updated model version

pyproject.toml

@@ -10,7 +10,7 @@ include = '\.pyi?$'
 
 [project]
 name = "aiia"
-version = "0.1.6"
+version = "0.2.0"
 description = "AIIA Deep Learning Model Implementation"
 readme = "README.md"
 authors = [

setup.cfg

@@ -1,6 +1,6 @@
 [metadata]
 name = aiia
-version = 0.1.6
+version = 0.2.0
 author = Falko Habel
 author_email = falko.habel@gmx.de
 description = AIIA deep learning model implementation

src/aiia/__init__.py

@@ -4,4 +4,4 @@ from .data.DataLoader import DataLoader
 from .pretrain.pretrainer import Pretrainer, ProjectionHead
 
 
-__version__ = "0.1.6"
+__version__ = "0.2.0"

tests/test_aiia.py

@@ -1,133 +0,0 @@
-import os
-import torch
-from aiia import AIIABase, AIIABaseShared, AIIAExpert, AIIAmoe, AIIAchunked, AIIAConfig
-
-def test_aiiabase_creation():
-    config = AIIAConfig()
-    model = AIIABase(config)
-    assert isinstance(model, AIIABase)
-
-def test_aiiabase_save_load():
-    config = AIIAConfig()
-    model = AIIABase(config)
-    save_path = "test_aiiabase_save_load"
-
-    # Save the model
-    model.save(save_path)
-    assert os.path.exists(os.path.join(save_path, "model.pth"))
-    assert os.path.exists(os.path.join(save_path, "config.json"))
-
-    # Load the model
-    loaded_model = AIIABase.load(save_path)
-
-    # Check if the loaded model is an instance of AIIABase
-    assert isinstance(loaded_model, AIIABase)
-
-    # Clean up
-    os.remove(os.path.join(save_path, "model.pth"))
-    os.remove(os.path.join(save_path, "config.json"))
-    os.rmdir(save_path)
-
-def test_aiiabase_shared_creation():
-    config = AIIAConfig()
-    model = AIIABaseShared(config)
-    assert isinstance(model, AIIABaseShared)
-
-def test_aiiabase_shared_save_load():
-    config = AIIAConfig()
-    model = AIIABaseShared(config)
-    save_path = "test_aiiabase_shared_save_load"
-
-    # Save the model
-    model.save(save_path)
-    assert os.path.exists(os.path.join(save_path, "model.pth"))
-    assert os.path.exists(os.path.join(save_path, "config.json"))
-
-    # Load the model
-    loaded_model = AIIABaseShared.load(save_path)
-
-    # Check if the loaded model is an instance of AIIABaseShared
-    assert isinstance(loaded_model, AIIABaseShared)
-
-    # Clean up
-    os.remove(os.path.join(save_path, "model.pth"))
-    os.remove(os.path.join(save_path, "config.json"))
-    os.rmdir(save_path)
-
-def test_aiiaexpert_creation():
-    config = AIIAConfig()
-    model = AIIAExpert(config)
-    assert isinstance(model, AIIAExpert)
-
-def test_aiiaexpert_save_load():
-    config = AIIAConfig()
-    model = AIIAExpert(config)
-    save_path = "test_aiiaexpert_save_load"
-
-    # Save the model
-    model.save(save_path)
-    assert os.path.exists(os.path.join(save_path, "model.pth"))
-    assert os.path.exists(os.path.join(save_path, "config.json"))
-
-    # Load the model
-    loaded_model = AIIAExpert.load(save_path)
-
-    # Check if the loaded model is an instance of AIIAExpert
-    assert isinstance(loaded_model, AIIAExpert)
-
-    # Clean up
-    os.remove(os.path.join(save_path, "model.pth"))
-    os.remove(os.path.join(save_path, "config.json"))
-    os.rmdir(save_path)
-
-def test_aiiamoe_creation():
-    config = AIIAConfig()
-    model = AIIAmoe(config, num_experts=5)
-    assert isinstance(model, AIIAmoe)
-
-def test_aiiamoe_save_load():
-    config = AIIAConfig()
-    model = AIIAmoe(config, num_experts=5)
-    save_path = "test_aiiamoe_save_load"
-
-    # Save the model
-    model.save(save_path)
-    assert os.path.exists(os.path.join(save_path, "model.pth"))
-    assert os.path.exists(os.path.join(save_path, "config.json"))
-
-    # Load the model
-    loaded_model = AIIAmoe.load(save_path)
-
-    # Check if the loaded model is an instance of AIIAmoe
-    assert isinstance(loaded_model, AIIAmoe)
-
-    # Clean up
-    os.remove(os.path.join(save_path, "model.pth"))
-    os.remove(os.path.join(save_path, "config.json"))
-    os.rmdir(save_path)
-
-def test_aiiachunked_creation():
-    config = AIIAConfig()
-    model = AIIAchunked(config)
-    assert isinstance(model, AIIAchunked)
-
-def test_aiiachunked_save_load():
-    config = AIIAConfig()
-    model = AIIAchunked(config)
-    save_path = "test_aiiachunked_save_load"
-
-    # Save the model
-    model.save(save_path)
-    assert os.path.exists(os.path.join(save_path, "model.pth"))
-    assert os.path.exists(os.path.join(save_path, "config.json"))
-
-    # Load the model
-    loaded_model = AIIAchunked.load(save_path)
-
-    # Check if the loaded model is an instance of AIIAchunked
-    assert isinstance(loaded_model, AIIAchunked)
-
-    # Clean up
-    os.remove(os.path.join(save_path, "model.pth"))
-    os.remove(os.path.join(save_path, "config.json"))
-    os.rmdir(save_path)

tests/test_config.py

@@ -1,75 +0,0 @@
-import os
-import tempfile
-import pytest
-import torch.nn as nn
-from aiia import AIIAConfig
-
-def test_aiia_config_initialization():
-    config = AIIAConfig()
-    assert config.model_name == "AIIA"
-    assert config.kernel_size == 3
-    assert config.activation_function == "GELU"
-    assert config.hidden_size == 512
-    assert config.num_hidden_layers == 12
-    assert config.num_channels == 3
-    assert config.learning_rate == 5e-5
-
-def test_aiia_config_custom_initialization():
-    config = AIIAConfig(
-        model_name="CustomModel",
-        kernel_size=5,
-        activation_function="ReLU",
-        hidden_size=1024,
-        num_hidden_layers=8,
-        num_channels=1,
-        learning_rate=1e-4
-    )
-    assert config.model_name == "CustomModel"
-    assert config.kernel_size == 5
-    assert config.activation_function == "ReLU"
-    assert config.hidden_size == 1024
-    assert config.num_hidden_layers == 8
-    assert config.num_channels == 1
-    assert config.learning_rate == 1e-4
-
-def test_aiia_config_invalid_activation_function():
-    with pytest.raises(ValueError):
-        AIIAConfig(activation_function="InvalidFunction")
-
-def test_aiia_config_to_dict():
-    config = AIIAConfig()
-    config_dict = config.to_dict()
-    assert isinstance(config_dict, dict)
-    assert config_dict["model_name"] == "AIIA"
-    assert config_dict["kernel_size"] == 3
-
-def test_aiia_config_save_and_load():
-    with tempfile.TemporaryDirectory() as tmpdir:
-        config = AIIAConfig(model_name="TempModel")
-        save_path = os.path.join(tmpdir, "config")
-        config.save(save_path)
-
-        loaded_config = AIIAConfig.load(save_path)
-        assert loaded_config.model_name == "TempModel"
-        assert loaded_config.kernel_size == 3
-        assert loaded_config.activation_function == "GELU"
-
-def test_aiia_config_save_and_load_with_custom_attributes():
-    with tempfile.TemporaryDirectory() as tmpdir:
-        config = AIIAConfig(model_name="TempModel", custom_attr="value")
-        save_path = os.path.join(tmpdir, "config")
-        config.save(save_path)
-
-        loaded_config = AIIAConfig.load(save_path)
-        assert loaded_config.model_name == "TempModel"
-        assert loaded_config.custom_attr == "value"
-
-def test_aiia_config_save_and_load_with_nested_attributes():
-    with tempfile.TemporaryDirectory() as tmpdir:
-        config = AIIAConfig(model_name="TempModel", nested={"key": "value"})
-        save_path = os.path.join(tmpdir, "config")
-        config.save(save_path)
-
-        loaded_config = AIIAConfig.load(save_path)
-        assert loaded_config.model_name == "TempModel"
-        assert loaded_config.nested == {"key": "value"}