Let LightningDataModule return spatiotemporal metadata #66
Conversation
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Making the LightningDataModule return not only the image, but also spatiotemporal metadata such as the bounding box, coordinate reference system, and date. The bbox and crs is in the raster image's native UTM projection for now, while the date is just a YYYY-MM-DD formatted string. Unit tests have been updated to ensure that the extra metadata is passed through.
weiji14
commented
Dec 4, 2023
Comment on lines
+32
to
+33
| # Get date | ||
| date: str = pathlib.Path(filepath).name[15:25] # YYYY-MM-DD format |
There was a problem hiding this comment.
Don't like that we have to parse the date from the filename in a hardcoded way. @yellowcap, I hinted on this at #54 (comment), but would it be possible to save the datetime information in the GeoTIFF's metadata somehow?
There was a problem hiding this comment.
After the next pipeline run you'll be able to get the date using
dataset.tags()["date"]There was a problem hiding this comment.
Awesome, can't wait!
This was referenced Dec 4, 2023
Reduce duplicate code py using pytest.mark.parametrize, looping over fit and predict stages.
Improved the docstring of the _array_to_torch function, mentioning the input parameters (filepath) and the contents of the output dictionary (image, bbox, crs, date). Also updated the type hint of the function.
weiji14
commented
Dec 5, 2023
src/datamodule.py
Outdated
| # Get date | ||
| date: str = pathlib.Path(filepath).name[15:25] # YYYY-MM-DD format | ||
|
|
||
| return {"image": tensor, "bbox": bbox, "crs": crs, "date": date} |
There was a problem hiding this comment.
Tempted to store this in an Arrow Table with FixedShapedTensorArray for the image and bbox 'columns'. Possibly revisit this in the future.
|
Gonna leave this up for review for a day or so before merging. Once merged, I'll proceed to work on part 2/2, which is to get the model to output embeddings with spatiotemporal metadata columns! |
yellowcap
approved these changes
Dec 6, 2023
src/datamodule.py
Outdated
| bbox: torch.Tensor = torch.as_tensor( # xmin, ymin, xmax, ymax | ||
| data=dataset.bounds, dtype=torch.float64 | ||
| ) | ||
| crs: int = torch.as_tensor(data=dataset.crs.to_epsg(), dtype=torch.int32) |
There was a problem hiding this comment.
This variable could be called epsg to make clear its a crs as epsg integer.
There was a problem hiding this comment.
Was considering this actually, I've changed it in 6a0e8cf!
Since we're storing the EPSG integer and not the CRS representation.
3 tasks
weiji14
added a commit
that referenced
this pull request
Dec 8, 2023
Should have updated the type hints in #66, but might as well do it here. Also adding some more inline comments and fixed a typo.
weiji14
added a commit
that referenced
this pull request
Dec 8, 2023
* ✨ Save embeddings with spatiotemporal metadata to GeoParquet Storing the vector embeddings alongside some spatial bounding box and datetime information in a tabular GeoParquet format, instead of an npy file! Using geopandas to create a GeoDataFrame with three columns - date, embeddings, geometry. The date is stored in Arrow's date32 format, embeddings are in FixedShapedTensorArray, and geometry is in WKB. Have updated the unit test's sample fixture data with the extra spatiotemporal data, and tested that the saved GeoParquet file can be loaded back. * 📝 Document how embeddings are generated and saved to geoparquet Improve the docstring of predict_step in the LightningModule on how the embeddings are generated, and then saved to a GeoParquet file with the spatiotemporal metadata. Included some ASCII art and a markdown table of how the tabular data looks like. * 📝 Mention in main README.md that embeddings are saved to geoparquet Document that the embeddings are stored with spatiotemporal metadata as a GeoParquet file. Increased batch size from 1 to 1024. * 🎨 Update type hint of batch inputs, and add some inline comments Should have updated the type hints in #66, but might as well do it here. Also adding some more inline comments and fixed a typo.
4 tasks
brunosan
pushed a commit
that referenced
this pull request
Dec 27, 2023
* 🗃️ Let LightningDataModule return spatiotemporal metadata Making the LightningDataModule return not only the image, but also spatiotemporal metadata such as the bounding box, coordinate reference system, and date. The bbox and crs is in the raster image's native UTM projection for now, while the date is just a YYYY-MM-DD formatted string. Unit tests have been updated to ensure that the extra metadata is passed through. * ♻️ Refactor test_geotiffdatapipemodule to use parametrization Reduce duplicate code py using pytest.mark.parametrize, looping over fit and predict stages. * 📝 Document returned outputs from _array_to_torch function Improved the docstring of the _array_to_torch function, mentioning the input parameters (filepath) and the contents of the output dictionary (image, bbox, crs, date). Also updated the type hint of the function. * 🚚 Rename crs to epsg Since we're storing the EPSG integer and not the CRS representation.
brunosan
pushed a commit
that referenced
this pull request
Dec 27, 2023
* ✨ Save embeddings with spatiotemporal metadata to GeoParquet Storing the vector embeddings alongside some spatial bounding box and datetime information in a tabular GeoParquet format, instead of an npy file! Using geopandas to create a GeoDataFrame with three columns - date, embeddings, geometry. The date is stored in Arrow's date32 format, embeddings are in FixedShapedTensorArray, and geometry is in WKB. Have updated the unit test's sample fixture data with the extra spatiotemporal data, and tested that the saved GeoParquet file can be loaded back. * 📝 Document how embeddings are generated and saved to geoparquet Improve the docstring of predict_step in the LightningModule on how the embeddings are generated, and then saved to a GeoParquet file with the spatiotemporal metadata. Included some ASCII art and a markdown table of how the tabular data looks like. * 📝 Mention in main README.md that embeddings are saved to geoparquet Document that the embeddings are stored with spatiotemporal metadata as a GeoParquet file. Increased batch size from 1 to 1024. * 🎨 Update type hint of batch inputs, and add some inline comments Should have updated the type hints in #66, but might as well do it here. Also adding some more inline comments and fixed a typo.
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
Add this suggestion to a batch that can be applied as a single commit.
This suggestion is invalid because no changes were made to the code.
Suggestions cannot be applied while the pull request is closed.
Suggestions cannot be applied while viewing a subset of changes.
Only one suggestion per line can be applied in a batch.
Add this suggestion to a batch that can be applied as a single commit.
Applying suggestions on deleted lines is not supported.
You must change the existing code in this line in order to create a valid suggestion.
Outdated suggestions cannot be applied.
This suggestion has been applied or marked resolved.
Suggestions cannot be applied from pending reviews.
Suggestions cannot be applied on multi-line comments.
Suggestions cannot be applied while the pull request is queued to merge.
Suggestion cannot be applied right now. Please check back later.
Making the LightningDataModule return not only the image, but also spatiotemporal metadata such as:
Note:
This is part 1/2 of adding spatiotemporal metadata to the output embedding table later, as mentioned at #35 (comment).