0.5.0 Release Timeline #1520
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Draft of release notes: TorchGeo 0.5.0 Release Notes0.5.0 encompasses over 8 months of hard work and new features contributed by 20 users from around the world. Below, we detail specific features worth highlighting. Highlights of this releaseNew command-line interfaceTorchGeo has always had tight integration with PyTorch Lightning, including datamodules for common benchmark datasets and trainers for most computer vision tasks. TorchGeo 0.5.0 introduces a new command-line interface for model training based on LightningCLI. It can be invoked in two ways: # If torchgeo has been installed
torchgeo
# If torchgeo has been installed, or if it has been cloned to the current directory
python3 -m torchgeoIt supports command-line configuration or YAML/JSON config files. Valid options can be found from the help messages: # See valid stages
torchgeo --help
# See valid trainer options
torchgeo fit --help
# See valid model options
torchgeo fit --model.help ClassificationTask
# See valid data options
torchgeo fit --data.help EuroSAT100DataModuleUsing the following config file: trainer:
max_epochs: 20
model:
class_path: ClassificationTask
init_args:
model: "resnet18"
in_channels: 13
num_classes: 10
data:
class_path: EuroSAT100DataModule
init_args:
batch_size: 8
dict_kwargs:
download: truewe can see the script in action: # Train and validate a model
torchgeo fit --config config.yaml
# Validate-only
torchgeo validate --config config.yaml
# Calculate and report test accuracy
torchgeo test --config config.yamlIt can also be imported and used in a Python script if you need to extend it to add new features: from torchgeo.main import main
main(["fit", "--config", "config.yaml"])See the Lightning documentation for more details. Self-supervised learning and Landsat
Self-supervised learning has become a dominant technique for model pre-training, especially in domains (like remote sensing) that are rich in data but lacking in large labeled datasets. The 0.5.0 release adds powerful trainers for the following SSL techniques: large unlabeled datasets for multiple satellite platforms: and the first ever models pre-trained on Landsat imagery. See our SSL4EO-L paper for more details. Utilities for splitting GeoDatasetsIn prior releases, the only way to create train/val/test splits of GeoDatasets was to use a Sampler
Splitting with a Sampler GeoDatasets now accept lists as inputPreviously, each GeoDataset accepted a single root directory as input. Now, users can pass one or more directories, or a list of files they want to include. At first glance, this doesn't seem like a big deal, but it actually opens a lot of possibilities for how users can construct GeoDatasets. For example, users can use custom filters: files = []
for file in glob.glob("*.tif"):
# check pixel QA band or metadata file
if cloud_cover < 20: # select images with minimal cloud cover
files.append(file)
ds = Landsat8(files)or use remote files from S3 buckets or Azure blob storage. Basically, as long as GDAL knows how to read the file, TorchGeo supports it, wherever the file lives. Note that some datasets may not support a list of files if you also want to automatically download the dataset because we need to know the directory to download to. Building a communityWith over 50 contributors from around the world, we needed a better way to discuss ideas and share announcements. TorchGeo now has a public Slack channel! Join us and say hello 👋 Now that the majority of the features we've needed have been implemented, one of our goals for the next release is to improve our documentation and tutorials. Expect to see TorchGeo tutorials at all the popular ML/RS conferences next year! We're excited to meet our users in person and learn more about their unique use cases and needs. Backwards-incompatible changes
Dependencies
DatamodulesNew datamodules:
Changes to existing datamodules:
New base classes:
Changes to existing base classes:
DatasetsNew datasets:
Changes to existing datasets:
Changes to existing base classes:
New utility functions:
ModelsChanges to existing models:
New pre-trained model weights:
Changes to existing pre-trained model weights:
SamplersChanges to existing samplers:
TrainersNew trainers:
Changes to existing trainers:
New base classes:
TransformsNew transforms:
ScriptsNew scripts:
Documentation
Testing
ContributorsThis release is thanks to the following contributors: @AABNassim |
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Hi, gave a first quick try to upgrade to 0.5.0 and noticed:
very high level questions:
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have to add... joined a month ago with 0.4.1. TorchGeo looks fantastic. will definitely be keeping up with the upcoming versions. |
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We mark all minimum supported versions, so if any of your other dependencies are too old, pip will automatically update them for you, you don't need to manually do anything.
0.5.0 is released, see https://pypi.org/project/torchgeo/0.5.0/.
More tutorials is one of our biggest focuses for the next year, expect to see many more in the near future. |
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We're planning a 0.5.0 release for September. This will include all features in the following milestones:
If you have any new features you would like to see in this release, or bugs you would like to see fixed before then, now is the time to push forward on PRs. Any questions about this release or suggestions for things that should be required before the release is finalized should be added here. We will implement a feature freeze on the releases/v0.5 branch a few days before the release is finalized in order to finish testing and release prep.
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