Training framework & tools for PyTorch-based machine learning projects.
This package provides a training framework and CLI for PyTorch-based machine learning projects. This is free software distributed under the Apache Software License version 2.0 built by researchers and developers from the Centre de Recherche Informatique de Montréal / Computer Research Institute of Montreal (CRIM).
To get a general idea of what this framework can be used for, visit the FAQ page. For installation instructions, refer to the installation guide. For usage instructions, refer to the user guide. The auto-generated documentation is available via readthedocs.io.
Development is still on-going — the API and internal classes may change in the future.
The project’s structure was originally generated by cookiecutter via ionelmc’s template.
Added SelectChannels transform operator for inplace sample modifications that might need it.
Fixed conda package builds for tagged deployments
Refactored and cleaned up HDF5 data extraction/parsing classes
Added dataset interfaces for BigEarthNet, Agri-Vis challenge
Update classification task to allow multi-label classification
Added activation layer customization for in-framework ResNet archs
Updated default move_tensor behavior to be non-blocking
Added trainer implementation for auto-encoder-type models
Added Orion reporting support for hyperparameter explorations
Added SLURM cluster utilities (tmpdir getter, launch scripts)
Skip image save call during metric rendering if the provided value is None as employed by basic logger/reporter.
Add JSON implementation for thelper.train.utils.ClassifLogger.
Fix concepts to handle any variation of upper/lower concept name.
Employ requirements.txt within conda-env.yml to kept dependencies in sync.
Fixes built docker image not using appropriate dependencies enforced through requirements.txt.
Fix version comparison check when validating configuration and/or checkpoint against package version. Version can now have a release part which was not considered.
Fix incorrect calculation of sample coordinates in thelper.data.geo.parsers.SlidingWindowDataset.
Remove not_skip = __init__.py config option for isort since __init__.py is included since 4.3.5. Also force isort<5 since many import checks break suddenly (e.g.: direct import with as alias break).
Update this changelog to use rst links (renders on github and readthedocs)
Add infer mode for classification of geo-referenced rasters
Add Dockerfile-geo to build thelper with pre-installed geo packages
Add geo-related build instructions to travis-ci build steps
Add auto-documentation of makefile targets and docker related targets
Removed optional dependencies from conda build env
Travis deploy test w/ split conda/docker stages
Split travis deploy stage into two phases
Fixed draw_segment threshold usage & params lookup
Fixed FCResNet embedding getter wrt latest pooling update
Update all matplotlib plots to use 160 dpi by default
Refactor trainer data/metric writer to save all viz data
Added viz pkg w/ t-SNE & UMAP support for in-trainer usage
Fixed geo pkg documentation build issue related to mocking
Fixed type and output format checks in numerous metrics
Updated all callback readers to rely on new utility function
Cleaned and optimize coordconv implementation
Added U-Net architecture implementation to nn package
Added IoU metric implementation
Added support for SRM kernels and SRM convolutions
Updated documentation (install, faq, maintenance)
Added fixed weight sampler to data package
Added lots of extra unit tests
Added efficientnet 3rd-party module wrapper
Fixed potential conflicts in task class names ordering
Fixed pytest-mock scope usage in metrics utests
Updated common resnet impl to support segmentation heads
Fixed samples usage for auto-weighting of loss functions
Cleaned up samples usage in loader factory data splitter
Add GDL compatibility module to geo package
Fix segmentation task dontcare default color mapping
Cleaned up and simplified coordconv implementation
Update segmentation trainer to use long-typed label maps
Cleaned up augmentor/albumentations demo configurations
Removed travis check in deploy stage for master branch
Added geo subpackage
Added geo vector/raster parsing classes
Added ogc module for testbed15-specific utilities
Added testbed15 train/viz configuration files
Cleaned up makefile targets & coverage usage
Replaced tox build system with makefile completely
Merged 3rdparty configs into setup.cfg
Updated travis to rely on makefile directly
Added extra logging calls in trainer and framework utils
Cleaned up data configuration parsing logger calls
Bypassed full device check when specific one is requested
Moved drawing utilities to new module
Cleaned up output root/save directory parsing
Cleaned up potential circular imports
Moved optional dependency imports inside relevant functions
Added support for root directory specification via config
Updated config load/save to make naming optional
Fixed potential issue when reinstantiating custom ResNet
Fixed ClassifLogger prediction logger w/o groundtruth
Add cli/config override for task compatibility mode setting
Cleaned up dependency lists, docstrings
Fixed bbox iou computation with mixed int/float
Fixed dontcare label deletion in segmentation task
Cleaned up training session output directory localization
Fixed object detection trainer empty bbox lists
Fixed exponential parsing with pyyaml
Fixed bbox display when using integer coords values
Fixed collate issues for pytorch >= 1.2
Fixed null-size batch issues
Cleaned up params#kwargs parsing in trainer
Added pickled hashed param support utils
Added support for yaml-based session configuration
Added concept decorators for metrics/consumer classes
Cleaned up shared interfaces to fix circular dependencies
Added detection (bbox) logger class
Fixed nn modules constructor args forwarding
Updated class importer to allow parsing of non-package dirs
Fixed file-based logging from submodules (e.g. for all data)
Cleaned and API-fied the CLI entrypoints for external use
Fixed travis timeouts on long deploy operations
Added output path to trainer callback impls
Added new draw-and-save display callback
Added togray/tocolor transformation operations
Cleaned up matplotlib use and show/block across draw functions
Fixed various dependency and logging issues
Fixed torch version checks in custom default collate impl
Fixed bbox predictions forwarding and evaluation in objdetect
Refactored metrics/callbacks to clean up trainer impls
Added pretrained opt to default resnet impl
Fixed objdetect trainer display and prediction callbacks
Refactored metrics/consumers into separate interfaces
Added unit tests for all metrics/prediction consumers
Updated trainer callback signatures to include more data
Updated install doc with links to anaconda/docker hubs
Cleaned drawing functions args wrt callback refactoring
Added eval module to optim w/ pascalvoc evaluation funcs
Fixed issues when reloading objdet model checkpoints
Fixed issues when trying to use missing color maps
Fixed backward compat issues when reloading old tasks
Cleaned up object detection drawing utilities
Fixed travis conda build dependencies & channels
Update documentation use cases (model export) & faq
Cleanup module base class config backup
Fixed docker build and automated it via travis
Fix metrics RawPredictions not returning predictions during eval
Fix parsing of checkpoint base path
Added dockerfile for containerized builds
Added object detection task & trainer implementations
Added CLI model/checkpoint export support
Added CLI dataset splitting/HDF5 support
Added baseline superresolution implementations
Added lots of new unit tests & docstrings
Cleaned up transform & display operations
Cleaned up build tools & docstrings throughout api
Added user guide in documentation build
Update tasks to allow dataset interface override
Cleaned up trainer output logs
Added fully convolutional resnet implementation
Fixup various issues related to fine-tuning via ‘resume’
Updated conda build recipe for python variants w/ auto upload
Added framework checkpoint/configuration migration utilities
Fixed minor config parsing backward compatibility issues
Fixed minor bugs related to query & drawing utilities
Fix travis-ci conda build/env path
Fix travis-ci conda channel setup
Fix openssl dependency
Fixed travis-ci matrix configuration
Added travis-ci deployment step for pypi
Fixed readthedocs documentation building
Updated readme shields & front page look
Cleaned up cli module entrypoint
Fixed openssl dependency issues for travis tox check jobs
Updated travis post-deploy to try to fix conda packaging (wip)
Added typedef module & cleaned up parameter inspections
Cleaned up all drawing utils & added callback support to trainers
Added support for albumentation pipelines via wrapper
Updated all trainers/schedulers to rely on 0-based indexing
Updated travis/rtd configs for auto-deploy & 3.6 support
Added regression/segmentation tasks and trainers
Added interface for pascalvoc dataset
Refactored data loaders/parsers and cleaned up data package
Added lots of new utilities in base trainer implementation
Added new unit tests for transformations
Refactored transformations to use wrappers for augments/lists
Added new samplers with dataset scaling support
Added baseline implementation for FCN32s
Added mae/mse metrics implementations
Added trainer support for loss computation via external members
Added utils to download/verify/extract files
Minor fixups and updates for CCFB02 compatibility
Added RawPredictions metric to fetch data from trainers
Fixed readthedocs sphinx auto-build w/ mocking.
Refactored package structure to avoid env issues.
Rewrote seeding to allow 100% reproducible sessions.
Cleaned up config file parameter lists.
Cleaned up session output vars/logs/images.
Add support for eval-time augmentation.
Update transform wrappers for multi-channels & lists.
Add gui module w/ basic segmentation annotation tool.
Refactored task interfaces to allow merging.
Simplified model fine-tuning via checkpoints.
Completed first documentation pass.
Fixed travis/rtfd builds.
Fixed device mapping/loading issues.
Initial release (work in progress).
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