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Update dev/main #276
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Update dev/main #276
Commits on Jul 22, 2024
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fix: 🐛 fix how file stats are updated (#265)
Prepend existing stats for file stats store to prevent overwriting.
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Commits on Jul 23, 2024
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fix: 🐛 Fix validation model loading.
"validate_run" must now be called to do validation from run name. The Run must be preloaded and passed to call "validate" directly.
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Commits on Jul 25, 2024
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Merge branch 'validate_patch' of github.com:janelia-cellmap/dacapo in…
…to validate_patch
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Commits on Jul 29, 2024
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Merge branch 'validate_patch' of https://github.com/janelia-cellmap/d…
…acapo into validate_patch
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Commits on Jul 30, 2024
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Format Python code with psf/black push (#281)
There appear to be some python formatting errors in f604f40. This pull request uses the [psf/black](https://github.com/psf/black) formatter to fix these issues.
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Commits on Aug 2, 2024
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Merge branch 'validate_patch' of github.com:janelia-cellmap/dacapo in…
…to validate_patch
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Format Python code with psf/black push (#282)
There appear to be some python formatting errors in cb300f5. This pull request uses the [psf/black](https://github.com/psf/black) formatter to fix these issues.
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Format Python code with psf/black push (#283)
There appear to be some python formatting errors in 81ec675. This pull request uses the [psf/black](https://github.com/psf/black) formatter to fix these issues.
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Commits on Aug 7, 2024
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Format Python code with psf/black push (#285)
There appear to be some python formatting errors in 50d9833. This pull request uses the [psf/black](https://github.com/psf/black) formatter to fix these issues.
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Commits on Aug 12, 2024
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Merge branch 'validate_patch' of github.com:janelia-cellmap/dacapo in…
…to validate_patch
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Commits on Aug 13, 2024
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Fixed a couple small typos I encountered running examples
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Commits on Sep 5, 2024
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fix: 🐛 Fix
datasplit._neuroglancer
bug that happened with binarized…… arrays. Also set GT to automatically be a segmentation layer in neuroglancer, for run.visualize_pipeline
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Commits on Sep 10, 2024
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Format Python code with psf/black push (#290)
There appear to be some python formatting errors in 38a1b7e. This pull request uses the [psf/black](https://github.com/psf/black) formatter to fix these issues.
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Commits on Sep 12, 2024
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Feat: datasplit limit validation size (#289)
Sometimes i want to speedup validation so as option we can limit validation size: `max_validation_volume_size = 600**3`
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Commits on Sep 23, 2024
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Commits on Sep 24, 2024
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Format Python code with psf/black push (#295)
There appear to be some python formatting errors in 4583723. This pull request uses the [psf/black](https://github.com/psf/black) formatter to fix these issues.
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Commits on Sep 25, 2024
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Commits on Oct 2, 2024
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Format Python code with psf/black push (#299)
There appear to be some python formatting errors in d899c63. This pull request uses the [psf/black](https://github.com/psf/black) formatter to fix these issues.
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Add bind parameters to neuroglancer instantiations (#297)
This PR just adds a `bind_address` and `bind_port` parameter to all neuroglancer instantiation calls. The parameter defaults do not change prior behavior, and specifying bind parameters is useful for me to access neuroglancer via ssh tunnels to a cluster that does not normally expose any ports/addresses.
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Format Python code with psf/black push (#300)
There appear to be some python formatting errors in 7794118. This pull request uses the [psf/black](https://github.com/psf/black) formatter to fix these issues.
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Format Python code with psf/black push (#301)
There appear to be some python formatting errors in 525f2fe. This pull request uses the [psf/black](https://github.com/psf/black) formatter to fix these issues.
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Commits on Oct 16, 2024
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Adds a simple tutorial start-to-finish tutorial for DaCapo. Still a few things left to resolve before its ready for merging: - [ ] Loss behaving strangely after the first validation (temporary solution involves giving an extra singleton dimension to the provided data) - [ ] Validation fails (we want to show validation loss/scores, validation outputs across iterations, and how to finally take the best iteration and apply it with the post processing to a volume manually) - [ ] stdout too verbose - [ ] plotting functions should be built into `DaCapo` (there are plotting functions, not sure how to include bokeh plots so I didn't dive too deep into this)
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Commits on Oct 17, 2024
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Format Python code with psf/black push (#306)
There appear to be some python formatting errors in 25f3812. This pull request uses the [psf/black](https://github.com/psf/black) formatter to fix these issues.
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Commits on Oct 21, 2024
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Commits on Oct 22, 2024
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Add a basic in-memory postprocessor for watershed. It doesn't seem to be working that well because prediction seems broken. Here's a picture: ![Figure_1](https://github.com/user-attachments/assets/01c209e3-914e-44a2-85b4-70343b1a984f) I expect the postprocessor to work significantly better if the predictions are done with enough context to avoid the block artifacts and the predictions are post processed properly so that high affinity within objects leads to an affinity of 1 (it looks like its set to 0 here), and a low affinity in the background leads to an affinity of 0 (looks like it is set to 0.5 here)
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@pattonw Adds a simple tutorial start-to-finish tutorial for DaCapo. Still a few things left to resolve before its ready for merging: - [ ] Loss behaving strangely after the first validation (temporary solution involves giving an extra singleton dimension to the provided data) - [ ] Validation fails (we want to show validation loss/scores, validation outputs across iterations, and how to finally take the best iteration and apply it with the post processing to a volume manually) - [ ] stdout too verbose - [ ] plotting functions should be built into `DaCapo` (there are plotting functions, not sure how to include bokeh plots so I didn't dive too deep into this)
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improve watershed training and validation
train on labels not mask, learn z affs for 3d objects, visualize with label colormap and without interpolation.
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Commits on Oct 23, 2024
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add some improvements to DaCapo and the basic tutorial for learning instance segmentation through affs + watershed DaCapo: 1) avoid doing any special postprocessing to convert to uint8 when writing out the predictions. just store as float32 2) avoid converting back to float in the watershed postprocessor, just use the predictions as saved in the zarr. Tutorial: 1) add labels colormap 2) train z affs 3) use valid padding Loss and validation plots are included below. There is still something strange happening with the loss after the first validation. The results still aren't as nice as they should be on such a simple toy dataset. ![Figure_2](https://github.com/user-attachments/assets/8badb5ca-5f4b-4e8e-821b-80f13c4987eb) ![Figure_3](https://github.com/user-attachments/assets/6f7276b6-5e3b-40b1-bcdc-ba0f2b865052)
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David Ackerman committed
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feat: ✨ Add support for training on Apple M1/M2/M3 (mps) devices.
Also add log printing of device used for training.
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Update gunpowder_trainer.py to swap to(device) and float() (#312)
One more place where you have to(device) and then float(). numpy default float is 64, so the to(device) will fail if the device is MPS that doesn't support float64.
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Commits on Oct 28, 2024
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Add optional probabilities to augmentations (#314)
This PR adds an optional `augmentation_probability: float = 1.` argument to `ElasticAugment`, `IntensityAugment`, `SimpleAugment`.
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fixing batch dim bugs (batch norm requires batch dimension even in predict mode) Seems to also fix the strange loss spike. I think it was due to setting model into eval mode and then not resetting to training at the end
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Add support for most of the remaining arrays.
Exceptions DVID and Resampled arrays
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Funlib persistence update (#322)
Upgrade to funlib.persistence `0.5`. This update makes a one big improvement: Custom `Array` class no longer needed. We used this mostly just to apply preprocessing lazily to large arrays. New `funlib` `Array` class uses `dask` internally which comes with much better support for lazy array operations than we built for ourselves. The `ZarrArray` and `NumpyArray` class which were used extensively throughout `DaCapo` have now been replaced with simple `funlib.persistence.Array`s. A minor incompatibility: `funlib.persistence.Array` has a convention (for now) that all axes have names, but non-spatial axes have a "^" in their name. This will be fixed in the near future. For now, DaCapo convention needed to change a little bit to adapt to this. We now have to use "c^" and "b^" for channel and batch dimensions instead of just "c" and "b". TODOs: This pull request is not quire ready to merge. I pass the tests run with `pytest`, and the `minimal_tutorial` notebook executes. But there is a lot of code that is not tested. Specifically many of the `ArrayConfig` subclasses are not yet tested and some are missing implementations. Here are the Preprocessing array configs, whether or not their implementation is complete, and their code coverage: - [X] BinarizeArrayConfig 96% - [X] ConcatArrayConfig 60% - [X] ConstantArrayConfig 57% - [X] CropArrayConfig 69% - [X] DummyArrayConfig 91% - [ ] DVIDArrayConfig 90% (misleading, only skeleton implementation so not much to test) - [X] IntensitiesArrayConfig 75% - [X] LogicalOrArrayConfig 60% - [x] MergeInstancesArrayConfig 100% (misleading, no implementation so nothing to test) - [x] MissingAnnotationsMaskConfig 100% (misleading) - [x] OnesArrayConfig 100% (misleading) - [ ] ResampledArrayConfig 100% (misleading) - [x] SumArrayConfig 100% (misleading) - [x] TiffArrayConfig 0% - [X] ZarrArrayConfig 70% Best practice would be to add tests before merging, but I want to put this here so others can test it
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Format Python code with psf/black push (#324)
There appear to be some python formatting errors in aeb77a6. This pull request uses the [psf/black](https://github.com/psf/black) formatter to fix these issues.
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zouinkhim committed
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Format Python code with psf/black push (#328)
There appear to be some python formatting errors in 1813190. This pull request uses the [psf/black](https://github.com/psf/black) formatter to fix these issues.
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Adds: 1) A data doc that defines our expectations for data formatting 2) Simplified data configuration classes 3) Deprication warnings for old configuration classes
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Format Python code with psf/black push (#336)
There appear to be some python formatting errors in e69bba2. This pull request uses the [psf/black](https://github.com/psf/black) formatter to fix these issues.
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fix bug in cnnectome_unet return types
The Architecture superclass says that we should be returning coordinates for these properties This is important because we regularly use these values in arithmetic where we expect to execute +-*/ element wise
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CNNectomeUNet: make the final conv pass in the upsample pass a bit mo…
…re robust If we upsample, we probably want to apply a convolution to finetune the outputs rather than simply upsampling which we could do outside of a network. If we assume a kernel of size (3, 3, 3), it fails for 2D networks that process using kernels of size (1, 3, 3). We now just use the last kernel in the kernel size up. This is a bit more robust.
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Gunpowder Trainer: if the raw data doesn't have a channel dim, add it…
… during training Otherwise the BatchNorm breaks
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Datasplit test fixture: use the voxel_size attribute for voxel size.
This should probably just be switched to use `funlib.perisistence.prepare_ds`
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Fixed a couple bugs 1) CNNectome UNet wasn't following the api defined by the Architecture `ABC`. It was supposed to be returning `Coordinate` class instances for voxel_size, input_shape etc. 2) CNNectome UNet had logical errors in the definition of kernel_size_up and down. 3) New tests was expecting multi channel data for the 2D UNet and single channel for the 3D unet despite always getting single channel data. 4) fix voxel_size attr in test fixtures
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Format Python code with psf/black push (#339)
There appear to be some python formatting errors in dba261f. This pull request uses the [psf/black](https://github.com/psf/black) formatter to fix these issues.
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Format Python code with psf/black push (#340)
There appear to be some python formatting errors in 392ddb3. This pull request uses the [psf/black](https://github.com/psf/black) formatter to fix these issues.
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Format Python code with psf/black push (#341)
There appear to be some python formatting errors in fde6af2. This pull request uses the [psf/black](https://github.com/psf/black) formatter to fix these issues.
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Format Python code with psf/black push (#344)
There appear to be some python formatting errors in 2763f12. This pull request uses the [psf/black](https://github.com/psf/black) formatter to fix these issues.
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Commits on Nov 15, 2024
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Format Python code with psf/black push (#345)
There appear to be some python formatting errors in 97abfa0. This pull request uses the [psf/black](https://github.com/psf/black) formatter to fix these issues.
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autouse fork/spawn fixture so we test both
this is necessary for getting compatibility with windows and macos
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bugfix: Distance predictor wasn't working properly when run on single…
… class prediction tasks
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If the kernel size of the task is set to 3, then this will cause errors if you are trying to use a model with input shape `(1, y, x)`. The head should just be mapping the penultimate layer embeddings into the appropriate dimensions for the task so it desn't need a size larger than 1.
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bugfix: remove the mask_integral_downsample_factor from use. Its not …
…used anywhere else
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Simplified the parameterized train test, and added validation. Fixed bugs that were found
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Format Python code with psf/black push (#347)
There appear to be some python formatting errors in 85a6dda. This pull request uses the [psf/black](https://github.com/psf/black) formatter to fix these issues.
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Commits on Dec 3, 2024
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fix small bug in simple dataset config
`get_paths` gets the paths per dataset by searching for "raw", but should return the path to the dataset containing raw
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fix small bug in simple dataset config (#351)
`get_paths` gets the paths per dataset by searching for "raw", but should return the path to the dataset containing raw
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