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Kedro TF Image 🖼️

This package consists of Kedro pipelines for preprocessing images using TensorFlow. I use it for Dermatology workflows and multimodal machine learning. Use this template that imports this package for multimodal ML. This package includes Kedro datasets for loading weights (as in CheXnet weights to a DenseNet121) and downloading pre-trained models from TF hub.

Pipelines

  • The download pipeline downloads online images defined in a csv file for multilabel classification. The labels are added to the filename. The csv format is:
id, url, labels
1, https://somesite.com/someimage.jpg,dog|black|grey
  • The folder pipeline creates TensorFlow dataset from a folder of images with labels as subfolders.
  • The multilabel pipeline processes files downloaded by the 'download' pipeline and create a dataset with images and labels. The labels are extracted from the filename. Example: _dog_black.jpg
  • Add labels in parameters.yml
master_labels: ["cat", "dog", "white", "black", "tan"]
val_size: 0.2

How to install

How to use

from kedro_tf_image.pipelines import preprocess

download = preprocess.create_download_pipeline(
        input="csvdata", output="imageset") #input is csv
folder = preprocess.create_folder_pipeline(
        input="imagefolder", output="processeddataset")
multilabel = preprocess.create_multilabel_pipeline(input="imageset", output="processeddataset")

# check output keys in the catalog below

Catalog


imageset:
  type: PartitionedDataSet
  dataset: {
      "type": "kedro_tf_image.extras.datasets.tf_image_dataset.TfImageDataSet",
      "imagedim": 224,
      "preprocess_input": "tensorflow.keras.applications.resnet50.preprocess_input"
  }
  path: data/01_raw/imageset
  filename_suffix: ".jpg"

csvdata:
  type: pandas.CSVDataSet
  filepath: data/01_raw/csvfile.csv

imagefolder:
  type: kedro_tf_image.extras.datasets.tf_image_folder.TfImageFolder
  folderpath: "/path/to/images"
  imagedim: 224
  load_args:
    validation_split: 0.2
    seed: 123
    batch_size: 1


processeddataset:
  type: kedro_tf_image.extras.datasets.tf_image_processed.TfImageProcessed
  folderpath: data/02_intermediate/
  imagedim: 224

# This is required as copy_mode: assign is needed for TF datasets
datasetinmemory:
  type: MemoryDataSet
  copy_mode: assign

Datasets

  • kedro_tf_image.extras.datasets.tf_image_dataset.TfImageDataSet - Load single images
  • kedro_tf_image.extras.datasets.tf_image_folder.TfImageFolder - Load a folder of images
  • kedro_tf_image.extras.datasets.tf_model_weights.TfModelWeights - Read model from weights (Ex: CheXnet with dim 14) (Use create_classification_layer to add a Dense layer of NCLASSES dim)
  • kedro_tf_image.extras.datasets.tf_model_download.TfModelDownload - Load model from TF hub.

Author

Overview

This is your new Kedro project, which was generated using Kedro 0.17.3.

Take a look at the Kedro documentation to get started.

Rules and guidelines

In order to get the best out of the template:

  • Don't remove any lines from the .gitignore file we provide
  • Make sure your results can be reproduced by following a data engineering convention
  • Don't commit data to your repository
  • Don't commit any credentials or your local configuration to your repository. Keep all your credentials and local configuration in conf/local/

How to install dependencies

Declare any dependencies in src/requirements.txt for pip installation and src/environment.yml for conda installation.

To install them, run:

kedro install

How to run Kedro

You can run your Kedro project with:

kedro run

How to test your Kedro project

Have a look at the file src/tests/test_run.py for instructions on how to write your tests. You can run your tests as follows:

kedro test

To configure the coverage threshold, look at the .coveragerc file.

Project dependencies

To generate or update the dependency requirements for your project:

kedro build-reqs

This will copy the contents of src/requirements.txt into a new file src/requirements.in which will be used as the source for pip-compile. You can see the output of the resolution by opening src/requirements.txt.

After this, if you'd like to update your project requirements, please update src/requirements.in and re-run kedro build-reqs.

Further information about project dependencies

How to work with Kedro and notebooks

Note: Using kedro jupyter or kedro ipython to run your notebook provides these variables in scope: context, catalog, and startup_error.

Jupyter

To use Jupyter notebooks in your Kedro project, you need to install Jupyter:

pip install jupyter

After installing Jupyter, you can start a local notebook server:

kedro jupyter notebook

JupyterLab

To use JupyterLab, you need to install it:

pip install jupyterlab

You can also start JupyterLab:

kedro jupyter lab

IPython

And if you want to run an IPython session:

kedro ipython

How to convert notebook cells to nodes in a Kedro project

You can move notebook code over into a Kedro project structure using a mixture of cell tagging and Kedro CLI commands.

By adding the node tag to a cell and running the command below, the cell's source code will be copied over to a Python file within src/<package_name>/nodes/:

kedro jupyter convert <filepath_to_my_notebook>

Note: The name of the Python file matches the name of the original notebook.

Alternatively, you may want to transform all your notebooks in one go. Run the following command to convert all notebook files found in the project root directory and under any of its sub-folders:

kedro jupyter convert --all

How to ignore notebook output cells in git

To automatically strip out all output cell contents before committing to git, you can run kedro activate-nbstripout. This will add a hook in .git/config which will run nbstripout before anything is committed to git.

Note: Your output cells will be retained locally.

Package your Kedro project

Further information about building project documentation and packaging your project