Fix installing package from wheel, rename package

.pre-commit-config.yaml

@@ -29,7 +29,7 @@ repos:
       - id: mypy
         name: mypy
         language: system
-        files: src
+        files: autoxai
         types: [ python ]
         entry: poetry run mypy
       - id: locks

README.md

@@ -4,6 +4,13 @@
 
 The project was tested using Python version `3.8`.
 
+## CUDA
+
+Recommended version of CUDA is `10.2` as it is supported since version
+`1.5.0` of `torch`. You can check compatilibity of Your CUDA version
+with current version of `torch`:
+https://pytorch.org/get-started/previous-versions/.
+
 ## Poetry
 
 To separate runtime environments for different services and repositories, it is
@@ -34,6 +41,34 @@ Once all the steps have been completed, the environment is ready to go.
 Virtual environment by default will be created with name `.venv` inside
 project directory.
 
+## pyenv
+
+`pyenv` is a tool used to manage multiple version of Python. To install
+this package follow instructions on project repository page:
+https://github.com/pyenv/pyenv#installation. After installation You can
+install desired Python version, e.g. `3.8.16`:
+```bash
+pyenv install 3.8.16
+```
+
+The next step is required to be able to use desired version of Python with
+`poetry`. To activate specific version of Python interpreter execute command:
+```bash
+pyenv local 3.8.16 # or `pyenv global 3.8.16`
+```
+
+Inside repository with `poetry` You can select specific version of Python
+interpreter with command:
+```bash
+poetry env use 3.8.16
+```
+
+After changing interpreter version You have to once again install all
+dependencies:
+```bash
+poetry install
+```
+
 ### Installation errors
 
 If You encounter errors during dependencies installation You can disable

src/callback.py → autoxai/callback.py

@@ -7,8 +7,8 @@
 import pytorch_lightning as pl
 import torch
 
-from src.cache_manager import LocalDirCacheManager
-from src.path_manager import ExperimentDataClass
+from autoxai.cache_manager import LocalDirCacheManager
+from autoxai.path_manager import ExperimentDataClass
 
 logger = logging.getLogger(__name__)
 

src/explainer/gradcam.py → autoxai/explainer/gradcam.py

@@ -6,8 +6,8 @@
 import torch
 from captum.attr import GuidedGradCam, LayerGradCam
 
-from src.explainer.model_utils import modify_modules
-from src.explainer.occulusion import CVExplainer
+from autoxai.explainer.model_utils import modify_modules
+from autoxai.explainer.occulusion import CVExplainer
 
 
 class BaseGradCAMCVExplainer(CVExplainer):

src/explainer/gradient_shap.py → autoxai/explainer/gradient_shap.py

@@ -6,7 +6,7 @@
 import torch
 from captum.attr import GradientShap, LayerGradientShap
 
-from src.explainer.base_explainer import CVExplainer
+from autoxai.explainer.base_explainer import CVExplainer
 
 
 class BaseGradientSHAPCVExplainer(CVExplainer):

src/explainer/integrated_gradients.py → autoxai/explainer/integrated_gradients.py

@@ -6,7 +6,7 @@
 import torch
 from captum.attr import IntegratedGradients, LayerIntegratedGradients
 
-from src.explainer.base_explainer import CVExplainer
+from autoxai.explainer.base_explainer import CVExplainer
 
 
 class BaseIntegratedGradientsCVExplainer(CVExplainer):

src/explainer/lrp.py → autoxai/explainer/lrp.py

@@ -6,8 +6,8 @@
 import torch
 from captum.attr import LRP, LayerLRP
 
-from src.explainer.base_explainer import CVExplainer
-from src.explainer.model_utils import modify_modules
+from autoxai.explainer.base_explainer import CVExplainer
+from autoxai.explainer.model_utils import modify_modules
 
 
 class BaseLRPCVExplainer(CVExplainer):

src/explainer/noise_tunnel.py → autoxai/explainer/noise_tunnel.py

@@ -6,7 +6,7 @@
 import torch
 from captum.attr import IntegratedGradients, LayerIntegratedGradients, NoiseTunnel
 
-from src.explainer.occulusion import CVExplainer
+from autoxai.explainer.occulusion import CVExplainer
 
 
 class BaseNoiseTunnelCVExplainer(CVExplainer):

src/explainer/occulusion.py → autoxai/explainer/occulusion.py

@@ -3,7 +3,7 @@
 import torch
 from captum.attr import Occlusion
 
-from src.explainer.base_explainer import CVExplainer
+from autoxai.explainer.base_explainer import CVExplainer
 
 
 class OcculusionCVExplainer(CVExplainer):

example/mnist.py

@@ -10,9 +10,9 @@
 from pytorch_lightning.callbacks.progress import TQDMProgressBar
 from streamlit_app.mnist_model import LitMNIST
 
-from src.cache_manager import LocalDirCacheManager
-from src.callback import CustomPytorchLightningCallback
-from src.path_manager import ExperimentDataClass
+from autoxai.cache_manager import LocalDirCacheManager
+from autoxai.callback import CustomPytorchLightningCallback
+from autoxai.path_manager import ExperimentDataClass
 
 
 def main() -> None:  # pylint: disable = (duplicate-code)
@@ -35,7 +35,7 @@ def main() -> None:  # pylint: disable = (duplicate-code)
 
     model = LitMNIST(data_dir=data_dir, batch_size=batch_size)
     trainer = Trainer(
-        accelerator="auto",
+        accelerator="gpu",
         devices=1 if torch.cuda.is_available() else None,
         max_epochs=max_epochs,
         callbacks=[TQDMProgressBar(refresh_rate=20), callback],

example/offline_resnet.py

@@ -11,8 +11,8 @@
 import torchvision
 from PIL import Image
 
-from src.cache_manager import LocalDirCacheManager
-from src.path_manager import ExperimentDataClass
+from autoxai.cache_manager import LocalDirCacheManager
+from autoxai.path_manager import ExperimentDataClass
 
 
 class DataTransformer:  # pylint: disable = (too-few-public-methods)

example/streamlit_app/method_names.py

@@ -3,21 +3,21 @@
 from enum import Enum
 from typing import Optional
 
-from src.explainer.gradcam import GuidedGradCAMCVExplainer, LayerGradCAMCVExplainer
-from src.explainer.gradient_shap import (
+from autoxai.explainer.gradcam import GuidedGradCAMCVExplainer, LayerGradCAMCVExplainer
+from autoxai.explainer.gradient_shap import (
     GradientSHAPCVExplainer,
     LayerGradientSHAPCVExplainer,
 )
-from src.explainer.integrated_gradients import (
+from autoxai.explainer.integrated_gradients import (
     IntegratedGradientsCVExplainer,
     LayerIntegratedGradientsCVExplainer,
 )
-from src.explainer.lrp import LayerLRPCVExplainer, LRPCVExplainer
-from src.explainer.noise_tunnel import (
+from autoxai.explainer.lrp import LayerLRPCVExplainer, LRPCVExplainer
+from autoxai.explainer.noise_tunnel import (
     LayerNoiseTunnelCVExplainer,
     NoiseTunnelCVExplainer,
 )
-from src.explainer.occulusion import OcculusionCVExplainer
+from autoxai.explainer.occulusion import OcculusionCVExplainer
 
 
 class MethodName(Enum):

example/streamlit_app/mnist_model.py

@@ -10,9 +10,9 @@
 from torchvision.datasets import MNIST
 
 
-class LitMNIST(
+class LitMNIST(  # pylint: disable = (abstract-method, too-many-ancestors, too-many-instance-attributes)
     LightningModule
-):  # pylint: disable = (too-many-ancestors, too-many-instance-attributes)
+):
     """Model to classify MNIST images."""
 
     def __init__(

example/streamlit_app/run_streamlit.py

@@ -20,22 +20,22 @@
     convert_figure_to_numpy,
 )
 
-from src.explainer.base_explainer import CVExplainer
-from src.explainer.gradcam import GuidedGradCAMCVExplainer, LayerGradCAMCVExplainer
-from src.explainer.gradient_shap import (
+from autoxai.explainer.base_explainer import CVExplainer
+from autoxai.explainer.gradcam import GuidedGradCAMCVExplainer, LayerGradCAMCVExplainer
+from autoxai.explainer.gradient_shap import (
     GradientSHAPCVExplainer,
     LayerGradientSHAPCVExplainer,
 )
-from src.explainer.integrated_gradients import (
+from autoxai.explainer.integrated_gradients import (
     IntegratedGradientsCVExplainer,
     LayerIntegratedGradientsCVExplainer,
 )
-from src.explainer.lrp import LayerLRPCVExplainer, LRPCVExplainer
-from src.explainer.noise_tunnel import (
+from autoxai.explainer.lrp import LayerLRPCVExplainer, LRPCVExplainer
+from autoxai.explainer.noise_tunnel import (
     LayerNoiseTunnelCVExplainer,
     NoiseTunnelCVExplainer,
 )
-from src.explainer.occulusion import OcculusionCVExplainer
+from autoxai.explainer.occulusion import OcculusionCVExplainer
 
 cache_path = os.environ.get("LOGDIR", "logs")
 

example/streamlit_app/streamlit_utils.py

@@ -7,7 +7,7 @@
 import torch
 from settings import Settings  # pylint: disable = (import-error)
 
-from src.cache_manager import LocalDirCacheManager
+from autoxai.cache_manager import LocalDirCacheManager
 
 
 def load_subdir(path: str) -> Union[Dict[str, Any], List[str]]:

example/streamlit_app/visualization_utils.py

@@ -5,7 +5,7 @@
 import torch
 from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas
 
-from src.explainer.base_explainer import CVExplainer
+from autoxai.explainer.base_explainer import CVExplainer
 
 
 def convert_figure_to_numpy(figure: matplotlib.pyplot.Figure) -> np.ndarray: