linting and style checks

Signed-off-by: Farhan Ahmed <[email protected]>

Author: f4str

art/estimators/object_detection/pytorch_object_detector.py

@@ -415,7 +415,11 @@ def fit(  # pylint: disable=W0221
         # Apply preprocessing and convert to tensors
         x_preprocessed, y_preprocessed = self._preprocess_and_convert_inputs(x=x, y=y, fit=True, no_grad=True)
 
-        class ObjectDetectorDataset(Dataset):
+        class ObjectDetectionDataset(Dataset):
+            """
+            Object detection dataset in PyTorch.
+            """
+
             def __init__(self, x, y):
                 self.x = x
                 self.y = y
@@ -427,7 +431,7 @@ def __getitem__(self, idx):
                 return self.x[idx], self.y[idx]
 
         # Create dataloader
-        dataset = ObjectDetectorDataset(x_preprocessed, y_preprocessed)
+        dataset = ObjectDetectionDataset(x_preprocessed, y_preprocessed)
         dataloader = DataLoader(
             dataset=dataset,
             batch_size=batch_size,
@@ -442,7 +446,7 @@ def __getitem__(self, idx):
             for x_batch, y_batch in dataloader:
                 # Move inputs to device
                 x_batch = torch.stack(x_batch).to(self.device)
-                y_batch = y_batch = [{k: v.to(self.device) for k, v in y_i.items()} for y_i in y_batch]
+                y_batch = [{k: v.to(self.device) for k, v in y_i.items()} for y_i in y_batch]
 
                 # Zero the parameter gradients
                 self._optimizer.zero_grad()

art/estimators/object_detection/pytorch_yolo.py

@@ -347,7 +347,9 @@ def _get_losses(
             width = self.input_shape[1]
 
         # Convert labels to YOLO format
-        y_preprocessed_yolo = translate_labels_x1y1x2y2_to_xcycwh(labels_x1y1x2y2=y_preprocessed, height=height, width=width)
+        y_preprocessed_yolo = translate_labels_x1y1x2y2_to_xcycwh(
+            labels_x1y1x2y2=y_preprocessed, height=height, width=width
+        )
 
         # Move inputs to device
         x_preprocessed = x_preprocessed.to(self.device)
@@ -514,7 +516,11 @@ def fit(  # pylint: disable=W0221
         # Apply preprocessing and convert to tensors
         x_preprocessed, y_preprocessed = self._preprocess_and_convert_inputs(x=x, y=y, fit=True, no_grad=True)
 
-        class ObjectDetectorDataset(Dataset):
+        class ObjectDetectionDataset(Dataset):
+            """
+            Object detection dataset in PyTorch.
+            """
+
             def __init__(self, x, y):
                 self.x = x
                 self.y = y
@@ -526,7 +532,7 @@ def __getitem__(self, idx):
                 return self.x[idx], self.y[idx]
 
         # Create dataloader
-        dataset = ObjectDetectorDataset(x_preprocessed, y_preprocessed)
+        dataset = ObjectDetectionDataset(x_preprocessed, y_preprocessed)
         dataloader = DataLoader(
             dataset=dataset,
             batch_size=batch_size,

art/estimators/object_detection/utils.py

@@ -18,7 +18,7 @@
 """
 This module contains utility functions for object detection.
 """
-from typing import Dict, List, Any, Tuple, Union, Optional, TYPE_CHECKING
+from typing import Dict, List, Union, Tuple, Optional, TYPE_CHECKING
 
 import numpy as np
 
@@ -95,8 +95,8 @@ def convert_pt_to_tf(y: List[Dict[str, np.ndarray]], height: int, width: int) ->
 
 
 def cast_inputs_to_pt(
-    x: np.ndarray,
-    y: Optional[List[Dict[str, np.ndarray]]] = None,
+    x: Union[np.ndarray, "torch.Tensor"],
+    y: Optional[List[Dict[str, Union[np.ndarray, "torch.Tensor"]]]] = None,
 ) -> Tuple["torch.Tensor", List[Dict[str, "torch.Tensor"]]]:
     """
     Cast object detection inputs `(x, y)` to PyTorch tensors.