99 lines
2.5 KiB
Python
99 lines
2.5 KiB
Python
import numpy as np
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import pytest
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from PIL import Image
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from image_prediction.estimator.estimators.keras import KerasEstimator
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from image_prediction.estimator.estimators.mock import EstimatorMock, DummyEstimator
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from image_prediction.exceptions import UnknownEstimatorAdapter
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from image_prediction.predictor.predictor import Predictor
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from image_prediction.service_estimator.service_estimator import ServiceEstimator
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@pytest.fixture
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def predictor(service_estimator):
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return Predictor(service_estimator)
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@pytest.fixture
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def service_estimator(estimator, classes):
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service_estimator = ServiceEstimator(estimator, classes)
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return service_estimator
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@pytest.fixture
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def estimator(estimator_type, keras_model, output_batch, monkeypatch):
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if estimator_type == "mock":
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estimator = EstimatorMock(DummyEstimator())
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elif estimator_type == "keras":
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estimator = KerasEstimator(keras_model)
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else:
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raise UnknownEstimatorAdapter(f"No adapter for estimator type {estimator_type} was specified.")
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def mock_predict(batch):
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_predict(batch)
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return output_batch
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_predict = estimator.predict
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monkeypatch.setattr(estimator, "predict", mock_predict)
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return estimator
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@pytest.fixture
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def keras_model(input_size):
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import warnings
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warnings.filterwarnings("ignore", category=DeprecationWarning)
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import os
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os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
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from tensorflow import keras
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inputs = keras.Input(shape=input_size)
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dense = keras.layers.Dense(64, activation="relu")
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outputs = keras.layers.Dense(10)(dense(inputs))
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model = keras.Model(inputs=inputs, outputs=outputs)
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model.compile()
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return model
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@pytest.fixture
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def images(input_batch):
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return list(map(array_to_image, input_batch))
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@pytest.fixture
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def input_batch(batch_size, input_size):
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return np.random.random_sample(size=(batch_size, *input_size))
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def array_to_image(array):
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assert np.all(array <= 1)
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assert np.all(array >= 0)
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return Image.fromarray(np.uint8(array * 255))
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@pytest.fixture
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def input_size(width=10, height=15):
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return width, height
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@pytest.fixture
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def expected_predictions(output_batch, classes):
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return map_labels(output_batch, classes)
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@pytest.fixture
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def output_batch(batch_size, classes):
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return np.random.randint(low=0, high=len(classes), size=batch_size)
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@pytest.fixture
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def classes():
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return ["A", "B", "C"]
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def map_labels(numeric_labels, classes):
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return [classes[nl] for nl in numeric_labels]
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