108 lines
2.7 KiB
Python
108 lines
2.7 KiB
Python
import numpy as np
|
|
import pytest
|
|
from PIL import Image
|
|
|
|
from image_prediction.estimator.adapter.adapters.keras import KerasEstimatorAdapter
|
|
from image_prediction.estimator.adapter.adapters.mock import DummyEstimator, EstimatorAdapterMock
|
|
from image_prediction.estimator.estimator import Estimator
|
|
from image_prediction.exceptions import UnknownEstimatorAdapter
|
|
from image_prediction.predictor.predictor import Predictor
|
|
|
|
|
|
@pytest.fixture
|
|
def predictor(estimator):
|
|
return Predictor(estimator)
|
|
|
|
|
|
@pytest.fixture
|
|
def estimator(estimator_adapter, classes):
|
|
service_estimator = Estimator(estimator_adapter, classes)
|
|
return service_estimator
|
|
|
|
|
|
@pytest.fixture
|
|
def estimator_adapter(estimator_type, keras_model, output_batch, monkeypatch):
|
|
if estimator_type == "mock":
|
|
estimator = EstimatorAdapterMock(DummyEstimator())
|
|
elif estimator_type == "keras":
|
|
estimator = KerasEstimatorAdapter(keras_model)
|
|
else:
|
|
raise UnknownEstimatorAdapter(f"No adapter for estimator type {estimator_type} was specified.")
|
|
|
|
def mock_predict(batch):
|
|
_predict(batch)
|
|
return output_batch
|
|
|
|
_predict = estimator.predict
|
|
monkeypatch.setattr(estimator, "predict", mock_predict)
|
|
|
|
return estimator
|
|
|
|
|
|
@pytest.fixture
|
|
def keras_model(input_size):
|
|
import warnings
|
|
warnings.filterwarnings("ignore", category=DeprecationWarning)
|
|
|
|
import os
|
|
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
|
|
|
|
import tensorflow as tf
|
|
|
|
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))
|
|
|
|
|
|
@pytest.fixture
|
|
def input_batch(batch_size, input_size):
|
|
return np.random.random_sample(size=(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), mode="RGB")
|
|
|
|
|
|
@pytest.fixture
|
|
def input_size(depth=3, width=10, height=15):
|
|
return width, height, depth
|
|
|
|
|
|
@pytest.fixture
|
|
def expected_predictions(output_batch, classes):
|
|
return map_labels(output_batch, classes)
|
|
|
|
|
|
@pytest.fixture
|
|
def output_batch(batch_size, classes):
|
|
return np.random.randint(low=0, high=len(classes), size=batch_size)
|
|
|
|
|
|
@pytest.fixture
|
|
def classes():
|
|
return ["A", "B", "C"]
|
|
|
|
|
|
def map_labels(numeric_labels, classes):
|
|
return [classes[nl] for nl in numeric_labels]
|