[test] change assertion type in test_normalize_array (helpers), fix predictors test cases

tests/events/samexporter_predict.json

@@ -1,18 +1,18 @@
 {
   "europe": {
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   }
 }

tests/io/test_raster_helpers.py

@@ -133,7 +133,7 @@ class Test(unittest.TestCase):
                         count_abs_diff += 1
                     if check_type == "float":
                         cls.assertAlmostEqual(el00, el01, places=places)
-                    cls.assertTrue(ndiff < check_ndiff)
+                    cls.assertLess(ndiff, check_ndiff)
             print("count_abs_diff:", count_abs_diff)
 
         normalized_array = raster_helpers.normalize_array(z)
@@ -174,7 +174,6 @@ class Test(unittest.TestCase):
     def test_normalize_array_exception_error_mocked(self, nanmax_mocked, nanmin_mocked):
         nanmax_mocked.return_value = 100
         nanmin_mocked.return_value = np.NaN
-
         with self.assertRaises(ValueError):
             try:
                 raster_helpers.normalize_array(

tests/prediction_api/test_predictors.py

@@ -3,51 +3,61 @@ import unittest
 from unittest.mock import patch
 
 import numpy as np
+import shapely
+from affine import Affine
 
 from samgis.prediction_api import predictors
-from samgis.prediction_api.predictors import get_raster_inference, samexporter_predict
+from samgis.prediction_api.predictors import samexporter_predict
 from tests import TEST_EVENTS_FOLDER
 
 
 class TestPredictors(unittest.TestCase):
-    @patch.object(predictors, "SegmentAnythingONNX")
-    def test_get_raster_inference(self, segment_anything_onnx_mocked):
+    @patch.object(predictors, "download_extent")
+    def test_get_raster_inference(self, download_extent_mocked):
         name_fn = "samexporter_predict"
 
         with open(TEST_EVENTS_FOLDER / f"{name_fn}.json") as tst_json:
             inputs_outputs = json.load(tst_json)
             for k, input_output in inputs_outputs.items():
-                model_mocked = segment_anything_onnx_mocked()
-
+                input_payload = input_output["input"]
+                prompt = input_payload["prompt"]
+                model_name = input_payload["model_name"]
+                bbox = input_payload["bbox"]
+                zoom = input_payload["zoom"]
+                print(f"k:{k}.")
                 img = np.load(TEST_EVENTS_FOLDER / f"{name_fn}" / k / "img.npy")
-                inference_out = np.load(TEST_EVENTS_FOLDER / f"{name_fn}" / k / "inference_out.npy")
-                mask = np.load(TEST_EVENTS_FOLDER / f"{name_fn}" / k / "mask.npy")
-                prompt = input_output["input"]["prompt"]
-                model_name = input_output["input"]["model_name"]
+                affine_transform = Affine.from_gdal(*input_payload["matrix"])
+                download_extent_mocked.return_value = img, affine_transform
+                expected_output = input_output["output"]
 
-                model_mocked.embed.return_value = np.array(img)
-                model_mocked.embed.side_effect = None
-                model_mocked.predict_masks.return_value = inference_out
-                model_mocked.predict_masks.side_effect = None
-                print(f"k:{k}.")
-                output_mask, len_inference_out = get_raster_inference(
-                    img=img,
-                    prompt=prompt,
-                    models_instance=model_mocked,
-                    model_name=model_name
+                output_dict = samexporter_predict(
+                    bbox,
+                    prompt,
+                    zoom,
+                    model_name
                 )
-                assert np.array_equal(output_mask, mask)
-                assert len_inference_out == input_output["output"]["n_predictions"]
+                len_inference_out = output_dict["n_predictions"]
+                geojson = output_dict["geojson"]
+                n_shapes_geojson = output_dict["n_shapes_geojson"]
+
+                assert isinstance(geojson, str)
+                assert isinstance(n_shapes_geojson, int)
+                assert len_inference_out == expected_output["n_predictions"]
+
+                output_geojson = shapely.from_geojson(geojson)
+                print("output_geojson::{}.".format(output_geojson))
+                assert isinstance(output_geojson, shapely.GeometryCollection)
+                assert len(output_geojson.geoms) > 0
 
     @patch.object(predictors, "get_raster_inference_with_embedding_from_dict")
-    @patch.object(predictors, "SegmentAnythingONNX")
+    @patch.object(predictors, "SegmentAnythingONNX2")
     @patch.object(predictors, "download_extent")
     @patch.object(predictors, "get_vectorized_raster_as_geojson")
-    def test_samexporter_predict(
+    def test_samexporter_predict_mocked(
             self,
             get_vectorized_raster_as_geojson_mocked,
             download_extent_mocked,
-            segment_anything_onnx_mocked,
+            segment_anything_onnx2_mocked,
             get_raster_inference_with_embedding_from_dict_mocked
     ):
         """
@@ -58,11 +68,14 @@ class TestPredictors(unittest.TestCase):
         get_vectorized_raster_as_geojson(mask, matrix)
         """
         aff = 1, 2, 3, 4, 5, 6
-        segment_anything_onnx_mocked.return_value = "SegmentAnythingONNX_instance"
-        download_extent_mocked.return_value = np.zeros((10, 10)), aff
-        get_raster_inference_with_embedding_from_dict_mocked.return_value = np.ones((10, 10)), 1
+        segment_anything_onnx2_mocked.return_value = "SegmentAnythingONNX2_instance"
+        input_downloaded = np.arange(0, 300, 1).reshape((10, 10, 3))
+        download_extent_mocked.return_value = input_downloaded, aff
+        mask_output = np.zeros((10, 10))
+        mask_output[4:4, 6:6] = 255.0
+        get_raster_inference_with_embedding_from_dict_mocked.return_value = mask_output, 1
         get_vectorized_raster_as_geojson_mocked.return_value = {"geojson": "{}", "n_shapes_geojson": 2}
         output = samexporter_predict(
-            bbox=[[1, 2], [3, 4]], prompt=[{}], zoom=10, model_name="fastsam", source_name="localtest"
+            bbox=[[1, 2], [3, 4]], prompt=[{}], zoom=10, model_name="mobile_sam", source_name="localtest"
         )
         assert output == {"n_predictions": 1, "geojson": "{}", "n_shapes_geojson": 2}