housekeeping before ownership txfer

fao_models/beam_utils.py

@@ -6,11 +6,11 @@
 import numpy as np
 from models import get_model, freeze
 
-def parse_shp_to_latlon(file):
+def parse_shp_to_latlon(file,id_field:str='PLOTID'):
     gdf = gpd.read_file(file)
     gdf.loc[:,'centroid'] = gdf.geometry.centroid
     gdf.loc[:,'lonlat'] = gdf.centroid.apply(lambda x: [x.x, x.y])
-    return gdf[['global_id', 'lonlat']].values.tolist()
+    return gdf[[id_field, 'lonlat']].values.tolist()
 
 def get_ee_img(coords):
     """retrieve s2 image composite from ee at given coordinates. coords is a tuple of (lon, lat) in degrees."""

fao_models/model_predict.py

@@ -1,7 +1,7 @@
 import numpy as np
 import datetime
 import logging
-from .models import get_model
+from models import get_model
 import os
 import tensorflow as tf
 import rasterio as rio
@@ -63,11 +63,11 @@ def main(config: str | dict):
     loss_function = config["loss_function"]
 
     model = load_predict_model(model_name, optimizer, loss_function, weights)
-
+    print(model.summary())
     # load image
     # local file as placeholder
     # img = "data/patch_pt9097_nonforest.tif"
-    img = "data_qa_old_caf\patch_pt0_nonforest.tif"
+    img = "data\\data_qa_old_caf\\patch_pt0_nonforest.tif"
     with rio.open(img) as dst:
         data = dst.read() / 10_000
         profile = dst.profile