CHANGELOG¶
Raster Vision 0.10¶
Raster Vision 0.10.0¶
Notes on switching to PyTorch-based backends¶
The current backends based on Tensorflow have several problems:
They depend on third party libraries (Deeplab, TF Object Detection API) that are complex, not well suited to being used as dependencies within a larger project, and are each written in a different style. This makes the code for each backend very different from one other, and unnecessarily complex. This increases the maintenance burden, makes it difficult to customize, and makes it more difficult to implement a consistent set of functionality between the backends.
Tensorflow, in the maintainer’s opinion, is more difficult to write and debug than PyTorch (although this is starting to improve).
The third party libraries assume that training images are stored as PNG or JPG files. This limits our ability to handle more than three bands and more that 8-bits per channel. We have recently completed some research on how to train models on > 3 bands, and we plan on adding this functionality to Raster Vision.
Therefore, we are in the process of sunsetting the Tensorflow backends (which will probably be removed) and have implemented replacement PyTorch-based backends. The main things to be aware of in upgrading to this version of Raster Vision are as follows:
Instead of there being CPU and GPU Docker images (based on Tensorflow), there are now tf-cpu, tf-gpu, and pytorch (which works on both CPU and GPU) images. Using
./docker/build --tf
or./docker/build --pytorch
will only build the TF or PyTorch images, respectively.Using the TF backends requires being in the TF container, and similar for PyTorch. There are now
--tf-cpu
,--tf-gpu
, and--pytorch-gpu
options for the./docker/run
command. The default setting is to use the PyTorch image in the standard (CPU) Docker runtime.The raster-vision-aws CloudFormation setup creates Batch resources for TF-CPU, TF-GPU, and PyTorch. It also now uses default AMIs provided by AWS, simplifying the setup process.
To easily switch between running TF and PyTorch jobs on Batch, we recommend creating two separate Raster Vision profiles with the Batch resources for each of them.
The way to use the
ConfigBuilders
for the new backends can be seen in the examples repo and the BackendConfig
Features¶
Add confusion matrix as metric for semantic segmentation #788
Add predict_chip_size as option for semantic segmentation #786
Handle “ignore” class for semantic segmentation #783
Add stochastic gradient descent (“SGD”) as an optimizer option for chip classification #792
Add option to determine if all touched pixels should be rasterized for rasterized RasterSource #803
Script to generate GeoTIFF from ZXY tile server #811
Remove QGIS plugin #818
Add PyTorch backends and add PyTorch Docker image #821 and #823.
Raster Vision 0.9¶
Raster Vision 0.9.0¶
Features¶
Add requester_pays RV config option #762
Unify Docker scripts #743
Switch default branch to master #726
Merge GeoTiffSource and ImageSource into RasterioSource #723
Simplify/clarify/test/validate RasterSource #721
Simplify and generalize geom processing #711
Predict zero for nodata pixels on semantic segmentation #701
Add support for evaluating vector output with AOIs #698
Conserve disk space when dealing with raster files #692
Optimize StatsAnalyzer #690
Include per-scene eval metrics #641
Make and save predictions and do eval chip-by-chip #635
Decrease semseg memory usage #630
Add support for vector tiles in .mbtiles files #601
Add support for getting labels from zxy vector tiles #532
Remove custom
__deepcopy__
implementation fromConfigBuilder
s. #567Add ability to shift raster images by given numbers of meters. #573
Add ability to generate GeoJSON segmentation predictions. #575
Add ability to run the DeepLab eval script. #653
Submit CPU-only stages to a CPU queue on Aws. #668
Parallelize CHIP and PREDICT commands #671
Refactor
update_for_command
to split out the IO reporting intoreport_io
. #671Add Multi-GPU Support to DeepLab Backend #590
Handle multiple AOI URIs #617
Give
train_restart_dir
Default Value #626Use
`make
to manage local execution #664Optimize vector tile processing #676
Bug Fixes¶
Fix Deeplab resume bug: update path in checkpoint file #756
Allow Spaces in
--channel-order
Argument #731Fix error when using predict packages with AOIs #674
Correct checkpoint name #624
Allow using default stride for semseg sliding window #745
Fix filter_by_aoi for ObjectDetectionLabels #746
Load null channel_order correctly #733
Handle Rasterio crs that doesn’t contain EPSG #725
Fixed issue with saving semseg predictions for non-georeferenced imagery #708
Fixed issue with handling width > height in semseg eval #627
Fixed issue with experiment configs not setting key names correctly #576
Fixed issue with Raster Sources that have channel order #576