MLMONDAYS

Materials for the USGS Deep Learning for Image Classification and Segmentation CDI workshop, October 2020, called ML MONDAYS

One module per week. All modules include slides, live session (plus video recordings), code, data, blog posts, and jupyter notebooks.

Please go to the project website for more details

This project was conceived by Dan Buscombe (Marda Science, LLC and contractor to USGS PCMSC), Leslie Hsu (USGS CDI) and Jonathan Warrick (USGS PCMSC). The project is supported by USGS CDI and the USGS CMHRP. Additional input over course content and testing has been provided by Phil Wernette (USGS PCMSC), and Rich Signell (USGS Woods Hole). Thanks to Sam Congdon (USGS CHS) and Rich Signell (USGS Woods Hole) for Pangeo CHS environment and development.

All code has been written by Dan Buscombe, unless where specified in function docstrings.

USGS ML-Mondays participants

Please do nothing, other than ensure you have access to the CHS/AWS environment especially set up for the course. Everything will be introduced in class in stages.

Desktop conda environment workflow

Clone the repo:

git clone --depth 1 https://github.com/dbuscombe-usgs/MLMONDAYS.git

cd to the repo

cd MLMONDAYS

Create a conda environment

A. Conda housekeeping

conda clean --all conda update -n base -c defaults conda

B. Create new mlmondays conda environment

We'll create a new conda environment and install packages into it from conda-forge

conda env create -f mlmondays.yml

C. Always a good idea to clean up after yourself

conda clean --all

Test your environment

A. Test 1: does your Tensorflow installation see your GPU?

python -c "import tensorflow as tf; print('Num GPUs Available: ', len(tf.config.experimental.list_physical_devices('GPU')))"

You should see a bunch of output from tensorflow, and then this line at the bottom

Num GPUs Available: 1

B. Test 2: is your jupyter kernel set up correctly?

jupyter kernelspec list

should show your python3 kernel inside your anaconda3/envs/mlmondays directory, for example

Available kernels:
  python3    /home/marda/anaconda3/envs/mlmondays/share/jupyter/kernels/python3

C. Change directory to the lesson you wish to work on, e.g. 1_ImageRecog

cd 1_ImageRecog

D. Launch using:

jupyter notebook

which should open your browser and show your directory structure within the jupyter environment

To shut down, use Ctrl+C

Windows desktop installation workflow

Follow this sequence of commands in this order:

conda create --name mlmondays python=3.7
conda activate mlmondays
pip install numpy
pip install seaborn   # --> installs Pillow and matplotlib
pip install jupyter
pip install sklearn
pip install matplotlib
conda install -c conda-forge tensorflow-gpu   # --> conda-forge only has TF v.2.1 (needs upgrade with pip in next line)
pip install tensorflow --upgrade
pip install tensorflow --upgrade   # --> yes, this needs to be run twice (likely)
pip install tensorflow-gpu --upgrade

Google Colab workflow

In each section, there are notebooks you can run on Google Colab instead of your own machine. They are identified by colab in the name.

Colab provide free GPU access to run these computational notebooks. If you save the notebooks to your own Google Drive, you can launch them from there.

Useful Links

Reference

Github repository (where all the code and website lives)
Documentation
API (list of all code functions, their inputs and outputs and what they do)
Summary of models
Blog

Notebooks

Data

Slides (these will be presented in the live class)

tamucc_imrecog_part1a.py
- load the subset 2-class (developed/undeveloped) train and validation datasets
- augment the data
- make a small custom categorical model
- train the model with the augmented data
- examine the history curves
- evaluate the model and plot a confusion matrix
tamucc_imrecog_part1b.py
- load the subset 2-class (developed/undeveloped) train and validation datasets
- augment the data
- make a small custom categorical model and compute class weights
- train the model using the class weights with the augmented data
- examine the history curves
- evaluate the model and plot a confusion matrix
tamucc_imrecog_part1c.py
- load the full 2-class (developed/undeveloped) train and validation datasets
- make a large custom categorical model
- train the model
- examine the history curves
- evaluate the model and plot a confusion matrix
tamucc_imrecog_part2a.py
- load the subset 3-class (developed/marsh/other) train and validation datasets
- augment the data
- make a large custom categorical model
- train the model with the augmented data
- examine the history curves
- evaluate the model and plot a confusion matrix
tamucc_imrecog_part2b.py
- load the subset 3-class (developed/marsh/other) train and validation datasets
- augment the data
- make a model based on a mobilenet feature extractor with imagenet weights
- train the model using the class weights with the augmented data
- examine the history curves
- evaluate the model and plot a confusion matrix
tamucc_imrecog_part2c.py
- load the subset 3-class (developed/marsh/other) train and validation datasets
- augment the data
- make a model based on a mobilenet feature extractor with imagenet weights
- load the previous weights, change the learning rate, and freeze the lower layers
- fine-tune the model with the augmented data
- examine the history curves
- evaluate the model and plot a confusion matrix
tamucc_imrecog_part3a.py
- load the subset 4-class train and validation datasets
- augment the data
- make a model based on a mobilenet feature extractor with imagenet weights
- train the model with the augmented data
- examine the history curves
- evaluate the model and plot a confusion matrix
tamucc_imrecog_part3b.py
- load the subset 4-class train and validation datasets
- augment the data
- make a model based on a mobilenet feature extractor with imagenet weights
- train the model using the class weights with the augmented data
- examine the history curves
- evaluate the model and plot a confusion matrix
tamucc_imrecog_part3c.py
- load the full 4-class train and validation datasets
- make a model based on a mobilenet feature extractor with imagenet weights
- train the model
- examine the history curves
- evaluate the model and plot a confusion matrix
nwpu_imrecog_part1.py
- load the subset 11-class train and validation datasets
- augment the data
- make a model based on a mobilenet feature extractor with imagenet weights
- train the model with the augmented data
- examine the history curves
- evaluate the model and plot a confusion matrix

functions - this is where most of the code is!

imports.py: wrapper imports function, loads the following three sets of functions from:
model_funcs.py: contains functions required for model building, training, evaluation, loss functions, etc
tfrecords_funcs.py: contains functions required for reading and writing tfrecords, and creating datasets, including preprocessing imagery and labels, etc
plot_funcs.py: contains functions required for data inputs and model outputs, etc

dataset-specific imports

tamucc_imports.py
nwpu_imports.py

file creation

nwpu_make_tfrecords.py: makes NPWU tfrecords from images with class labels in the file name
tamucc_make_tfrecords_sample_4class.py: makes TAMUCC tfrecords from images with class labels in the file name, and reclassify to 4 classes
tamucc_make_tfrecords_sample_3class.py: makes TAMUCC tfrecords from images with class labels in the file name, and reclassify to 3 classes
tamucc_make_tfrecords_sample_2class.py: makes TAMUCC tfrecords from images with class labels in the file name, and reclassify to 2 classes
tamucc_make_tfrecords.py: makes TAMUCC tfrecords from images with class labels in the file name

Week 2: Object recognition

Get the data and model weights by running the download script.

cd 2_ObjRecog
python download_data.py

notebook lessons (these are the 'live' components of ML-Mondays)

notebooks/MLMondays_week2_live.ipynb

There is also colab versions of the notebook that you can save to your own google drive, then launch in google colab

model training and evaluation scripts

coco_objrecog_part1.py:
- view a few examples from the secoora validation dataset
- make a model, load coco weights and use the model on coco imagery
- view some examples
- fine-tune the model on a coco data subset
- view some examples of fine-tuned model predictions on secoora imagery
secoora_objrecog_part1.py
- view a few examples from the secoora training dataset
- make a model, load coco weights and use the model on secoora imagery
- view some examples
- fine-tune the model on a secoora data subset
- view some examples of fine-tuned model predictions on secoora imagery
secoora_objrecog_part2.py
- view a few examples from the secoora sample jpeg dataset
- make a model, and train it from scratch on secoora imagery
- view some examples of model predictions on sample secoora imagery
- compare the number of people in each frame with model estimates of the same quantity

functions - this is where most of the code is!

imports.py: wrapper imports function, loads the following three sets of functions from:
model_funcs.py: contains functions required for model building, training, evaluation, loss functions, etc
tfrecords_funcs.py: contains functions required for reading and writing tfrecords, and creating datasets, including preprocessing imagery and labels, etc
plot_funcs.py: contains functions required for data inputs and model outputs, etc
data_funcs.py: contains functions required for data transformations and other utilities, etc

dataset-specific imports

coco_imports.py: imports things like batch size and other fixtures of the model-dataset combo

file creation

secoora_make_tfrecords.py: this function creates tfrecords from a csv file containing rows of filename, xmin, ymin, xmax, ymax, and class , and a folder of images

Week 3: Image segmentation

Get the data by running the download script. Only download the data you wish to use

cd 3_ImSeg
python download_data.py

notebook lessons (these are the 'live' components of ML-Mondays)

`notebooks/``
`notebooks/``

There are also colab versions of both notebooks that you can save to your own google drive, then launch in google colab

model training and evaluation scripts

obx_imseg_part1.py
- view a few examples from the OBX validation dataset
- make a model for a binary classification (deep water / no deep water), compile is using Dice loss
- train it using learning rate, checkpoint, and stopping callbacks
- view some examples of model segmentations on validation imagery
obx_imseg_part2a.py
- view a few examples from the OBX validation dataset
- make a model for a multiclass classification (deep water, shallow water, broken water, dry), compile is using categorical cross-entropy loss
- train it using learning rate, checkpoint, and stopping callbacks
- view some examples of model segmentations on validation imagery
obx_imseg_part2b.py
- view a few examples from the OBX validation dataset
- make a model for a multiclass classification (deep water, shallow water, broken water, dry), compile is using categorical hinge loss
- train it using learning rate, checkpoint, and stopping callbacks
- view some examples of model segmentations on validation imagery
- make an ensemble model that uses both trained models (the one here and the one previously trained in obx_imseg_part2a.py) to make predictions, then a CRF model to rewfine those predictions. The best prediction is assumed to be the one with the smaller Kullback-Liebler divergence score
oyster_imseg_part1.py
- view a few examples from the Oysternet validation dataset
- make a model for a binary classification (reef / no-reef), compile is using binary cross-entropy loss
- train it using learning rate, checkpoint, and stopping callbacks
- view some examples of model segmentations on validation imagery
oyster_imseg_part2.py
- view a few examples from the Oysternet validation dataset
- make a model for a binary classification, compile is using Dice loss
- train it using learning rate, checkpoint, and stopping callbacks
- view some examples of model segmentations on validation imagery

functions - this is where most of the code is!

imports.py: wrapper imports function, loads the following three sets of functions from:
model_funcs.py: contains functions required for model building, training, evaluation, loss functions, etc
tfrecords_funcs.py: contains functions required for reading and writing tfrecords, and creating datasets, including preprocessing imagery and labels, etc
plot_funcs.py: contains functions required for data inputs and model outputs, etc

dataset-specific imports

oyster_imports.py

file creation

oysternet_make_tfrecords.py
obx_make_tfrecords.py

Week 4: Self-supervised Image recognition

Get the data by running the download script. Only download the data you wish to use

cd 4_ImageRecog
python download_data.py

notebook lessons (these are the 'live' components of ML-Mondays)

notebooks/MLMondays_week1_live.ipynb: TAMUCC 4-class model building and evaluation

There are also colab versions of both notebooks that you can save to your own google drive, then launch in google colab

model training and evaluation scripts

tamucc_imrecog_part1a.py
- load the subset 2-class TAMUCC train and validation datasets and visualize some examples
- make an embedding model and train it on the training subset
- train a k-nearest neighbours model on the embeddings
- use the trained model to estimate the embeddings for the validation subset
- use the kNN model on each test sample to predict the class based on the similarity of the sample embedding with the k nearest sample embeddings
tamucc_imrecog_part1b.py
- the same as the above, except using thr weighted_binary_crossentropy function instead of sparse categorical cross-entropy
tamucc_imrecog_part2.py
- load the subset 3-class TAMUCC train and validation datasets and visualize some examples
- make an embedding model and train it on the training subset
- train a k-nearest neighbours model on the embeddings
- use the trained model to estimate the embeddings for the validation subset
- use the kNN model on each test sample to predict the class based on the similarity of the sample embedding with the k nearest sample embeddings
tamucc_imrecog_part3.py
- load the subset 12-class TAMUCC train and validation datasets and visualize some examples
- make an embedding model and train it on the training subset
- train a k-nearest neighbours model on the embeddings
- use the trained model to estimate the embeddings for the validation subset
- use the kNN model on each test sample to predict the class based on the similarity of the sample embedding with the k nearest sample embeddings
tamucc_imrecog_part4.py
- same as the above but for the full 12-class dataset, not the subset
nwpu_ssimrecog_part1.py
- load the subset 11-class NWPU train and validation datasets and visualize some examples
- make an embedding model and train it on the training subset
- train a k-nearest neighbours model on the embeddings
- use the trained model to estimate the embeddings for the validation subset
- use the kNN model on each test sample to predict the class based on the similarity of the sample embedding with the k nearest sample embeddings

functions - this is where most of the code is!

imports.py: wrapper imports function, loads the following three sets of functions from:
model_funcs.py: contains functions required for model building, training, evaluation, loss functions, etc
tfrecords_funcs.py: contains functions required for reading and writing tfrecords, and creating datasets, including preprocessing imagery and labels, etc
plot_funcs.py: contains functions required for data inputs and model outputs, etc

dataset-specific imports

tamucc_imports.py
nwpu_imports.py

file creation

tamucc_make_tfrecords_sample_12class.py

General workflow using your own data

Part 1: Supervised Image Recognition

Create a TFREcord dataset from your data, organised as follows:

copy training images into a folder called train
copy validation images into a folder called validation
ensure the class name is written to each file name. Ideally this is a prefix such that it is trivial to extract the class name from the file name
modify one of the provided workflows (such as tamucc_make_tfrecords.py) for your dataset, to create your train and validation tfrecord shards

Set up your model

Decide on whether you want to train a small custom model from scratch, a large model from scratch, or a large model trained using weights transfered from another task
If a small custom model, use make_cat_model with shallow=True for a relatively small model, and shallow=False for a relatively large model
If a large model with transfer learning, decide on which one to utilize (transfer_learning_mobilenet_model, transfer_learning_xception_model, or transfer_learning_model_vgg)
If you wish to train a large model from scratch, decide on which one to utilize (mobilenet_model, or xception_model)

Set up a data pipeline

Modify and follow the provided examples to create a get_training_dataset() and get_validation_dataset(). This will likely require you copy and modify get_batched_dataset to your own needs, depending on the format of your labels in filenames, by writing your own read_tfrecord function for your dataset (depending on the model selected)

Set up a model training pipeline

.compile() your model with an appropriate loss function and metrics
define a LearningRateScheduler function to vary learning rates over training as a function of training epoch
define an EarlyStopping criteria and create a ModelCheckpoint to save trained model weights
if transfer learning using weights not from imagenet, load your initial weights from somewhere else

Train the model

Use history = model.fit() to create a record of the training history. Pass the training and validation datasets, and a list of callbacks containing your model checkpoint, learning rate scheduler, and early stopping monitor)

Evaluate your model

Plot and study the history time-series of losses and metrics. If unsatisfactory, begin the iterative process of model optimization
Use the loss, accuracy = model.evaluate(get_validation_dataset(), batch_size=BATCH_SIZE, steps=validation_steps) function using the validation dataset and specifying the number of validation steps
Make plots of model outputs, organized in such a way that you can at-a-glance see where the model is failing. Make use of make_sample_plot and p_confmat, as a starting point, to visualize sample imagery with their model predictions, and a confusion matrix of predicted/true class-correspondences

Part 2: Object Recognition

Create a TFREcord dataset from your data, organised as follows:

copy training images into a folder called train
copy validation images into a folder called validation
create a text, csv file that lists each of the objects in each image, with the following columns: filename, xmin, ymin, xmax (float y coord pixel), ymax (float y coord pixel), class (string)
modify the provided workflow (secoora_make_tfrecords.py) for your dataset, to create your train and validation tfrecord shards

  filename,	width,	height,	class,	xmin,	ymin,	xmax,	ymax
  staugustinecam.2019-04-18_1400.mp4_frames_25.jpg,	1280,	720,	person,	1088,	581,	1129,	631
  staugustinecam.2019-04-18_1400.mp4_frames_25.jpg,	1280,	720,	person,	1125,	524,	1183,	573
  staugustinecam.2019-04-04_0700.mp4_frames_51.jpg,	1280,	720,	person,	158,	198,	178,	244
  staugustinecam.2019-04-04_0700.mp4_frames_51.jpg,	1280,	720,	person,	131,	197,	162,	244
  staugustinecam.2019-04-04_0700.mp4_frames_51.jpg,	1280,	720,	person,	40,	504,	87,	581
  staugustinecam.2019-04-04_0700.mp4_frames_51.jpg,	1280,	720,	person,	0,	492,	15,	572
  staugustinecam.2019-01-01_1400.mp4_frames_44.jpg,	1280,	720,	person,	1086,	537,	1130,	615
  staugustinecam.2019-01-01_1400.mp4_frames_44.jpg,	1280,	720,	person,	1064,	581,	1134,	624
  staugustinecam.2019-01-01_1400.mp4_frames_44.jpg,	1280,	720,	person,	1136,	526,	1186,	570

Set up your model

Decide on whether you want to train a model from scratch, or trained using weights transfered from another task (such as coco 2017)

Set up a model training pipeline

.compile() your model with an appropriate loss function and metrics
define a LearningRateScheduler function to vary learning rates over training as a function of training epoch
define an EarlyStopping criteria and create a ModelCheckpoint to save trained model weights
if transfer learning using weights not from coco, load your initial weights from somewhere else

Train the model

Use history = model.fit() to create a record of the training history. Pass the training and validation datasets, and a list of callbacks containing your model checkpoint, learning rate scheduler, and early stopping monitor)

Evaluate your model

Plot and study the history time-series of losses and metrics. If unsatisfactory, begin the iterative process of model optimization
Use the loss, accuracy = model.evaluate(get_validation_dataset(), batch_size=BATCH_SIZE, steps=validation_steps) function using the validation dataset and specifying the number of validation steps
Make plots of model outputs, organized in such a way that you can at-a-glance see where the model is failing. Make use of visualize_detections, as a starting point, to visualize sample imagery with their model predictions

Part 3: Supervised Image Segmentation

Create a TFREcord dataset from your data, organised as follows:

Copy images into a folder called images
Copy label images into a folder called labels
Modify one of the provided workflows (such as obx_make_tfrecords.py) for your dataset, to create your train and validation tfrecord shards

Set up your model
Set up a model training pipeline

.compile() your model with an appropriate loss function and metrics
define a LearningRateScheduler function to vary learning rates over training as a function of training epoch
define an EarlyStopping criteria and create a ModelCheckpoint to save trained model weights

Train the model

Use history = model.fit() to create a record of the training history. Pass the training and validation datasets, and a list of callbacks containing your model checkpoint, learning rate scheduler, and early stopping monitor)

Evaluate your model

Plot and study the history time-series of losses and metrics. If unsatisfactory, begin the iterative process of model optimization
Use the loss, accuracy = model.evaluate(get_validation_dataset(), batch_size=BATCH_SIZE, steps=validation_steps) function using the validation dataset and specifying the number of validation steps
Make plots of model outputs, organized in such a way that you can at-a-glance see where the model is failing. Make use of make_sample_seg_plot, as a starting point, to visualize sample imagery with their model predictions

Part 4: Semi-supervised Image Recognition

Create a TFREcord dataset from your data, organised as follows:

Copy training images into a folder called train
Copy validation images into a folder called validation
Ensure the class name is written to each file name. Ideally this is a prefix such that it is trivial to extract the class name from the file name
Modify one of the provided workflows (such as tamucc_make_tfrecords.py) for your dataset, to create your train and validation tfrecord shards

Set up your model

Decide on whether you want to train a small or large embedding model (get_embedding_model or get_large_embedding_model)

Set up a data pipeline

Modify and follow the provided examples to create a get_training_dataset() and get_validation_dataset(). This will likely require you copy and modify get_batched_dataset to your own needs, depending on the format of your labels in filenames, by writing your own read_tfrecord function for your dataset (depending on the model selected)
Remember for this method you have to read all the data at once into memory, which isn't ideal. You may therefore need to modify get_data_stuff to be a more efficient way to do so for your data

Set up a model training pipeline

.compile() your model with an appropriate loss function and metrics
Define a LearningRateScheduler function to vary learning rates over training as a function of training epoch
Define an EarlyStopping criteria and create a ModelCheckpoint to save trained model weights

Train the autoencoder embedding model

Use history = model.fit() to create a record of the training history. Pass the training and validation datasets, and a list of callbacks containing your model checkpoint, learning rate scheduler, and early stopping monitor)

Train the k-nearest neighbour classifer

Decide or determine the optimal number of neighbours (k)
Use fit_knn_to_embeddings to make a model of your training embeddings

Evaluate your model

Plot and study the history time-series of losses and metrics. If unsatisfactory, begin the iterative process of model optimization
Use the loss, accuracy = model.evaluate(get_validation_dataset(), batch_size=BATCH_SIZE, steps=validation_steps) function using the validation dataset and specifying the number of validation steps
Make plots of model outputs, organized in such a way that you can at-a-glance see where the model is failing. Make use of make_sample_plot and p_confmat, as a starting point, to visualize sample imagery with their model predictions, and a confusion matrix of predicted/true class-correspondences
On the test set, play tf.nn.l2_normalize (i.e. don't use it on test and/or train embeddings and see if it improves results)

Contributing

Contributions are welcome, and they are greatly appreciated! Credit will always be given.

Report Bugs

Report bugs at https://github.com/dbuscombe-usgs/MLMONDAYS/issues.

Please include:

* Your operating system name and version.
* Any details about your local setup that might be helpful in troubleshooting.
* Detailed steps to reproduce the bug.

Fix Bugs

Look through the GitHub issues for bugs. Anything tagged with "bug" and "help wanted" is open to whoever wants to implement it.

Implement Features

Look through the GitHub issues for features. Anything tagged with "enhancement" and "help wanted" is open to whoever wants to implement it.

Write Documentation

We could always use more documentation, whether as part of the docs, in docstrings, or using this software in blog posts, articles, etc.

Get Started!

Ready to contribute? Here's how to set up for local development.

Fork the dash_doodler repo on GitHub.
Clone your fork locally:

$ git clone git@github.com:your_name_here/MLMONDAYS.git

Install your local copy into a virtualenv. Assuming you have virtualenvwrapper installed, this is how you set up your fork for local development:

$ cd MLMONDAYS/ $ conda env create --file mlmondays.yml $ conda activate dashdoodler

Create a branch for local development:

$ git checkout -b name-of-your-bugfix-or-feature

Now you can make your changes locally.

Commit your changes and push your branch to GitHub:

$ git add .

$ git commit -m "Your detailed description of your changes."

$ git push origin name-of-your-bugfix-or-feature

Submit a pull request through the GitHub website.

Name		Name	Last commit message	Last commit date
Latest commit History 74 Commits
.ipynb_checkpoints		.ipynb_checkpoints
1_ImageRecog		1_ImageRecog
2_ObjRecog		2_ObjRecog
3_ImageSeg		3_ImageSeg
4_UnsupImageRecog		4_UnsupImageRecog
docs		docs
website		website
.dockerignore		.dockerignore
.gitignore		.gitignore
Dockerfile		Dockerfile
INSTALL.md		INSTALL.md
README.md		README.md
docker-compose.yml		docker-compose.yml
mlmondays.yml		mlmondays.yml

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MLMONDAYS

USGS ML-Mondays participants

Desktop conda environment workflow

Windows desktop installation workflow

Google Colab workflow

Useful Links

Reference

Notebooks

Data

Slides (these will be presented in the live class)

Contents

Week 1: Image recognition

notebook lessons (these are the 'live' components of ML-Mondays)

data viz. scripts

model training and evaluation scripts

functions - this is where most of the code is!

dataset-specific imports

file creation

Week 2: Object recognition

notebook lessons (these are the 'live' components of ML-Mondays)

model training and evaluation scripts

functions - this is where most of the code is!

dataset-specific imports

file creation

Week 3: Image segmentation

notebook lessons (these are the 'live' components of ML-Mondays)

model training and evaluation scripts

functions - this is where most of the code is!

dataset-specific imports

file creation

Week 4: Self-supervised Image recognition

notebook lessons (these are the 'live' components of ML-Mondays)

model training and evaluation scripts

functions - this is where most of the code is!

dataset-specific imports

file creation

General workflow using your own data

Part 1: Supervised Image Recognition

Part 2: Object Recognition

Part 3: Supervised Image Segmentation

Part 4: Semi-supervised Image Recognition

Contributing

Report Bugs

Fix Bugs

Implement Features

Write Documentation

Get Started!

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