Feat/example yolov5

.gitattributes

@@ -0,0 +1,3 @@
+*.ipynb filter=nbstripout
+*.zpln filter=nbstripout
+*.ipynb diff=ipynb

.gitignore

@@ -161,3 +161,7 @@ cython_debug/
 
 # VS Code
 .vscode/
+
+# pytorch checkpoints
+*.pt
+*.pth

autoxai/array_utils.py

@@ -86,7 +86,7 @@ def resize_attributes(
     single_channel_attributes: np.ndarray = np.array(
         cv2.resize(
             attributes,
-            (dest_height, dest_width),
+            (dest_width, dest_height),
         )
     )
 

autoxai/callbacks/wandb_callback.py

@@ -1,15 +1,15 @@
 """Callback for Weights and Biases."""
 from collections import defaultdict
-from typing import Dict, Generator, List, Optional, Tuple
+from typing import Dict, Generator, List, Optional, Tuple, cast
 
 import matplotlib
 import numpy as np
 import pytorch_lightning as pl
 import torch
+import wandb
 from pytorch_lightning.loggers import WandbLogger
 from torch.utils.data import DataLoader
 
-import wandb
 from autoxai.array_utils import convert_float_to_uint8
 from autoxai.context_manager import AutoXaiExplainer, ExplainerWithParams
 from autoxai.explainer.base_explainer import CVExplainer
@@ -106,7 +106,7 @@ def explain(  # pylint: disable = (too-many-arguments)
             explainers=self.explainers,
             target=int(target_label.item()),
         ) as xai_model:
-            _, attributes = xai_model(item.to(model.device))
+            _, attributes = xai_model(item.to(cast(torch.device, model.device)))
 
         for explainer in self.explainers:
             explainer_name: str = explainer.explainer_name.name

autoxai/cli/experiment_update.py

@@ -7,10 +7,10 @@
 import pandas as pd
 import pytorch_lightning as pl
 import torch
+import wandb
 from omegaconf import DictConfig
 from torchvision.io import ImageReadMode, read_image
 
-import wandb
 from autoxai.cli.config_model import ConfigDataModel, MethodDataModel
 from autoxai.context_manager import AutoXaiExplainer, Explainers, ExplainerWithParams
 
@@ -210,7 +210,8 @@ def main(cfg: DictConfig) -> None:  # pylint: disable = (too-many-locals)
                     target=label,
                 ) as xai_model:
                     input_data = input_data.float()
-                    _, attributes_dict = xai_model(input_data.to(model.device))
+                    # type: ignore
+                    _, attributes_dict = xai_model(input_data.to(model.device))  # type: ignore # type: ignore
                     explainer_name: str = (
                         explainer_config.explainer_with_params.explainer_name.name
                     )

autoxai/explainer/noise_tunnel.py

@@ -37,7 +37,7 @@ def calculate_features(
         pred_label_idx: TargetType = None,
         nt_type: str = "smoothgrad",
         nt_samples: int = 5,
-        nt_samples_batch_size: int = None,
+        nt_samples_batch_size: Optional[int] = None,
         stdevs: Union[float, Tuple[float, ...]] = 1.0,
         draw_baseline_from_distrib: bool = False,
         **kwargs,
@@ -118,13 +118,13 @@ def calculate_features(
         noise_tunnel = self.create_explainer(model=model, layer=layer)
 
         attributions = noise_tunnel.attribute(
-            input_data,
-            nt_samples=nt_samples,
+            inputs=input_data,
             nt_type=nt_type,
-            target=pred_label_idx,
+            nt_samples=nt_samples,
             nt_samples_batch_size=nt_samples_batch_size,
             stdevs=stdevs,
             draw_baseline_from_distrib=draw_baseline_from_distrib,
+            target=pred_label_idx,
         )
         validate_result(attributions=attributions)
         return attributions