added image-tensor conversion logic

image_prediction/estimator/estimator.py

@@ -16,7 +16,9 @@ class Estimator:
     def predict(self, batch: np.array) -> List[str]:
 
         if batch.shape[0] == 0:
-            logger.warning("Estimator received empty batch.")
             return []
 
         return [self.__classes[numeric_label] for numeric_label in self.__estimator_adapter.predict(batch)]
+
+    def __call__(self, batch: np.array) -> List[str]:
+        return self.predict(batch)

image_prediction/estimator/preprocessor/preprocessor.py

@@ -1,9 +1,12 @@
 from image_prediction.estimator.adapter.adapter import EstimatorAdapter
 
 
-class EstimatorPreprocessor:
-    def __init__(self, estimator: EstimatorAdapter):
-        self.estimator = estimator
+class Preprocessor:
+    def __init__(self):
+        pass
 
-    def predict(self, batch):
-        return self.estimator.predict(batch)
+    # def preprocess(self, batch):
+    #     return (map(image_to_normalized_tensor, batch))
+
+    def __call__(self, batch):
+        return self.preprocess(batch)

image_prediction/estimator/preprocessor/utils.py

@@ -0,0 +1,10 @@
+import numpy as np
+from PIL.Image import Image
+
+
+def image_to_normalized_tensor(image: Image) -> np.ndarray:
+    return np.array(image) / 255
+
+
+def images_to_batch_tensor(images) -> np.ndarray:
+    return np.array(list(map(image_to_normalized_tensor, images)))

image_prediction/utils.py

@@ -1,8 +1,11 @@
 import logging
 import tempfile
 from contextlib import contextmanager
+from itertools import takewhile, starmap, islice, repeat
+from operator import truth
 
 from image_prediction.config import CONFIG
+from redai.utils import export
 
 
 @contextmanager
@@ -66,3 +69,8 @@ def show_banner():
     logger.addHandler(handler)
 
     logger.info(banner)
+
+
+@export
+def chunk_iterable(iterable, chunk_size):
+    return takewhile(truth, map(tuple, starmap(islice, repeat((iter(iterable), chunk_size)))))

test/unit_tests/conftest.py

@@ -47,17 +47,26 @@ def keras_model(input_size):
     import os
     os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
 
-    from tensorflow import keras
+    import tensorflow as tf
 
-    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)
+    tf.keras.backend.set_image_data_format('channels_last')
+
+    inputs = tf.keras.Input(shape=input_size)
+    conv = tf.keras.layers.Conv2D(3, 3)
+    dense = tf.keras.layers.Dense(10, activation="relu")
+
+    outputs = tf.keras.layers.Dense(10)(dense(conv(inputs)))
+    model = tf.keras.Model(inputs=inputs, outputs=outputs)
     model.compile()
 
     return model
 
 
+@pytest.fixture
+def batch_size():
+    return 4
+
+
 @pytest.fixture
 def images(input_batch):
     return list(map(array_to_image, input_batch))
@@ -71,12 +80,12 @@ def input_batch(batch_size, input_size):
 def array_to_image(array):
     assert np.all(array <= 1)
     assert np.all(array >= 0)
-    return Image.fromarray(np.uint8(array * 255))
+    return Image.fromarray(np.uint8(array * 255), mode="RGB")
 
 
 @pytest.fixture
-def input_size(width=10, height=15):
-    return width, height
+def input_size(depth=3, width=10, height=15):
+    return width, height, depth
 
 
 @pytest.fixture

test/unit_tests/preprocessor_test.py

@@ -0,0 +1,25 @@
+import logging
+
+import numpy as np
+from PIL import Image
+
+from image_prediction.estimator.preprocessor.utils import image_to_normalized_tensor, images_to_batch_tensor
+from image_prediction.utils import get_logger
+
+
+
+
+def test_image_to_tensor(images):
+
+    def inner(image):
+        tensor = image_to_normalized_tensor(image)
+        image_re = Image.fromarray(np.uint8(tensor * 255), mode="RGB")
+        return image == image_re and tensor.ndim == 3
+
+    assert all(map(inner, images))
+
+
+def test_images_to_batch_tensor(images):
+    tensor = images_to_batch_tensor(images)
+    assert isinstance(tensor, np.ndarray)
+    assert tensor.ndim == 4

test/unit_tests/service_estimator_test.py

@@ -13,3 +13,15 @@ logger.setLevel(logging.DEBUG)
 def test_predict(service_estimator, input_batch, expected_predictions):
     predictions = service_estimator.predict(input_batch)
     assert predictions == expected_predictions
+
+
+def test_batch_format(input_batch):
+
+    def channels_are_last(input_batch):
+        return input_batch.shape[-1] == 3
+
+    def is_fourth_order_tensor(input_batch):
+        return input_batch.ndim == 4
+
+    assert channels_are_last(input_batch)
+    assert is_fourth_order_tensor(input_batch)