#
+# Using the GPU
+# ^^^^^^^^^^^^^
+#
+# This tutorial demonstrates training a neural network, which is requires a lot of computing power
+# and will go much faster if you have a GPU. If you are viewing this tutorial on Google Colab, you
+# can enable a GPU by going to **Runtime->Change runtime type** and selecting "GPU" as our hardware
+# accelerator. If you are running locally, you can `follow Tensorflow's guide <https://www.tensorflow.org/guide/gpu>`_ instead.
+#
+# We can test our GPU installation with the following code:
+
+import tensorflow as tf
+
+if not tf.test.gpu_device_name():
+ print("No GPU was detected!")
+ print("Model training will take much longer (~30 minutes instead of ~5)")
+else:
+ print("GPU detected - you're good to go.")
+
+######################################################################
+# Choosing Our Work Dir
+# ^^^^^^^^^^^^^^^^^^^^^
+# We need to pick a directory where our image datasets, trained model, and eventual Arduino sketch
+# will all live. If running on Google Colab, we'll save everything in ``/root`` (aka ``~``) but you'll
+# probably want to store it elsewhere if running locally. Note that this variable only affects Python
+# scripts - you'll have to adjust the Bash commands too.
+
+import os
+
+FOLDER = "/root"
+# sphinx_gallery_start_ignore
+import tempfile
+
+FOLDER = tempfile.mkdtemp()
+# sphinx_gallery_end_ignore
+
+######################################################################
+# Downloading the Data
+# --------------------
+# Convolutional neural networks usually learn by looking at many images, along with labels telling
+# the network what those images are. To get these images, we'll need a publicly available dataset
+# with thousands of images of all sorts of objects and labels of what's in each image. We'll also
+# need a bunch of images that **aren't** of cars, as we're trying to distinguish these two classes.
+#
+# In this tutorial, we'll create a model to detect if an image contains a **car**, but you can use
+# whatever category you like! Just change the source URL below to one containing images of another
+# type of object.
+#
+# To get our car images, we'll be downloading the `Stanford Cars dataset <http://ai.stanford.edu/~jkrause/cars/car_dataset.html>`_,
+# which contains 16,185 full color images of cars. We'll also need images of random things that
+# aren't cars, so we'll use the `COCO 2017 <https://cocodataset.org/#home>`_ validation set (it's
+# smaller, and thus faster to download than the full training set. Training on the full data set
+# would yield better results). Note that there are some cars in the COCO 2017 data set, but it's
+# a small enough fraction not to matter - just keep in mind that this will drive down our percieved
+# accuracy slightly.
+#
+# We could use the Tensorflow dataloader utilities, but we'll instead do it manually to make sure
+# it's easy to change the datasets being used. We'll end up with the following file hierarchy:
+#
+# .. code-block::
+#
+# /root
+# ├── images
+# │ ├── object
+# │ │ ├── 000001.jpg
+# │ │ │ ...
+# │ │ └── 016185.jpg
+# │ ├── object.tgz
+# │ ├── random
+# │ │ ├── 000000000139.jpg
+# │ │ │ ...
+# │ │ └── 000000581781.jpg
+# │ └── random.zip
+#
+# We should also note that Stanford cars has 8k images, while the COCO 2017 validation set is 5k
+# images - it is not a 50/50 split! If we wanted to, we could weight these classes differently
+# during training to correct for this, but training will still work if we ignore it. It should
+# take about **2 minutes** to download the Stanford Cars, while COCO 2017 validation will take
+# **1 minute**.
+
+import os
+import shutil
+import urllib.request
+
+# Download datasets
+os.makedirs(f"{FOLDER}/images")
+urllib.request.urlretrieve(
+ "http://ai.stanford.edu/~jkrause/car196/cars_train.tgz", f"{FOLDER}/images/target.tgz"
+)
+urllib.request.urlretrieve(
+ "http://images.cocodataset.org/zips/val2017.zip", f"{FOLDER}/images/random.zip"
+)
+
+# Extract them and rename their folders
+shutil.unpack_archive(f"{FOLDER}/images/target.tgz", f"{FOLDER}/images")
+shutil.unpack_archive(f"{FOLDER}/images/random.zip", f"{FOLDER}/images")
+shutil.move(f"{FOLDER}/images/cars_train", f"{FOLDER}/images/target")
+shutil.move(f"{FOLDER}/images/val2017", f"{FOLDER}/images/random")
+
+######################################################################
+# Loading the Data
+# ----------------
+# Currently, our data is stored on-disk as JPG files of various sizes. To train with it, we'll have
+# to load the images into memory, resize them to be 64x64, and convert them to raw, uncompressed
+# data. Keras's ``image_dataset_from_directory`` will take care of most of this, though it loads
+# images such that each pixel value is a float from 0 to 255.
+#
+# We'll also need to load labels, though Keras will help with this. From our subdirectory structure,
+# it knows the images in ``/objects`` are one class, and those in ``/random`` another. Setting
+# ``label_mode='categorical'`` tells Keras to convert these into **categorical labels** - a 2x1 vector
+# that's either ``[1, 0]`` for an object of our target class, or ``[0, 1]`` vector for anything else.
+# We'll also set ``shuffle=True`` to randomize the order of our examples.
+#
+# We will also **batch** the data - grouping samples into clumps to make our training go faster.
+# Setting ``batch_size = 32`` is a decent number.
+#
+# Lastly, in machine learning we generally want our inputs to be small numbers. We'll thus use a
+# ``Rescaling`` layer to change our images such that each pixel is a float between ``0.0`` and ``1.0``,
+# instead of ``0`` to ``255``. We need to be careful not to rescale our categorical labels though, so
+# we'll use a ``lambda`` function.
+
+IMAGE_SIZE = (64, 64, 3)
+unscaled_dataset = tf.keras.utils.image_dataset_from_directory(
+ f"{FOLDER}/images",
+ batch_size=32,
+ shuffle=True,
+ label_mode="categorical",
+ image_size=IMAGE_SIZE[0:2],
+)
+rescale = tf.keras.layers.Rescaling(scale=1.0 / 255)
+full_dataset = unscaled_dataset.map(lambda im, lbl: (rescale(im), lbl))
+
+######################################################################
+# What's Inside Our Dataset?
+# ^^^^^^^^^^^^^^^^^^^^^^^^^^
+# Before giving this data set to our neural network, we ought to give it a quick visual inspection.
+# Does the data look properly transformed? Do the labels seem appropriate? And what's our ratio of
+# objects to other stuff? We can display some examples from our datasets using ``matplotlib``:
+
+import matplotlib.pyplot as plt
+
+num_target_class = len(os.listdir(f"{FOLDER}/images/target/"))
+num_random_class = len(os.listdir(f"{FOLDER}/images/random/"))
+print(f"{FOLDER}/images/target contains {num_target_class} images")
+print(f"{FOLDER}/images/random contains {num_random_class} images")
+
+# Show some samples and their labels
+SAMPLES_TO_SHOW = 10
+plt.figure(figsize=(20, 10))
+for i, (image, label) in enumerate(unscaled_dataset.unbatch()):
+ if i >= SAMPLES_TO_SHOW:
+ break
+ ax = plt.subplot(1, SAMPLES_TO_SHOW, i + 1)
+ plt.imshow(image.numpy().astype("uint8"))
+ plt.title(list(label.numpy()))
+ plt.axis("off")
+
+######################################################################
+# Validating our Accuracy
+# ^^^^^^^^^^^^^^^^^^^^^^^
+# While developing our model, we'll often want to check how accurate it is (e.g. to see if it
+# improves during training). How do we do this? We could just train it on *all* of the data, and
+# then ask it to classify that same data. However, our model could cheat by just memorizing all of
+# the samples, which would make it *appear* to have very high accuracy, but perform very badly in
+# reality. In practice, this "memorizing" is called **overfitting**.
+#
+# To prevent this, we will set aside some of the data (we'll use 20%) as a **validation set**. Our
+# model will never be trained on validation data - we'll only use it to check our model's accuracy.
+
+num_batches = len(full_dataset)
+train_dataset = full_dataset.take(int(num_batches * 0.8))
+validation_dataset = full_dataset.skip(len(train_dataset))
+
+######################################################################
+# Loading the Data
+# ----------------
+# In the past decade, `convolutional neural networks <https://en.wikipedia.org/wiki/Convolutional_neural_network>`_ have been widely
+# adopted for image classification tasks. State-of-the-art models like `EfficientNet V2 <https://arxiv.org/abs/2104.00298>`_ are able
+# to perform image classification better than even humans! Unfortunately, these models have tens of
+# millions of parameters, and thus won't fit on cheap security camera computers.
+#
+# Our applications generally don't need perfect accuracy - 90% is good enough. We can thus use the
+# older and smaller MobileNet V1 architecture. But this *still* won't be small enough - by default,
+# MobileNet V1 with 224x224 inputs and depth 1.0 takes ~50 MB to just **store**. To reduce the size
+# of the model, there are three knobs we can turn. First, we can reduce the size of the input images
+# from 224x224 to 96x96 or 64x64, and Keras makes it easy to do this. We can also reduce the **depth**
+# of the model, from 1.0 to 0.25. And if we were really strapped for space, we could reduce the
+# number of **channels** by making our model take grayscale images instead of RGB ones.
+#
+# In this tutorial, we will use an RGB 64x64 input image and 0.25 depth scale. This is not quite
+# ideal, but it allows the finished model to fit in 192 KB of RAM, while still letting us perform
+# transfer learning using the official Tensorflow source models (if we used depth scale <0.25 or
+# a grayscale input, we wouldn't be able to do this).
+#
+# What is Transfer Learning?
+# ^^^^^^^^^^^^^^^^^^^^^^^^^^
+# Deep learning has `dominated image classification <https://paperswithcode.com/sota/image-classification-on-imagenet>`_ for a long time,
+# but training neural networks takes a lot of time. When a neural network is trained "from scratch",
+# its parameters start out randomly initialized, forcing it to learn very slowly how to tell images
+# apart.
+#
+# With transfer learning, we instead start with a neural network that's **already** good at a
+# specific task. In this example, that task is classifying images from `the ImageNet database <https://www.image-net.org/>`_. This
+# means the network already has some object detection capabilities, and is likely closer to what you
+# want then a random model would be.
+#
+# This works especially well with image processing neural networks like MobileNet. In practice, it
+# turns out the convolutional layers of the model (i.e. the first 90% of the layers) are used for
+# identifying low-level features like lines and shapes - only the last few fully connected layers
+# are used to determine how those shapes make up the objects the network is trying to detect.
+#
+# We can take advantage of this by starting training with a MobileNet model that was trained on
+# ImageNet, and already knows how to identify those lines and shapes. We can then just remove the
+# last few layers from this pretrained model, and add our own final layers. We'll then train this
+# conglomerate model for a few epochs on our cars vs non-cars dataset, to fine tune the first layers
+# and train from scratch the last layers.
+#
+# Source MobileNets for transfer learning have been `pretrained by the Tensorflow folks <https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet_v1.md>`_, so we
+# can just download the one closest to what we want (the 128x128 input model with 0.25 depth scale).
+
+os.makedirs(f"{FOLDER}/models")
+WEIGHTS_PATH = f"{FOLDER}/models/mobilenet_2_5_128_tf.h5"
+urllib.request.urlretrieve(
+ "https://storage.googleapis.com/tensorflow/keras-applications/mobilenet/mobilenet_2_5_128_tf.h5",
+ WEIGHTS_PATH,
+)
+
+pretrained = tf.keras.applications.MobileNet(
+ input_shape=IMAGE_SIZE, weights=WEIGHTS_PATH, alpha=0.25
+)
+
+######################################################################
+# Modifying Our Network
+# ^^^^^^^^^^^^^^^^^^^^^
+# As mentioned above, our pretrained model is designed to classify the 1,000 ImageNet categories,
+# but we want to convert it to classify cars. Since only the bottom few layers are task-specific,
+# we'll **cut off the last five layers** of our original model. In their place we'll build our own
+# "tail" to the model by performing respape, dropout, flatten, and softmax operations.
+
+model = tf.keras.models.Sequential()
+
+model.add(tf.keras.layers.InputLayer(input_shape=IMAGE_SIZE))
+model.add(tf.keras.Model(inputs=pretrained.inputs, outputs=pretrained.layers[-5].output))
+
+model.add(tf.keras.layers.Reshape((-1,)))
+model.add(tf.keras.layers.Dropout(0.1))
+model.add(tf.keras.layers.Flatten())
+model.add(tf.keras.layers.Dense(2, activation="softmax"))
+
+######################################################################
+# Training Our Network
+# ^^^^^^^^^^^^^^^^^^^^
+# When training neural networks, we must set a parameter called the **learning rate** that controls
+# how fast our network learns. It must be set carefully - too slow, and our network will take
+# forever to train; too fast, and our network won't be able to learn some fine details. Generally
+# for Adam (the optimizer we're using), ``0.001`` is a pretty good learning rate (and is what's
+# recommended in the `original paper <https://arxiv.org/abs/1412.6980>`_). However, in this case
+# ``0.0005`` seems to work a little better.
+#
+# We'll also pass the validation set from earlier to ``model.fit``. This will evaluate how good our
+# model is each time we train it, and let us track how our model is improving. Once training is
+# finished, the model should have a validation accuracy around ``0.98`` (meaning it was right 98% of
+# the time on our validation set).
+
+model.compile(
+ optimizer=tf.keras.optimizers.Adam(learning_rate=0.0005),
+ loss="categorical_crossentropy",
+ metrics=["accuracy"],
+)
+model.fit(train_dataset, validation_data=validation_dataset, epochs=3, verbose=2)
+
+######################################################################
+# Quantization
+# ------------
+# We've done a decent job of reducing our model's size so far - changing the input dimension,
+# along with removing the bottom layers reduced the model to just 219k parameters. However, each of
+# these parameters is a ``float32`` that takes four bytes, so our model will take up almost one MB!
+#
+# Additionally, it might be the case that our hardware doesn't have built-in support for floating
+# point numbers. While most high-memory Arduinos (like the Nano 33 BLE) do have hardware support,
+# some others (like the Arduino Due) do not. On any boards *without* dedicated hardware support,
+# floating point multiplication will be extremely slow.
+#
+# To address both issues we will **quantize** the model - representing the weights as eight bit
+# integers. It's more complex than just rounding, though - to get the best performance, TensorFlow
+# tracks how each neuron in our model activates, so we can figure out how to best represent the
+# while being relatively truthful to the original model.
+#
+# We will help TensorFlow do this by creating a representative dataset - a subset of the original
+# that is used for tracking how those neurons activate. We'll then pass this into a ``TFLiteConverter``
+# (Keras itself does not have quantization support) with an ``Optimize`` flag to tell TFLite to perform
+# the conversion. By default, TFLite keeps the inputs and outputs of our model as floats, so we must
+# explicitly tell it to avoid this behavior.
+
+converter = tf.lite.TFLiteConverter.from_keras_model(model)
+
+
+def representative_dataset():
+ for image_batch, label_batch in full_dataset.take(10):
+ yield [image_batch]
+
+
+converter.optimizations = [tf.lite.Optimize.DEFAULT]
+converter.representative_dataset = representative_dataset
+converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8]
+converter.inference_input_type = tf.uint8
+converter.inference_output_type = tf.uint8
+
+quantized_model = converter.convert()
+
+######################################################################
+# Download the Model if Desired
+# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+# We've now got a finished model, that you can use locally or in other tutorials (try autotuning
+# this model or viewing it on `https://netron.app/ <https://netron.app/>`_). But before we do
+# those things, we'll have to write it to a file (``quantized.tflite``). If you're running this
+# tutorial on Google Colab, you'll have to uncomment the last two lines to download the file
+# after writing it.
+
+QUANTIZED_MODEL_PATH = f"{FOLDER}/models/quantized.tflite"
+with open(QUANTIZED_MODEL_PATH, "wb") as f:
+ f.write(quantized_model)
+# from google.colab import files
+# files.download(QUANTIZED_MODEL_PATH)
+
+######################################################################
+# Compiling With TVM For Arduino
+# ------------------------------
+# Tensorflow has a built-in framework for deploying to microcontrollers - `TFLite Micro <https://www.tensorflow.org/lite/microcontrollers>`_. However,
+# it's poorly supported by development boards, and does not support autotuning. We will use Apache
+# TVM instead.
+#
+# TVM can be used either with its command line interface (``tvmc``) or with its Python interface. The
+# Python interface is fully-featured and more stable, so we'll use it here.
+#
+# TVM is an optimizing compiler, and optimizations to our model are performed in stages via
+# **intermediate representations**. The first of these is `Relay <https://arxiv.org/abs/1810.00952>`_ a high-level intermediate
+# representation emphasizing portability. The conversion from ``.tflite`` to Relay is done without any
+# knowledge of our "end goal" - the fact we intend to run this model on an Arduino.
+#
+# Choosing an Arduino Board
+# ^^^^^^^^^^^^^^^^^^^^^^^^^
+# Next, we'll have to decide exactly which Arduino board to use. The Arduino sketch that we
+# ultimately generate should be compatible with any board, but knowing which board we are using in
+# advance allows TVM to adjust its compilation strategy to get better performance.
+#
+# There is one catch - we need enough **memory** (flash and RAM) to be able to run our model. We
+# won't ever be able to run a complex vision model like a MobileNet on an Arduino Uno - that board
+# only has 2 kB of RAM and 32 kB of flash! Our model has ~200,000 parameters, so there is just no
+# way it could fit.
+#
+# For this tutorial, we will use the Nano 33 BLE, which has 1 MB of flash memory and 256 KB of RAM.
+# However, any other Arduino with those specs or better should also work.
+#
+# Generating our project
+# ^^^^^^^^^^^^^^^^^^^^^^
+# Next, we'll compile the model to TVM's MLF (machine learning format) intermediate representation,
Review Comment:
Model Library Format
##########
tests/scripts/ci.py:
##########
@@ -267,7 +267,7 @@ def docs(
"tlcpack-sphinx-addon==0.2.1",
"synr==0.5.0",
"image==1.5.33",
- "sphinx-gallery==0.4.0",
+ "git+https://github.com/guberti/sphinx-gallery.git@ipynb-include-bash",
Review Comment:
should we update docker/install scripts too if we're going to go this route? also, is this based off 0.4.0? may need to backport or verify it won't break anything to update.
##########
gallery/how_to/work_with_microtvm/micro_train.py:
##########
@@ -0,0 +1,638 @@
+# 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.
+"""
+.. _microtvm-train-arduino:
+
+Training Vision Models for microTVM on Arduino
+==============================================
+**Author**: `Gavin Uberti <https://github.com/guberti>`_
+
+This tutorial shows how MobileNetV1 models can be trained
+to fit on embedded devices, and how those models can be
+deployed to Arduino using TVM.
+"""
+
+######################################################################
+# .. note::
+#
+# This tutorial is best viewed as a Jupyter Notebook. You can download and run it locally
+# using the link at the bottom of this page, or open it online for free using Google Colab.
+# Click the icon below to open in Google Colab.
+#
+# .. image:: https://raw.githubusercontent.com/guberti/web-data/micro-train-tutorial-data/images/utilities/colab_button.png
+# :align: center
+# :target: https://colab.research.google.com/github/guberti/tvm-site/blob/asf-site/docs/_downloads/a7c7ea4b5017ae70db1f51dd8e6dcd82/micro_train.ipynb
+# :width: 600px
Review Comment:
should we consider shrinking this button? i don't want it to be inconspicuous, but it's pretty big right now.
##########
gallery/how_to/work_with_microtvm/micro_train.py:
##########
@@ -0,0 +1,638 @@
+# 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.
+"""
+.. _microtvm-train-arduino:
+
+Training Vision Models for microTVM on Arduino
+==============================================
+**Author**: `Gavin Uberti <https://github.com/guberti>`_
+
+This tutorial shows how MobileNetV1 models can be trained
+to fit on embedded devices, and how those models can be
+deployed to Arduino using TVM.
+"""
+
+######################################################################
+# .. note::
+#
+# This tutorial is best viewed as a Jupyter Notebook. You can download and run it locally
+# using the link at the bottom of this page, or open it online for free using Google Colab.
+# Click the icon below to open in Google Colab.
+#
+# .. image:: https://raw.githubusercontent.com/guberti/web-data/micro-train-tutorial-data/images/utilities/colab_button.png
+# :align: center
+# :target: https://colab.research.google.com/github/guberti/tvm-site/blob/asf-site/docs/_downloads/a7c7ea4b5017ae70db1f51dd8e6dcd82/micro_train.ipynb
+# :width: 600px
+#
+# Motivation
+# ----------
+# When building IOT devices, we often want them to **see and understand** the world around them.
+# This can take many forms, but often times a device will want to know if a certain **kind of
+# object** is in its field of vision.
+#
+# For example, a security camera might look for **people**, so it can decide whether to save a video
+# to memory. A traffic light might look for **cars**, so it can judge which lights should change
+# first. Or a forest camera might look for a **kind of animal**, so they can estimate how large
+# the animal population is.
+#
+# To make these devices affordable, we would like them to need only a low-cost processor like the
+# `nRF52840 <https://www.nordicsemi.com/Products/nRF52840>`_ (costing five dollars each on Mouser) or the `RP2040 <https://www.raspberrypi.com/products/rp2040/>`_ (just $1.45 each!).
+#
+# These devices have very little memory (~250 KB RAM), meaning that no conventional edge AI
+# vision model (like MobileNet or EfficientNet) will be able to run. In this tutorial, we will
+# show how these models can be modified to work around this requirement. Then, we will use TVM
+# to compile and deploy it for an Arduino that uses one of these processors.
+#
+# Installing the Prerequisites
+# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+#
+# To run this tutorial, we will need Tensorflow and TFLite to train our model, pyserial and tlcpack
+# (a community build of TVM) to compile and test it, and imagemagick and curl to preprocess data.
+# We will also need to install the Arduino CLI and the mbed_nano package to test our model.
+#
+# .. code-block:: bash
+#
+# %%bash
+# pip install -q tensorflow tflite pyserial
+# pip install -q tlcpack-nightly -f https://tlcpack.ai/wheels
+# apt-get -qq install imagemagick curl
+#
+# # Install Arduino CLI and library for Nano 33 BLE
+# curl -fsSL https://raw.githubusercontent.com/arduino/arduino-cli/master/install.sh | sh
+# /content/bin/arduino-cli core update-index
+# /content/bin/arduino-cli core install arduino:mbed_nano
+#
+# Using the GPU
+# ^^^^^^^^^^^^^
+#
+# This tutorial demonstrates training a neural network, which is requires a lot of computing power
+# and will go much faster if you have a GPU. If you are viewing this tutorial on Google Colab, you
+# can enable a GPU by going to **Runtime->Change runtime type** and selecting "GPU" as our hardware
+# accelerator. If you are running locally, you can `follow Tensorflow's guide <https://www.tensorflow.org/guide/gpu>`_ instead.
+#
+# We can test our GPU installation with the following code:
+
+import tensorflow as tf
+
+if not tf.test.gpu_device_name():
+ print("No GPU was detected!")
+ print("Model training will take much longer (~30 minutes instead of ~5)")
+else:
+ print("GPU detected - you're good to go.")
+
+######################################################################
+# Choosing Our Work Dir
+# ^^^^^^^^^^^^^^^^^^^^^
+# We need to pick a directory where our image datasets, trained model, and eventual Arduino sketch
+# will all live. If running on Google Colab, we'll save everything in ``/root`` (aka ``~``) but you'll
+# probably want to store it elsewhere if running locally. Note that this variable only affects Python
+# scripts - you'll have to adjust the Bash commands too.
+
+import os
+
+FOLDER = "/root"
+# sphinx_gallery_start_ignore
+import tempfile
+
+FOLDER = tempfile.mkdtemp()
+# sphinx_gallery_end_ignore
+
+######################################################################
+# Downloading the Data
+# --------------------
+# Convolutional neural networks usually learn by looking at many images, along with labels telling
+# the network what those images are. To get these images, we'll need a publicly available dataset
+# with thousands of images of all sorts of objects and labels of what's in each image. We'll also
+# need a bunch of images that **aren't** of cars, as we're trying to distinguish these two classes.
+#
+# In this tutorial, we'll create a model to detect if an image contains a **car**, but you can use
+# whatever category you like! Just change the source URL below to one containing images of another
+# type of object.
+#
+# To get our car images, we'll be downloading the `Stanford Cars dataset <http://ai.stanford.edu/~jkrause/cars/car_dataset.html>`_,
+# which contains 16,185 full color images of cars. We'll also need images of random things that
+# aren't cars, so we'll use the `COCO 2017 <https://cocodataset.org/#home>`_ validation set (it's
+# smaller, and thus faster to download than the full training set. Training on the full data set
+# would yield better results). Note that there are some cars in the COCO 2017 data set, but it's
+# a small enough fraction not to matter - just keep in mind that this will drive down our percieved
+# accuracy slightly.
+#
+# We could use the Tensorflow dataloader utilities, but we'll instead do it manually to make sure
+# it's easy to change the datasets being used. We'll end up with the following file hierarchy:
+#
+# .. code-block::
+#
+# /root
+# ├── images
+# │ ├── object
+# │ │ ├── 000001.jpg
+# │ │ │ ...
+# │ │ └── 016185.jpg
+# │ ├── object.tgz
+# │ ├── random
+# │ │ ├── 000000000139.jpg
+# │ │ │ ...
+# │ │ └── 000000581781.jpg
+# │ └── random.zip
+#
+# We should also note that Stanford cars has 8k images, while the COCO 2017 validation set is 5k
+# images - it is not a 50/50 split! If we wanted to, we could weight these classes differently
+# during training to correct for this, but training will still work if we ignore it. It should
+# take about **2 minutes** to download the Stanford Cars, while COCO 2017 validation will take
+# **1 minute**.
+
+import os
+import shutil
+import urllib.request
+
+# Download datasets
+os.makedirs(f"{FOLDER}/images")
+urllib.request.urlretrieve(
+ "http://ai.stanford.edu/~jkrause/car196/cars_train.tgz", f"{FOLDER}/images/target.tgz"
+)
+urllib.request.urlretrieve(
+ "http://images.cocodataset.org/zips/val2017.zip", f"{FOLDER}/images/random.zip"
+)
+
+# Extract them and rename their folders
+shutil.unpack_archive(f"{FOLDER}/images/target.tgz", f"{FOLDER}/images")
+shutil.unpack_archive(f"{FOLDER}/images/random.zip", f"{FOLDER}/images")
+shutil.move(f"{FOLDER}/images/cars_train", f"{FOLDER}/images/target")
+shutil.move(f"{FOLDER}/images/val2017", f"{FOLDER}/images/random")
+
+######################################################################
+# Loading the Data
+# ----------------
+# Currently, our data is stored on-disk as JPG files of various sizes. To train with it, we'll have
+# to load the images into memory, resize them to be 64x64, and convert them to raw, uncompressed
+# data. Keras's ``image_dataset_from_directory`` will take care of most of this, though it loads
+# images such that each pixel value is a float from 0 to 255.
+#
+# We'll also need to load labels, though Keras will help with this. From our subdirectory structure,
+# it knows the images in ``/objects`` are one class, and those in ``/random`` another. Setting
+# ``label_mode='categorical'`` tells Keras to convert these into **categorical labels** - a 2x1 vector
+# that's either ``[1, 0]`` for an object of our target class, or ``[0, 1]`` vector for anything else.
+# We'll also set ``shuffle=True`` to randomize the order of our examples.
+#
+# We will also **batch** the data - grouping samples into clumps to make our training go faster.
+# Setting ``batch_size = 32`` is a decent number.
+#
+# Lastly, in machine learning we generally want our inputs to be small numbers. We'll thus use a
+# ``Rescaling`` layer to change our images such that each pixel is a float between ``0.0`` and ``1.0``,
+# instead of ``0`` to ``255``. We need to be careful not to rescale our categorical labels though, so
+# we'll use a ``lambda`` function.
+
+IMAGE_SIZE = (64, 64, 3)
+unscaled_dataset = tf.keras.utils.image_dataset_from_directory(
+ f"{FOLDER}/images",
+ batch_size=32,
+ shuffle=True,
+ label_mode="categorical",
+ image_size=IMAGE_SIZE[0:2],
+)
+rescale = tf.keras.layers.Rescaling(scale=1.0 / 255)
+full_dataset = unscaled_dataset.map(lambda im, lbl: (rescale(im), lbl))
+
+######################################################################
+# What's Inside Our Dataset?
+# ^^^^^^^^^^^^^^^^^^^^^^^^^^
+# Before giving this data set to our neural network, we ought to give it a quick visual inspection.
+# Does the data look properly transformed? Do the labels seem appropriate? And what's our ratio of
+# objects to other stuff? We can display some examples from our datasets using ``matplotlib``:
+
+import matplotlib.pyplot as plt
+
+num_target_class = len(os.listdir(f"{FOLDER}/images/target/"))
+num_random_class = len(os.listdir(f"{FOLDER}/images/random/"))
+print(f"{FOLDER}/images/target contains {num_target_class} images")
+print(f"{FOLDER}/images/random contains {num_random_class} images")
+
+# Show some samples and their labels
+SAMPLES_TO_SHOW = 10
+plt.figure(figsize=(20, 10))
+for i, (image, label) in enumerate(unscaled_dataset.unbatch()):
+ if i >= SAMPLES_TO_SHOW:
+ break
+ ax = plt.subplot(1, SAMPLES_TO_SHOW, i + 1)
+ plt.imshow(image.numpy().astype("uint8"))
+ plt.title(list(label.numpy()))
+ plt.axis("off")
+
+######################################################################
+# Validating our Accuracy
+# ^^^^^^^^^^^^^^^^^^^^^^^
+# While developing our model, we'll often want to check how accurate it is (e.g. to see if it
+# improves during training). How do we do this? We could just train it on *all* of the data, and
+# then ask it to classify that same data. However, our model could cheat by just memorizing all of
+# the samples, which would make it *appear* to have very high accuracy, but perform very badly in
+# reality. In practice, this "memorizing" is called **overfitting**.
+#
+# To prevent this, we will set aside some of the data (we'll use 20%) as a **validation set**. Our
+# model will never be trained on validation data - we'll only use it to check our model's accuracy.
+
+num_batches = len(full_dataset)
+train_dataset = full_dataset.take(int(num_batches * 0.8))
+validation_dataset = full_dataset.skip(len(train_dataset))
+
+######################################################################
+# Loading the Data
+# ----------------
+# In the past decade, `convolutional neural networks <https://en.wikipedia.org/wiki/Convolutional_neural_network>`_ have been widely
+# adopted for image classification tasks. State-of-the-art models like `EfficientNet V2 <https://arxiv.org/abs/2104.00298>`_ are able
+# to perform image classification better than even humans! Unfortunately, these models have tens of
+# millions of parameters, and thus won't fit on cheap security camera computers.
+#
+# Our applications generally don't need perfect accuracy - 90% is good enough. We can thus use the
+# older and smaller MobileNet V1 architecture. But this *still* won't be small enough - by default,
+# MobileNet V1 with 224x224 inputs and depth 1.0 takes ~50 MB to just **store**. To reduce the size
+# of the model, there are three knobs we can turn. First, we can reduce the size of the input images
+# from 224x224 to 96x96 or 64x64, and Keras makes it easy to do this. We can also reduce the **depth**
Review Comment:
perhaps elaborate on "depth" here (could just link somewhere)
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