[pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

neodroidvision/data/classification/nlet/pair_dataset.py

@@ -29,7 +29,8 @@ class PairDataset(
 ):  # TODO: Extract image specificity of class to a subclass and move this super pair class to a
     # general torch lib.
     """
-    # This dataset generates a pair of images. 0 for geniune pair and 1 for imposter pair"""
+    # This dataset generates a pair of images. 0 for geniune pair and 1 for imposter pair
+    """
 
     @passes_kws_to(DictImageFolder.__init__)
     @drop_unused_kws
@@ -54,9 +55,7 @@ def __init__(
                 root=data_path / SplitEnum.training.value, split=self.split, **kwargs
             )
 
-    def __getitem__(
-        self, idx1: int
-    ) -> Union[
+    def __getitem__(self, idx1: int) -> Union[
         Tuple[torch.Tensor, torch.Tensor, torch.Tensor],
         Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor],
     ]:

neodroidvision/data/mixed/nvlabs_ffhq.py

@@ -374,11 +374,11 @@ def download_files(
                 "%.2f/%.2f %s"
                 % (bytes_done / bytes_div, bytes_total / bytes_div, bytes_unit),
                 "%.2f %s/s" % (bandwidth / bandwidth_div, bandwidth_unit),
-                "done"
-                if bytes_total == bytes_done
-                else "..."
-                if len(timing) < timing_window or bandwidth == 0
-                else eta,
+                (
+                    "done"
+                    if bytes_total == bytes_done
+                    else "..." if len(timing) < timing_window or bandwidth == 0 else eta
+                ),
             ),
             end="",
             flush=True,

neodroidvision/data/synthesis/conversion/mnist/convert_mnist_to_png.py

@@ -75,7 +75,7 @@ def write_dataset(labels, data, size, rows, cols, output_dir) -> None:
             os.makedirs(dir)
     import png  # pip install pypng
 
-    for (i, label) in enumerate(labels):
+    for i, label in enumerate(labels):
         output_filename = output_dirs[label] / f"{str(i)}.png"
         print(f"writing {output_filename}")
         with open(output_filename, "wb") as h:

neodroidvision/detection/single_stage/ssd/architecture/nms_box_heads/box_predictor.py

@@ -28,7 +28,7 @@ def __init__(self, boxes_per_location, out_channels, num_categories):
         self.cls_headers = nn.ModuleList()
         self.reg_headers = nn.ModuleList()
 
-        for (level_i, (num_boxes, num_channels)) in enumerate(
+        for level_i, (num_boxes, num_channels) in enumerate(
             zip(boxes_per_location, self.out_channels)
         ):
             self.cls_headers.append(

neodroidvision/detection/single_stage/ssd/architecture/nms_box_heads/ssd_box_head.py

@@ -118,7 +118,7 @@ def forward(self, features: torch.Tensor) -> SSDOut:
         categori_logits, bbox_pred = self.predictor(features)
 
         results = []
-        for (scores, boxes) in zip(
+        for scores, boxes in zip(
             functional.log_softmax(
                 categori_logits, dim=-1
             ),  # TODO:Check dim maybe it should be 1

neodroidvision/detection/single_stage/ssd/multi_box_loss.py

@@ -46,7 +46,8 @@ def forward(
         confidence (batch_size, num_priors, num_categories): class predictions.
         predicted_locations (batch_size, num_priors, 4): predicted locations.
         labels (batch_size, num_priors): real labels of all the priors.
-        gt_locations (batch_size, num_priors, 4): real boxes corresponding all the priors."""
+        gt_locations (batch_size, num_priors, 4): real boxes corresponding all the priors.
+        """
 
         with torch.no_grad():
             # derived from cross_entropy=sum(log(p))

neodroidvision/detection/single_stage/ssd/object_detection_dataloader.py

@@ -58,9 +58,9 @@ def object_detection_data_loaders(
         cfg=cfg,
         dataset_type=cfg.dataset_type,
         data_root=data_root,
-        sub_datasets=cfg.datasets.train
-        if split == SplitEnum.training
-        else cfg.datasets.test,
+        sub_datasets=(
+            cfg.datasets.train if split == SplitEnum.training else cfg.datasets.test
+        ),
         split=split,
     ).sub_datasets:
         if distributed:
@@ -72,9 +72,11 @@ def object_detection_data_loaders(
 
         batch_sampler = torch.utils.data.sampler.BatchSampler(
             sampler=sampler,
-            batch_size=cfg.solver.batch_size
-            if split == SplitEnum.training
-            else cfg.test.batch_size,
+            batch_size=(
+                cfg.solver.batch_size
+                if split == SplitEnum.training
+                else cfg.test.batch_size
+            ),
             drop_last=False,
         )
         if max_iter is not None:

neodroidvision/regression/diffusion/__init__.py

@@ -10,8 +10,7 @@
 __all__ = []
 
 
-def _main():
-    ...
+def _main(): ...
 
 
 if __name__ == "__main__":

neodroidvision/regression/diffusion/diffusion.py

@@ -10,8 +10,7 @@
 __all__ = []
 
 
-def _main():
-    ...
+def _main(): ...
 
 
 if __name__ == "__main__":

neodroidvision/regression/vae/architectures/flow/architectures.py

@@ -43,7 +43,8 @@ def forward(self, input: torch.Tensor) -> torch.Tensor:
 
 class Generator(nn.Module):
     """
-    Bernoulli model parameterized by a generative network with Gaussian latents for MNIST."""
+    Bernoulli model parameterized by a generative network with Gaussian latents for MNIST.
+    """
 
     def __init__(self, latent_size, data_size):
         super().__init__()

neodroidvision/regression/vae/architectures/flow/vae_flow.py

@@ -106,7 +106,8 @@ class MADE(nn.Module):
 
     Follows https://arxiv.org/abs/1502.03509
 
-    This is used to build MAF: Masked Autoregressive Flow (https://arxiv.org/abs/1705.07057)."""
+    This is used to build MAF: Masked Autoregressive Flow (https://arxiv.org/abs/1705.07057).
+    """
 
     def __init__(self, num_input, num_output, num_hidden, num_context):
         super().__init__()

neodroidvision/segmentation/evaluation/f_score.py

@@ -49,8 +49,6 @@ def f_score(
     fp = torch.sum(pr) - tp
     fn = torch.sum(gt) - tp
 
-    score = ((1 + beta**2) * tp + eps) / (
-        (1 + beta**2) * tp + beta**2 * fn + fp + eps
-    )
+    score = ((1 + beta**2) * tp + eps) / ((1 + beta**2) * tp + beta**2 * fn + fp + eps)
 
     return score

neodroidvision/utilities/visualisation/plot_kernel.py

@@ -57,7 +57,8 @@ def plot_kernels(
       number_cols: number of columns to be displayed
       m_interpolation: interpolation methods matplotlib. See in:
 
-      https://matplotlib.org/gallery/images_contours_and_fields/interpolation_methods.html"""
+      https://matplotlib.org/gallery/images_contours_and_fields/interpolation_methods.html
+    """
 
     number_kernels = tensor.shape[0]
     number_rows = 1 + number_kernels // number_cols

samples/detection/two_stage/maskrcnn_neodroid_pose_dectection.py

@@ -74,7 +74,7 @@ def show_preds(img, pred):
     drawdot = lambda x, y, r=3, fill="red": draw.ellipse(
         (x - r, y - r, x + r, y + r), fill=fill
     )
-    for (box, kpts) in pred:
+    for box, kpts in pred:
         for kpt in kpts:
             if kpt[2] == 1:
                 drawdot(kpt[0], kpt[1])

samples/misc/graphical_interfaces/kivy_demo_app/opencv_face_tracking.py

@@ -147,7 +147,7 @@ def update(self, dt):
                 gray, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30)
             )
 
-            for (x, y, w, h) in faces:
+            for x, y, w, h in faces:
                 cv2.rectangle(rgb, (x, y), (x + w, y + h), (0, 255, 0), 2)
         except Exception as e:
             print(e)

samples/misc/graphical_interfaces/pygame_demo_app/face_detection.py

@@ -72,7 +72,7 @@ def draw_from_points(cv_image, points):
     Returns a cv_image."""
     cv_image = numpy.ascontiguousarray(cv_image, dtype=numpy.uint8)
     for f in points:
-        for (x, y, w, h) in f:
+        for x, y, w, h in f:
             cv2.rectangle(cv_image, (x, y), (x + w, y + h), 255)
     return cv_image
 

samples/misc/opencv_samples/autocapture/dlib_hog_examples/draw_regions.py

@@ -92,7 +92,7 @@ def asijdas():
         for image in AsyncVideoStream():
             gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
 
-            for (i, rect) in enumerate(detector(gray, upsample)):
+            for i, rect in enumerate(detector(gray, upsample)):
                 # determine the facial landmarks for the face region, then
                 # convert the landmark (x, y)-coordinates to a NumPy array
                 shape = shape_to_ndarray(predictor(gray, rect))
@@ -119,7 +119,7 @@ def asijdas():
 
                         # loop over the subset of facial landmarks, drawing the
                         # specific face part
-                        for (x, y) in shape[i:j]:
+                        for x, y in shape[i:j]:
                             cv2.circle(clone, (x, y), 1, (0, 0, 255), -1)
 
                         if False:

samples/misc/opencv_samples/autocapture/dlib_hog_examples/landmark_demo.py

@@ -25,13 +25,13 @@
 for image in AsyncVideoStream():
     gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
 
-    for (i, rect) in enumerate(detector(gray, upsample_num_times)):
+    for i, rect in enumerate(detector(gray, upsample_num_times)):
         # determine the facial landmarks for the face region, then
         # convert the facial landmark (x, y)-coordinates to a NumPy
         # array
         # loop over the (x, y)-coordinates for the facial landmarks
         # and draw them on the image
-        for (x, y) in shape_to_ndarray(predictor(gray, rect)):
+        for x, y in shape_to_ndarray(predictor(gray, rect)):
             cv2.circle(image, (x, y), 2, (0, 255, 0), -1)
 
     if show_image(