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Obtain patch level metadata (e.g. geospatial bounds and cloud cover), save and demo DEP use case (sim search) #172
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The notebook
@weiji14 @yellowcap ready for when you have time to review. |
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Thanks @lillythomas! I haven't looked too closely yet, but would it be possible to show where the similarity search results are located? Maybe something like showing the bounding boxes of all the patches on a map, and also overlay where the original quarry points are. |
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Yes! Great idea. Working on this tomorrow. |
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Thanks Lilly, this is great to see! I did not yet have time get to the sim search part, but will get back to it on Monday. Left some comments for now.
Could you add some more context on what the purpose is? Do you think we going to apply this to a region for searching certain events / features?
"outputs": [], | ||
"source": [ | ||
"mrd = gpd.read_file(\n", | ||
" \"../mineral-resource-detection/training_data/draft_inputs/MRD_dissagregated_25.geojson\"\n", |
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Use url for this, then it becomes reproducible
"outputs": [], | ||
"source": [ | ||
"DATA_DIR = \"data/minicubes\"\n", | ||
"CKPT_PATH = \"/home/ubuntu/data/checkpoints/mae_epoch-24_val-loss-0.46.ckpt\"\n", |
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Same here, if we use huggingface its reproducible
https://huggingface.co/made-with-clay/Clay/resolve/main/Clay_v0.1_epoch-24_val-loss-0.46.ckpt
" box_emb = shapely.geometry.box(box_[0], box_[1], box_[2], box_[3])\n", | ||
"\n", | ||
" # Create the GeoDataFrame\n", | ||
" gdf = gpd.GeoDataFrame(data, geometry=[box_emb], crs=f\"EPSG:{epsg}\")\n", |
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Why making one pdf file per embedding and not one that contains all? This would make everything downstream much easier. Would it be possible to create one before the loop and add rows to it?
" lambda x: Path(x).stem.rsplit(\"/\")[-1].rsplit(\"_\")[0]\n", | ||
" )\n", | ||
" gdf[\"idx\"] = \"_\".join(emb.split(\"/\")[-1].split(\"_\")[2:]).replace(\".gpq\", \"\")\n", | ||
" gdf[\"box\"] = [gdf.geometry[0].bounds]\n", |
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This did not work for me, replaced with the following to get it to work
gdf["box"] = [box(*geom.bounds) for geom in gdf.geometry]
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Oh interesting. It works on my end. Do you have the traceback?
"# Combine patch level geodataframes into one\n", | ||
"embeddings_gdf = pd.concat(gdfs, ignore_index=True)\n", | ||
"# Make a polygon for each patch level bounding box\n", | ||
"embeddings_gdf[\"bbox\"] = embeddings_gdf[\"box\"].apply(lambda bbox: box(*bbox))" |
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The lambda function is no longer necessary if comment above is applied
}, | ||
{ | ||
"data": { | ||
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Here I got the following error.
ArrowInvalid: Could not convert <POLYGON ((177.507 -17.778, 177.507 -17.775, 177.504 -17.775, 177.504 -17.77...> with type Polygon: did not recognize Python value type when inferring an Arrow data type
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This was introduced by making your suggested change in #172 (comment) but I have a way to handling it. Essentially pyarrow doesn't like a polygon object, so I can get a list equivalent for the table by adding the bounds method to "box": row["box"]
e.g. "box": row["box"].bounds
, which I am in favor of doing as it allows us to drop the lamba function as you pointed out.
src/model_clay.py
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@@ -790,7 +790,7 @@ def __init__( # noqa: PLR0913 | |||
wd=0.05, | |||
b1=0.9, | |||
b2=0.95, | |||
embeddings_level: Literal["mean", "patch", "group"] = "mean", |
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I would drop this from this PR seems not necessary for the notebook to work.
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True. Will do.
… results and ground truth points on rgb xarray, add descriptions, revert change in model_clay.py
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This PR will work towards a demonstration of how to obtain patch level embeddings and write them to GeoParquet files to run similarity search with.
Main tasks that need to be done:
embeddings_level = "patch"
)Reference tickets: #168 #140