added keras estimator wrapper

image_prediction/estimator/estimators/keras.py

@@ -0,0 +1,11 @@
+import numpy as np
+
+from image_prediction.estimator.estimator import Estimator
+
+
+class KerasEstimator(Estimator):
+    def __init__(self, estimator):
+        super().__init__(estimator)
+
+    def predict(self, batch: np.array):
+        self.estimator.predict(batch)

image_prediction/estimator/estimators/mock.py

@@ -1,10 +1,16 @@
 from image_prediction.estimator.estimator import Estimator
 
 
+class DummyEstimator:
+    @staticmethod
+    def predict(_):
+        return True
+
+
 class EstimatorMock(Estimator):
 
-    def __init__(self, estimator=lambda x: x):
+    def __init__(self, estimator):
         super().__init__(estimator=estimator)
 
     def predict(self, batch):
-        return self.estimator(batch)
+        return self.estimator.predict(batch)

image_prediction/service_estimator/service_estimator.py

@@ -1,6 +1,9 @@
 from typing import Mapping, List
 
 from image_prediction.estimator.adapter.adapter import EstimatorAdapter
+from image_prediction.utils import get_logger
+
+logger = get_logger()
 
 
 class ServiceEstimator:
@@ -9,4 +12,9 @@ class ServiceEstimator:
         self.__classes = classes
 
     def predict(self, batch) -> List[str]:
+
+        if batch.shape[0] == 0:
+            logger.warning("ServiceEstimator received empty batch")
+            return []
+
         return [self.__classes[numeric_label] for numeric_label in self.__estimator_adapter.predict(batch)]

test/unit_tests/service_estimator_test.py

@@ -3,8 +3,9 @@ import logging
 import numpy as np
 import pytest
 
-from image_prediction.estimator.estimators.mock import EstimatorMock
 from image_prediction.estimator.adapter.patch import EstimatorAdapterPatch
+from image_prediction.estimator.estimators.keras import KerasEstimator
+from image_prediction.estimator.estimators.mock import EstimatorMock, DummyEstimator
 from image_prediction.service_estimator.service_estimator import ServiceEstimator
 from image_prediction.utils import get_logger
 
@@ -13,9 +14,33 @@ logger.setLevel(logging.DEBUG)
 
 
 @pytest.fixture(scope="session")
-def estimator(estimator_type):
+def input_size():
+    return 10, 15
+
+
+@pytest.fixture(scope="session")
+def keras_model(input_size):
+
+    import os
+    os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
+
+    from tensorflow import keras
+
+    inputs = keras.Input(shape=input_size)
+    dense = keras.layers.Dense(64, activation="relu")
+    outputs = keras.layers.Dense(10)(dense(inputs))
+    model = keras.Model(inputs=inputs, outputs=outputs)
+    model.compile()
+
+    return model
+
+
+@pytest.fixture(scope="session")
+def estimator(estimator_type, keras_model):
     if estimator_type == "mock":
-        return EstimatorMock()
+        return EstimatorMock(DummyEstimator())
+    if estimator_type == "keras":
+        return KerasEstimator(keras_model)
 
 
 @pytest.fixture(scope="session")
@@ -26,8 +51,8 @@ def estimator_adapter(output_batch, estimator):
 
 
 @pytest.fixture(scope="session")
-def input_batch(batch_size, classes):
+def input_batch(batch_size, classes, input_size):
-    return np.random.normal(size=(batch_size, 10, 15))
+    return np.random.normal(size=(batch_size, *input_size))
 
 
 @pytest.fixture(scope="session")
@@ -54,7 +79,7 @@ def service_estimator(estimator_adapter, classes):
     return ServiceEstimator(estimator_adapter, classes)
 
 
-@pytest.mark.parametrize("estimator_type", ["mock"], scope="session")
+@pytest.mark.parametrize("estimator_type", ["mock", "keras"], scope="session")
 @pytest.mark.parametrize("batch_size", [0, 1, 2, 16, 32, 64], scope="session")
 def test_predict(service_estimator, input_batch, expected_predictions):
     predictions = service_estimator.predict(input_batch)