refactoring: estimator + model

image_prediction/estimator/mock.py

@@ -0,0 +1,14 @@
+class EstimatorMock:
+    def __init__(self):
+        self.__output_batch = None
+
+    @property
+    def output_batch(self):
+        return self.__output_batch
+
+    @output_batch.setter
+    def output_batch(self, output_batch):
+        self.__output_batch = output_batch
+
+    def predict(self, batch):
+        return self.__output_batch

image_prediction/model/mock.py

@@ -0,0 +1,6 @@
+from image_prediction.model.model import Model
+
+
+class ModelMock(Model):
+    def __init__(self, estimator):
+        super().__init__(estimator=estimator)

image_prediction/model/model.py

@@ -0,0 +1,13 @@
+import abc
+
+
+class Model(abc.ABC):
+    def __init__(self, estimator):
+        self.__estimator = estimator
+
+    @property
+    def estimator(self):
+        return self.__estimator
+
+    def predict(self, batch):
+        return self.estimator.predict(batch)

test/conftest.py

@@ -1,70 +0,0 @@
-import os.path
-
-import pytest
-
-from image_prediction.predictor import Predictor
-
-
-@pytest.fixture
-def predictions():
-    return [
-        {
-            "class": "signature",
-            "probabilities": {
-                "signature": 1.0,
-                "logo": 9.150285377746546e-19,
-                "other": 4.374506412383356e-19,
-                "formula": 3.582569597002796e-24,
-            },
-        }
-    ]
-
-
-@pytest.fixture
-def metadata():
-    return [
-        {
-            "page_height": 612.0,
-            "page_width": 792.0,
-            "height": 61.049999999999955,
-            "width": 139.35000000000002,
-            "page_idx": 8,
-            "x1": 63.5,
-            "x2": 202.85000000000002,
-            "y1": 472.0,
-            "y2": 533.05,
-        }
-    ]
-
-
-@pytest.fixture
-def response():
-    return [
-        {
-            "classification": {
-                "label": "signature",
-                "probabilities": {"formula": 0.0, "logo": 0.0, "other": 0.0, "signature": 1.0},
-            },
-            "filters": {
-                "allPassed": True,
-                "geometry": {
-                    "imageFormat": {"quotient": 2.282555282555285, "tooTall": False, "tooWide": False},
-                    "imageSize": {"quotient": 0.13248234868245012, "tooLarge": False, "tooSmall": False},
-                },
-                "probability": {"unconfident": False},
-            },
-            "geometry": {"height": 61.049999999999955, "width": 139.35000000000002},
-            "position": {"pageNumber": 9, "x1": 63.5, "x2": 202.85000000000002, "y1": 472.0, "y2": 533.05},
-        }
-    ]
-
-
-@pytest.fixture
-def predictor():
-    return Predictor()
-
-
-@pytest.fixture
-def test_pdf():
-    with open("./test/test_data/f2dc689ca794fccb8cd38b95f2bf6ba9.pdf", "rb") as f:
-        return f.read()

test/unit_tests/model_test.py

@@ -0,0 +1,36 @@
+import numpy as np
+import pytest
+
+from image_prediction.estimator.mock import EstimatorMock
+from image_prediction.model.mock import ModelMock
+
+
+@pytest.fixture(scope="session")
+def estimator():
+    return EstimatorMock()
+
+
+@pytest.fixture(scope="session")
+def batches(batch_size):
+    input_batch = np.random.normal(size=(batch_size, 10, 15))
+    output_batch = np.random.randint(low=42, high=43, size=(batch_size, 10, 15))
+    return input_batch, output_batch
+
+
+@pytest.fixture(scope="session")
+def classes():
+    return ["A", "B", "C"]
+
+
+@pytest.fixture(scope="session")
+def model(model_type, estimator):
+    if model_type == "mock":
+        return ModelMock(estimator)
+
+
+@pytest.mark.parametrize("model_type", ["mock"], scope="session")
+@pytest.mark.parametrize("batch_size", [0, 1, 2, 16, 32, 64], scope="session")
+def test_predict(model, batches):
+    input_batch, output_batch = batches
+    model.estimator.output_batch = output_batch
+    assert np.all(np.equal(model.predict(input_batch), output_batch))

test/unit_tests/test_predictor.py

@@ -1,26 +0,0 @@
-def test_predict_pdf_works(predictor, test_pdf):
-    # FIXME ugly test since there are '\n's in the dict with unknown heritage
-    predictions, metadata = predictor.predict_pdf(test_pdf)
-    predictions = [p for p in predictions][0]
-    assert predictions["class"] == "formula"
-    probabilities = predictions["probabilities"]
-    # Floating point precision problem for output so test only that keys exist not the values
-    assert all(key in probabilities for key in ("formula", "other", "signature", "logo"))
-    metadata = list(metadata)
-    metadata = dict(**metadata[0])
-    metadata.pop("document_filename")  # temp filename cannot be tested
-    assert metadata == {
-        "px_width": 389.0,
-        "px_height": 389.0,
-        "width": 194.49999000000003,
-        "height": 194.49998999999997,
-        "x1": 320.861,
-        "x2": 515.36099,
-        "y1": 347.699,
-        "y2": 542.19899,
-        "page_width": 595.2800000000001,
-        "page_height": 841.89,
-        "page_rotation": 0,
-        "page_idx": 1,
-        "n_pages": 3,
-    }

test/unit_tests/test_response.py

@@ -1,5 +0,0 @@
-from image_prediction.response import build_response
-
-
-def test_build_response_returns_valid_response(predictions, metadata, response):
-    assert build_response(predictions, metadata) == response