def test_input_tensor():
hp = hp_module.HyperParameters()
inputs = tf.keras.Input(shape=(256, 256, 3))
hypermodel = efficientnet.HyperEfficientNet(input_tensor=inputs,
classes=10)
model = hypermodel.build(hp)
assert model.inputs == [inputs]
def test_augmentation_param_fixed_model():
hp = hp_module.HyperParameters()
aug_model = tf.keras.Sequential(name="aug")
hypermodel = efficientnet.HyperEfficientNet(input_shape=(32, 32, 3),
classes=10,
augmentation_model=aug_model)
model = hypermodel.build(hp)
assert model.layers[1].name == "aug"
def test_tf_version_too_low_error():
pp_module = efficientnet.preprocessing
efficientnet.preprocessing = None
with pytest.raises(ImportError, match="HyperEfficientNet requires"):
efficientnet.HyperEfficientNet()
efficientnet.preprocessing = pp_module
def test_hyperparameter_override():
hp = hp_module.HyperParameters()
hp.Choice("version", ["B1"])
hp.Fixed("top_dropout_rate", 0.5)
hypermodel = efficientnet.HyperEfficientNet(input_shape=(256, 256, 3),
classes=10)
hypermodel.build(hp)
assert hp.get("version") == "B1"
assert hp.get("top_dropout_rate") == 0.5
def test_hyperparameter_existence_and_defaults():
hp = hp_module.HyperParameters()
hypermodel = efficientnet.HyperEfficientNet(input_shape=(224, 224, 3),
classes=10)
hypermodel.build(hp)
assert hp.get("version") == "B0"
assert hp.get("top_dropout_rate") == 0.2
assert hp.get("learning_rate") == 0.01
assert hp.get("pooling") == "avg"
def test_model_construction(version):
hp = hp_module.HyperParameters()
hp.Choice("version", [version])
hypermodel = efficientnet.HyperEfficientNet(input_shape=(32, 32, 3), classes=10)
model = hypermodel.build(hp)
assert hp.values["version"] == version
assert model.layers
assert model.name == "EfficientNet"
assert model.output_shape == (None, 10)
model.train_on_batch(np.ones((1, 32, 32, 3)), np.ones((1, 10)))
out = model.predict(np.ones((1, 32, 32, 3)))
assert out.shape == (1, 10)
def test_augmentation_param_hyper_model():
class HyperAug(hm_module.HyperModel):
def build(self, hp):
model = tf.keras.Sequential(name="aug")
scaling_factor = hp.Choice("scaling_factor", [1])
model.add(tf.keras.layers.Lambda(lambda x: x * scaling_factor))
return model
hp = hp_module.HyperParameters()
aug_hm = HyperAug()
hypermodel = efficientnet.HyperEfficientNet(input_shape=(32, 32, 3),
classes=10,
augmentation_model=aug_hm)
model = hypermodel.build(hp)
assert model.layers[1].name == "aug"
assert hp.values["scaling_factor"] == 1
def test_augmentation_param_invalid_input():
with pytest.raises(ValueError):
efficientnet.HyperEfficientNet(input_shape=(32, 32, 3),
classes=10,
augmentation_model=0)