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test_model_class.py
test_model_class.py 11.43 KiB
import keras
import pytest
from src.model_modules.model_class import AbstractModelClass
from src.model_modules.model_class import MyPaperModel
class Paddings:
allowed_paddings = {"pad1": 34, "another_pad": True}
class AbstractModelSubClass(AbstractModelClass):
def __init__(self):
super().__init__(shape_inputs=(12, 1, 2), shape_outputs=3)
self.test_attr = "testAttr"
class TestAbstractModelClass:
@pytest.fixture
def amc(self):
return AbstractModelClass(shape_inputs=(14, 1, 2), shape_outputs=(3,))
@pytest.fixture
def amsc(self):
return AbstractModelSubClass()
def test_init(self, amc):
assert amc.model is None
# assert amc.loss is None
assert amc.model_name == "AbstractModelClass"
assert amc.custom_objects == {}
assert amc.shape_inputs == (14, 1, 2)
assert amc.shape_outputs == 3
def test_model_property(self, amc):
amc.model = keras.Model()
assert isinstance(amc.model, keras.Model) is True
# def test_loss_property(self, amc):
# amc.loss = keras.losses.mean_absolute_error
# assert amc.loss == keras.losses.mean_absolute_error
def test_compile_options_setter_all_empty(self, amc):
amc.compile_options = None
assert amc.compile_options == {'optimizer': None,
'loss': None,
'metrics': None,
'loss_weights': None,
'sample_weight_mode': None,
'weighted_metrics': None,
'target_tensors': None
}
def test_compile_options_setter_as_dict(self, amc):
amc.compile_options = {"optimizer": keras.optimizers.SGD(),
"loss": keras.losses.mean_absolute_error,
"metrics": ["mse", "mae"]}
assert isinstance(amc.compile_options["optimizer"], keras.optimizers.SGD)
assert amc.compile_options["loss"] == keras.losses.mean_absolute_error
assert amc.compile_options["metrics"] == ["mse", "mae"]
assert amc.compile_options["loss_weights"] is None
assert amc.compile_options["sample_weight_mode"] is None
assert amc.compile_options["target_tensors"] is None
assert amc.compile_options["weighted_metrics"] is None
def test_compile_options_setter_as_attr(self, amc):
amc.optimizer = keras.optimizers.SGD()
amc.loss = keras.losses.mean_absolute_error
amc.compile_options = None # This line has to be called!
# optimizer check
assert isinstance(amc.optimizer, keras.optimizers.SGD)
assert isinstance(amc.compile_options["optimizer"], keras.optimizers.SGD)
# loss check
assert amc.loss == keras.losses.mean_absolute_error
assert amc.compile_options["loss"] == keras.losses.mean_absolute_error
# check rest (all None as not set)
assert amc.compile_options["metrics"] is None
assert amc.compile_options["loss_weights"] is None
assert amc.compile_options["sample_weight_mode"] is None
assert amc.compile_options["target_tensors"] is None
assert amc.compile_options["weighted_metrics"] is None
def test_compile_options_setter_as_mix_attr_dict_no_duplicates(self, amc):
amc.optimizer = keras.optimizers.SGD()
amc.compile_options = {"loss": keras.losses.mean_absolute_error,
"loss_weights": [0.2, 0.8]}
# check setting by attribute
assert isinstance(amc.optimizer, keras.optimizers.SGD)
assert isinstance(amc.compile_options["optimizer"], keras.optimizers.SGD)
# check setting by dict
assert amc.compile_options["loss"] == keras.losses.mean_absolute_error
assert amc.compile_options["loss_weights"] == [0.2, 0.8]
# check rest (all None as not set)
assert amc.compile_options["metrics"] is None
assert amc.compile_options["sample_weight_mode"] is None
assert amc.compile_options["target_tensors"] is None
assert amc.compile_options["weighted_metrics"] is None
def test_compile_options_setter_as_mix_attr_dict_valid_duplicates_optimizer(self, amc):
amc.optimizer = keras.optimizers.SGD()
amc.metrics = ['mse']
amc.compile_options = {"optimizer": keras.optimizers.SGD(),
"loss": keras.losses.mean_absolute_error}
# check duplicate (attr and dic)
assert isinstance(amc.optimizer, keras.optimizers.SGD)
assert isinstance(amc.compile_options["optimizer"], keras.optimizers.SGD)
# check setting by dict
assert amc.compile_options["loss"] == keras.losses.mean_absolute_error
# check setting by attr
assert amc.metrics == ['mse']
assert amc.compile_options["metrics"] == ['mse']
# check rest (all None as not set)
assert amc.compile_options["loss_weights"] is None
assert amc.compile_options["sample_weight_mode"] is None
assert amc.compile_options["target_tensors"] is None
assert amc.compile_options["weighted_metrics"] is None
def test_compile_options_setter_as_mix_attr_dict_valid_duplicates_none_optimizer(self, amc):
amc.optimizer = keras.optimizers.SGD()
amc.metrics = ['mse']
amc.compile_options = {"metrics": ['mse'],
"loss": keras.losses.mean_absolute_error}
# check duplicate (attr and dic)
assert amc.metrics == ['mse']
assert amc.compile_options["metrics"] == ['mse']
# check setting by dict
assert amc.compile_options["loss"] == keras.losses.mean_absolute_error
# check setting by attr
assert isinstance(amc.optimizer, keras.optimizers.SGD)
assert isinstance(amc.compile_options["optimizer"], keras.optimizers.SGD)
# check rest (all None as not set)
assert amc.compile_options["loss_weights"] is None
assert amc.compile_options["sample_weight_mode"] is None
assert amc.compile_options["target_tensors"] is None
assert amc.compile_options["weighted_metrics"] is None
def test_compile_options_property_type_error(self, amc):
with pytest.raises(TypeError) as einfo:
amc.compile_options = 'hello world'
assert "`compile_options' must be `dict' or `None', but is <class 'str'>." in str(einfo.value)
def test_compile_options_setter_as_mix_attr_dict_invalid_duplicates_other_optimizer(self, amc):
amc.optimizer = keras.optimizers.SGD()
with pytest.raises(ValueError) as einfo:
amc.compile_options = {"optimizer": keras.optimizers.Adam()}
assert "Got different values or arguments for same argument: self.optimizer=<class" \
" 'keras.optimizers.SGD'> and 'optimizer': <class 'keras.optimizers.Adam'>" in str(einfo.value)
def test_compile_options_setter_as_mix_attr_dict_invalid_duplicates_same_optimizer_other_args(self, amc):
amc.optimizer = keras.optimizers.SGD(lr=0.1)
with pytest.raises(ValueError) as einfo:
amc.compile_options = {"optimizer": keras.optimizers.SGD(lr=0.001)}
assert "Got different values or arguments for same argument: self.optimizer=<class" \
" 'keras.optimizers.SGD'> and 'optimizer': <class 'keras.optimizers.SGD'>" in str(einfo.value)
def test_compile_options_setter_as_dict_invalid_keys(self, amc):
with pytest.raises(ValueError) as einfo:
amc.compile_options = {"optimizer": keras.optimizers.SGD(), "InvalidKeyword": [1, 2, 3]}
assert "Got invalid key for compile_options. dict_keys(['optimizer', 'InvalidKeyword'])" in str(einfo.value)
def test_compare_keras_optimizers_equal(self, amc):
assert amc._AbstractModelClass__compare_keras_optimizers(keras.optimizers.SGD(), keras.optimizers.SGD()) is True
def test_compare_keras_optimizers_no_optimizer(self, amc):
assert amc._AbstractModelClass__compare_keras_optimizers('NoOptimizer', keras.optimizers.SGD()) is False
def test_compare_keras_optimizers_other_parameters_run_sess(self, amc):
assert amc._AbstractModelClass__compare_keras_optimizers(keras.optimizers.SGD(lr=0.1),
keras.optimizers.SGD(lr=0.01)) is False
def test_compare_keras_optimizers_other_parameters_none_sess(self, amc):
assert amc._AbstractModelClass__compare_keras_optimizers(keras.optimizers.SGD(decay=1),
keras.optimizers.SGD(decay=0.01)) is False
def test_getattr(self, amc):
amc.model = keras.Model()
assert hasattr(amc, "compile") is True
assert hasattr(amc.model, "compile") is True
assert amc.compile == amc.model.compile
def test_get_settings(self, amc, amsc):
assert amc.get_settings() == {"model_name": "AbstractModelClass", "shape_inputs": (14, 1, 2),
"shape_outputs": 3}
assert amsc.get_settings() == {"test_attr": "testAttr", "model_name": "AbstractModelSubClass",
"shape_inputs": (12, 1, 2), "shape_outputs": 3}
def test_custom_objects(self, amc):
amc.custom_objects = {"Test": 123}
assert amc.custom_objects == {"Test": 123}
def test_set_custom_objects(self, amc):
amc.set_custom_objects(Test=22, minor_param="minor")
assert amc.custom_objects == {"Test": 22, "minor_param": "minor"}
amc.set_custom_objects(Test=2, minor_param1="minor1")
assert amc.custom_objects == {"Test": 2, "minor_param1": "minor1"}
paddings = Paddings()
amc.set_custom_objects(Test=1, Padding2D=paddings)
assert amc.custom_objects == {"Test": 1, "Padding2D": paddings, "pad1": 34, "another_pad": True}
class TestMyPaperModel:
@pytest.fixture
def mpm(self):
return MyPaperModel(shape_inputs=[(7, 1, 9)], shape_outputs=[(4,)])
def test_init(self, mpm):
# check if loss number of loss functions fit to model outputs
# same loss fkts. for all tails or different fkts. per tail
if isinstance(mpm.model.output_shape, list):
assert (callable(mpm.compile_options["loss"]) or (len(mpm.compile_options["loss"]) == 1)) or (
len(mpm.compile_options["loss"]) == len(mpm.model.output_shape))
elif isinstance(mpm.model.output_shape, tuple):
assert callable(mpm.compile_options["loss"]) or (len(mpm.compile_options["loss"]) == 1)
def test_set_model(self, mpm):
assert isinstance(mpm.model, keras.Model)
assert mpm.model.layers[0].output_shape == (None, 7, 1, 9)
# check output dimensions
if isinstance(mpm.model.output_shape, tuple):
assert mpm.model.output_shape == (None, 4)
elif isinstance(mpm.model.output_shape, list):
for tail_shape in mpm.model.output_shape:
assert tail_shape == (None, 4)
else:
raise TypeError(f"Type of model.output_shape as to be a tuple (one tail)"
f" or a list of tuples (multiple tails). Received: {type(mpm.model.output_shape)}")
# def test_set_loss(self, mpm):
# assert callable(mpm.loss) or (len(mpm.loss) > 0)
def test_set_compile_options(self, mpm):
assert callable(mpm.compile_options["loss"]) or (len(mpm.compile_options["loss"]) > 0)