fixed batching issue in prediction monkey patch by introducinbg an output generator, that yields the expected predictions

image_prediction/estimator/adapter/adapters/keras.py

@@ -8,4 +8,4 @@ class KerasEstimatorAdapter(EstimatorAdapter):
         super().__init__(estimator)
 
     def predict(self, batch: np.array):
-        self.estimator.predict(batch)
+        return self.estimator.predict(batch)

image_prediction/estimator/adapter/adapters/mock.py

@@ -3,8 +3,8 @@ from image_prediction.estimator.adapter.adapter import EstimatorAdapter
 
 class DummyEstimator:
     @staticmethod
-    def predict(_):
-        return True
+    def predict(batch):
+        return [None for _ in batch]
 
 
 class EstimatorAdapterMock(EstimatorAdapter):

image_prediction/predictor/predictor.py

@@ -15,12 +15,6 @@ class Predictor:
         self.preprocessor = preprocessor if preprocessor else BasicPreprocessor()
         self.pipe = lambda batch: self.estimator(self.preprocessor(batch))
 
-    def predict_images(self, images: List[Image], batch_size=4):
+    def predict(self, images: List[Image], batch_size=2):
         batches = chunk_iterable(images, chunk_size=batch_size)
-        batches = list(batches)
-        print(list(map(len, batches)))
-        for batch in batches:
-            print(len(batch))
-            print(self.pipe(batch))
-
         return chain(*map(self.pipe, batches))

test/unit_tests/conftest.py

@@ -1,3 +1,5 @@
+import random
+
 import numpy as np
 import pytest
 from PIL import Image
@@ -10,7 +12,7 @@ from image_prediction.predictor.predictor import Predictor
 
 
 @pytest.fixture
-def predictor(estimator):
+def predictor(estimator, monkeypatch, expected_predictions):
     return Predictor(estimator)
 
 
@@ -21,7 +23,14 @@ def estimator(estimator_adapter, classes):
 
 
 @pytest.fixture
-def estimator_adapter(estimator_type, keras_model, output_batch, monkeypatch):
+def input_output_mapper(input_batch, classes):
+    """Mocks the internal, real estimator of an EstimatorAdapter object."""
+    outputs = random.choices(range(len(classes)), k=len(input_batch))
+    return outputs
+
+
+@pytest.fixture
+def estimator_adapter(estimator_type, keras_model, output_batch_generator, monkeypatch):
     if estimator_type == "mock":
         estimator_adapter = EstimatorAdapterMock(DummyEstimator())
     elif estimator_type == "keras":
@@ -30,9 +39,9 @@ def estimator_adapter(estimator_type, keras_model, output_batch, monkeypatch):
         raise UnknownEstimatorAdapter(f"No adapter for estimator type {estimator_type} was specified.")
 
     def mock_predict(batch):
-        # assert len(batch) == len(output_batch)
-        _predict(batch)
-        return output_batch
+        # Run real predict function to test for mechanical issues, but return externally defined
+        # predictions to test the callers of the estimator adapter against the expected predictions
+        return [next(output_batch_generator) for _ in _predict(batch)]
 
     _predict = estimator_adapter.predict
     monkeypatch.setattr(estimator_adapter, "predict", mock_predict)
@@ -90,8 +99,13 @@ def expected_predictions(output_batch, classes):
 
 
 @pytest.fixture
-def output_batch(batch_size, classes):
-    return np.random.randint(low=0, high=len(classes), size=batch_size)
+def output_batch(input_batch, classes):
+    return random.choices(range(len(classes)), k=len(input_batch))
+
+
+@pytest.fixture
+def output_batch_generator(output_batch):
+    return iter(output_batch)
 
 
 @pytest.fixture

test/unit_tests/predictor_test.py

@@ -4,9 +4,10 @@ from image_prediction.utils import chunk_iterable
 
 
 @pytest.mark.parametrize("estimator_type", ["mock", "keras"])
-@pytest.mark.parametrize("batch_size", [0, 1, 2, 16, 32, 64])
+# @pytest.mark.parametrize("batch_size", [0, 1, 2, 16, 32, 64])
+@pytest.mark.parametrize("batch_size", [0, 1, 2, 4])
 def test_predict(predictor, images, expected_predictions):
-    predictions = list(predictor.predict_images(images))
+    predictions = list(predictor.predict(images))
     assert predictions == expected_predictions
 
 

test/unit_tests/preprocessor_test.py

@@ -15,7 +15,7 @@ def image_conversion_is_correct(image):
 
 @pytest.mark.parametrize("batch_size", [0, 1, 2, 16, 32, 64])
 def images_conversion_is_correct(images, tensor):
-    if not (images or tensor):
+    if not (images or tensor.size > 0):
         return True
     return all([isinstance(tensor, np.ndarray), tensor.ndim == 4, tensor.shape[0] == len(images)])