diff --git a/test/test_model_modules/test_abstract_model_class.py b/test/test_model_modules/test_abstract_model_class.py
new file mode 100644
index 0000000000000000000000000000000000000000..dfef68d550b07f824ed38e5c7809c00e5386d115
--- /dev/null
+++ b/test/test_model_modules/test_abstract_model_class.py
@@ -0,0 +1,199 @@
+import keras
+import pytest
+
+from mlair import AbstractModelClass
+
+
+class Paddings:
+ allowed_paddings = {"pad1": 34, "another_pad": True}
+
+
+class AbstractModelSubClass(AbstractModelClass):
+
+ def __init__(self):
+ super().__init__(input_shape=(12, 1, 2), output_shape=3)
+ self.test_attr = "testAttr"
+
+
+class TestAbstractModelClass:
+
+ @pytest.fixture
+ def amc(self):
+ return AbstractModelClass(input_shape=(14, 1, 2), output_shape=(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._input_shape == (14, 1, 2)
+ assert amc._output_shape == 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", "_input_shape": (14, 1, 2),
+ "_output_shape": 3}
+ assert amsc.get_settings() == {"test_attr": "testAttr", "model_name": "AbstractModelSubClass",
+ "_input_shape": (12, 1, 2), "_output_shape": 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}
diff --git a/test/test_model_modules/test_flatten_tail.py b/test/test_model_modules/test_flatten_tail.py
index 46d63e75f7784a0473685f70d0de8f050a095262..623d51c07f6b27c8d6238d8a5189dea33837115e 100644
--- a/test/test_model_modules/test_flatten_tail.py
+++ b/test/test_model_modules/test_flatten_tail.py
@@ -91,7 +91,7 @@ class TestFlattenTail:
assert final_dropout.rate == 0.35
inner_act = self.step_in(final_dropout)
- assert inner_act.get_config() == {'name': 'activation_3', 'trainable': True, 'activation': 'tanh'}
+ assert inner_act.get_config() == {'name': 'activation_1', 'trainable': True, 'activation': 'tanh'}
inner_dense = self.step_in(inner_act)
assert inner_dense.units == 64
diff --git a/test/test_model_modules/test_model_class.py b/test/test_model_modules/test_model_class.py
index f93f9154350c3b81a9e9218c9809813344126990..cbff4cec6c5b002c3166954880e1008e7f4d7ae3 100644
--- a/test/test_model_modules/test_model_class.py
+++ b/test/test_model_modules/test_model_class.py
@@ -1,205 +1,9 @@
import keras
import pytest
-from mlair.model_modules import AbstractModelClass
from mlair.model_modules.model_class import MyPaperModel
-class Paddings:
- allowed_paddings = {"pad1": 34, "another_pad": True}
-
-
-class AbstractModelSubClass(AbstractModelClass):
-
- def __init__(self):
- super().__init__(input_shape=(12, 1, 2), output_shape=3)
- self.test_attr = "testAttr"
-
-
-class TestAbstractModelClass:
-
- @pytest.fixture
- def amc(self):
- return AbstractModelClass(input_shape=(14, 1, 2), output_shape=(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._input_shape == (14, 1, 2)
- assert amc._output_shape == 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", "_input_shape": (14, 1, 2),
- "_output_shape": 3}
- assert amsc.get_settings() == {"test_attr": "testAttr", "model_name": "AbstractModelSubClass",
- "_input_shape": (12, 1, 2), "_output_shape": 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