Fix bugs in cultivated and woody cover plugins (#149)

* round the predict output to float32 resolution

* correct the data type in woody cover

* exit gracefully if band is missing for cultivated and woody

* fix woody cover aggregation

* add the hacky fix

* comment the docker file

* please docker lint

---------

Co-authored-by: Emma Ai <emma.ai@ga.gov.au>

Author: emmaai

docker/Dockerfile

@@ -22,6 +22,11 @@ ENV GDAL_DRIVER_PATH=/env/lib/gdalplugins \
     GDAL_DATA=/env/share/gdal \
     PATH=/env/bin:$PATH
 
+# here is very hacky fix for the threading issue
+# MUST follow up with package owner and further address the issue accordingly
+
+RUN wget -q -O /env/lib/python3.10/site-packages/numexpr/necompiler.py https://raw.githubusercontent.com/emmaai/numexpr/master/numexpr/necompiler.py
+
 WORKDIR /tmp
 
 RUN odc-stats --version 

odc/stats/plugins/lc_ml_treelite.py

@@ -6,6 +6,7 @@
 from typing import Dict, Sequence, Optional
 
 import os
+import sys
 import numpy as np
 import numexpr as ne
 import xarray as xr
@@ -21,6 +22,7 @@
 from ._registry import StatsPluginInterface
 from ._worker import TreeliteModelPlugin
 import tl2cgen
+import logging
 
 
 def mask_and_predict(
@@ -44,6 +46,8 @@ def mask_and_predict(
     if block_masked.shape[0] > 0:
         dmat = tl2cgen.DMatrix(block_masked)
         output_data = predictor.predict(dmat).squeeze(axis=1)
+        # round the number to float32 resolution
+        output_data = np.round(output_data, 6)
         if ptype == "categorical":
             prediction[mask_flat] = output_data.argmax(axis=-1)[..., np.newaxis]
         else:
@@ -70,6 +74,7 @@ def __init__(
         self.dask_worker_plugin = TreeliteModelPlugin(model_path)
         self.output_classes = output_classes
         self.mask_bands = mask_bands
+        self._log = logging.getLogger(__name__)
 
     def input_data(
         self, datasets: Sequence[Dataset], geobox: GeoBox, **kwargs
@@ -117,6 +122,7 @@ def input_data(
 
     def preprocess_predict_input(self, xx: xr.Dataset):
         images = []
+        veg_mask = None
         for var in xx.data_vars:
             image = xx[var].data
             if var not in self.mask_bands:
@@ -140,6 +146,9 @@ def preprocess_predict_input(self, xx: xr.Dataset):
                     **{"_v": int(self.mask_bands[var])},
                 )
 
+        if veg_mask is None:
+            raise TypeError("Missing Veg Mask")
+
         images = [
             da.concatenate([image, veg_mask[..., np.newaxis]], axis=-1).rechunk(
                 (None, None, image.shape[-1] + veg_mask.shape[-1])
@@ -157,7 +166,12 @@ def aggregate_results_from_group(self, predict_output):
         pass
 
     def reduce(self, xx: xr.Dataset) -> xr.Dataset:
-        images = self.preprocess_predict_input(xx)
+        try:
+            images = self.preprocess_predict_input(xx)
+        except TypeError as e:
+            self._log.warning(e)
+            sys.exit(0)
+
         res = []
 
         for image in images:

odc/stats/plugins/lc_treelite_woody.py

@@ -63,13 +63,13 @@ def aggregate_results_from_group(self, predict_output):
         )
 
         if m_size > 1:
-            predict_output = predict_output.sum(axis=0).astype("int")
+            predict_output = predict_output.sum(axis=0)
 
         predict_output = expr_eval(
             "where((a/nodata)>=_l, nodata, a%nodata)",
             {"a": predict_output},
             name="summary_over_classes",
-            dtype="uint8",
+            dtype="float32",
             **{
                 "_l": m_size,
                 "nodata": NODATA,
@@ -80,7 +80,7 @@ def aggregate_results_from_group(self, predict_output):
             "where((a>0)&(a<nodata), _nw, a)",
             {"a": predict_output},
             name="output_classes_herbaceous",
-            dtype="uint8",
+            dtype="float32",
             **{"nodata": NODATA, "_nw": self.output_classes["herbaceous"]},
         )
 

tests/test_rf_models.py

@@ -492,6 +492,10 @@ def test_cultivated_reduce(
         == np.array([[112, 255], [112, 112]], dtype="uint8")
     ).all()
 
+    with pytest.raises(SystemExit) as excinfo:
+        cultivated.reduce(input_datasets.drop("classes_l3_l4"))
+    assert excinfo.value.code == 0
+
 
 def test_woody_aggregate_results(
     woody_input_bands,