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

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

neodroidvision/classification/procedures/classification_procedures.py

@@ -180,7 +180,6 @@ def pred_target_train_model(
                 for phase in [SplitEnum.training, SplitEnum.validation]:
                     if phase == SplitEnum.training:
                         with TorchTrainSession(model):
-
                             input, true_label = zip(*next(train_iterator))
 
                             rgb_imgs = torch_vision_normalize_batch_nchw(

neodroidvision/classification/procedures/deprec/procedures.py

@@ -177,7 +177,6 @@ def predictor_response_train_model_neodroid_observations(
                 for phase in [SplitEnum.training, SplitEnum.validation]:
                     if phase == SplitEnum.training:
                         with TorchTrainSession(model):
-
                             input, true_label = zip(*next(train_iterator))
 
                             rgb_imgs = torch_vision_normalize_batch_nchw(

neodroidvision/data/classification/imagenet/tools/extract_categories.py

@@ -11,7 +11,6 @@
 from pathlib import Path
 
 if __name__ == "__main__":
-
     with open(str(Path.home() / "Downloads" / "imagenet_class_index.json")) as f:
         with open("../imagenet_2012_names.py", "w") as sfn:
             with open("../imagenet_2012_id.py", "w") as sfi:

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

neodroidvision/data/detection/voc/voc_evaluation.py

@@ -235,7 +235,6 @@ def calc_detection_voc_prec_rec(
     ) in six.moves.zip(
         pred_bboxes, pred_labels, pred_scores, gt_bboxes, gt_labels, gt_difficults
     ):
-
         if gt_difficult is None:
             gt_difficult = numpy.zeros(gt_bbox.shape[0], dtype=bool)
 

neodroidvision/data/neodroid_environments/neodroid_camera_observation.py

@@ -75,7 +75,6 @@ def __getitem__(self, index):
 
 
 if __name__ == "__main__":
-
     import torch
     from matplotlib import pyplot
 

neodroidvision/data/segmentation/clouds.py

@@ -83,7 +83,6 @@ def __init__(
         N_FOLDS=10,
         SEED=246232,
     ):
-
         self.transp = transp
 
         if subset != subset.testing:

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/data/synthesis/conversion/mnist/threed/to_nrrd.py

@@ -53,7 +53,6 @@ def save_dataset(X, y, voxel, output, shape=(28, 28)):
 
 
 if __name__ == "__main__":
-
     with gzip.open(PROJECT_APP_PATH.user_data / "mnist.pkl.gz", "rb") as f:
         train_set, valid_set, test_set = pickle.load(f, encoding="iso-8859-1")
 

neodroidvision/data/synthesis/conversion/mnist/threed/voxel_grid.py

@@ -118,7 +118,6 @@ def plot(self, d=2, cmap="Oranges", show_axis: bool = False):
 
         """
         if d == 2:
-
             fig, axes = pyplot.subplots(
                 int(numpy.ceil(self.n_z / 4)), 4, figsize=(8, 8)
             )

neodroidvision/detection/single_stage/ssd/architecture/backbones/efficient_net.py

@@ -104,7 +104,6 @@ def __init__(self, size, model_name, blocks_args=None, global_params=None):
         # Build blocks
         self._blocks = nn.ModuleList([])
         for block_args in self._blocks_args:
-
             # Update block input and output filters based on depth multiplier.
             block_args = block_args._replace(
                 input_filters=round_filters(

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/two_stage/mask_rcnn/maskrcnn_engine.py

@@ -50,7 +50,6 @@ def maskrcnn_train_single_epoch(
     :return:"""
     model.to(device)
     with TorchTrainSession(model):
-
         for images, targets in progress_bar(data_loader, description="Batch #"):
             images = [img.to(device) for img in images]
         targets = [{k: v.to(device) for k, v in t.items()} for t in targets]
@@ -111,7 +110,6 @@ def maskrcnn_evaluate(
 
     with torch.no_grad():
         with TorchEvalSession(model):
-
             for image, targets in progress_bar(data_loader):
                 image = [img.to(device) for img in image]
             targets = [{k: v.to(device) for k, v in t.items()} for t in targets]

neodroidvision/regression/vae/architectures/disentangled/conditional_vae.py

@@ -57,7 +57,6 @@ class Decoder(nn.Module):
     def __init__(
         self, layer_sizes: Sequence[int], latent_size: int, num_conditions: int
     ):
-
         super().__init__()
 
         self.MLP = nn.Sequential()

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/utilities/opencv_utilities/calibrators/hough_circle_calibration.py

@@ -104,13 +104,11 @@ def hough_circle_calibrator(
         hi = cv2.getTrackbarPos(hi_label, canny_frame_window_label)
 
         if lo != lo_prev or hi != hi_prev:  # --------------------------= RE-SYNC
-
             a_canny_refresh_flag = True  # --------------------------= FLAG
 
             lo_prev = lo
             hi_prev = hi
         else:
-
             a_canny_refresh_flag = False  # --------------------------= Un-FLAG
 
         dp = cv2.getTrackbarPos(dp_label, canny_hough_circle_window_label)
@@ -134,7 +132,6 @@ def hough_circle_calibrator(
             or min_radius != min_radius_prev
             or max_radius != max_radius_prev
         ):  # ----------------------------------------------= RE-SYNC
-
             a_hough_refresh_flag = True  # --------------------------= FLAG
 
             dp_prev = dp
@@ -144,7 +141,6 @@ def hough_circle_calibrator(
             min_radius_prev = min_radius
             max_radius_prev = max_radius
         else:
-
             a_hough_refresh_flag = False  # --------------------------= Un-FLAG
 
         if (
@@ -155,7 +151,6 @@ def hough_circle_calibrator(
             cv2.imshow(canny_frame_window_label, edges)
 
         if a_canny_refresh_flag or a_hough_refresh_flag:
-
             circles = cv2.HoughCircles(
                 edges,
                 cv2.HOUGH_GRADIENT,

neodroidvision/utilities/opencv_utilities/calibrators/hough_line_calibration.py

@@ -139,7 +139,6 @@ def hough_line_calibrator(
             lo_prev = lo
             hi_prev = hi
         else:
-
             a_canny_refresh_flag = False  # --------------------------= Un-FLAG
 
         threshold = cv2.getTrackbarPos(threshold_label, canny_hough_lines_window_label)
@@ -171,7 +170,6 @@ def hough_line_calibrator(
             or min_theta != min_theta_prev
             or max_theta != max_theta_prev
         ):  # ----------------------------------------------= RE-SYNC
-
             a_hough_refresh_flag = True  # --------------------------= FLAG
 
             rho_prev = rho
@@ -182,7 +180,6 @@ def hough_line_calibrator(
             min_theta_prev = min_theta
             max_theta_prev = max_theta
         else:
-
             a_hough_refresh_flag = False  # --------------------------= Un-FLAG
 
         if (

neodroidvision/utilities/opencv_utilities/images/add_watermark.py

@@ -45,7 +45,6 @@ def __init__(
         rgb_weight=(0, 1, 1.5),
         input_frame_shape=None,
     ) -> None:
-
         logo_image = cv2.imread(logo_path, cv2.IMREAD_UNCHANGED)
         h, w, c = logo_image.shape
         if angle % 360 != 0:
@@ -77,7 +76,6 @@ def __init__(
         self.logo_image[:, :, 0] = self.logo_image[:, :, 0] * self.rgb_weight[2]
 
         if input_frame_shape is not None:
-
             logo_w = input_frame_shape[1] * self.size
             ratio = logo_w / self.ori_shape[1]
             logo_h = int(ratio * self.ori_shape[0])

neodroidvision/utilities/torch_utilities/output_activation/ops/non_maximum_suppression.py

@@ -20,10 +20,8 @@
 if int(torchvision.__version__.split(".")[1]) >= int("0.3.0".split(".")[1]):
     nms_support = torchvision.ops.nms
 else:
-
     print(f"torchvision version: {torchvision.__version__}" "\n nms not supported")
     try:
-
         import ssd_torch_extension
 
         nms_support = ssd_torch_extension.nms  # non_maximum_suppression

neodroidvision/utilities/torch_utilities/patches/ndim/patching.py

@@ -80,7 +80,6 @@ def suahd():
         patch_size = 8
 
         if show_2d:
-
             from cv2 import circle
             from matplotlib import pyplot
 

neodroidvision/utilities/visualisation/bounding_box_visualisation.py

@@ -344,7 +344,6 @@ def draw_bounding_box_on_image(
             text_left = left
 
         if top > total_display_str_height:
-
             if label_inside:
                 text_bottom = top + total_display_str_height
             else:

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/classification/mnist_retrain.py

@@ -101,7 +101,6 @@ def predictor_response_train_model(
                     for phase in [SplitEnum.training, SplitEnum.validation]:
                         if phase == SplitEnum.training:
                             with TorchTrainSession(model):
-
                                 img, true_label = next(train_iterator)
 
                                 rgb_imgs = to_tensor(

samples/classification/pair_siamese_training.py

@@ -166,7 +166,6 @@ def train_siamese(
                     train_loss = loss_contrastive.cpu().item()
                     writer.scalar("train_loss", train_loss, batch_i)
                 if batch_counter.__next__() % validation_interval == 0:
-
                     with TorchEvalSession(model):
                         valid_loss = 0
                         valid_accuracy = []

samples/classification/ram/ram_train.py

@@ -175,7 +175,6 @@ def train(self, *, writer):
         )
 
         for epoch in range(self.start_epoch, self.epochs):
-
             print(
                 f"\nEpoch: {epoch + 1}/{self.epochs} - LR: {self.optimiser.param_groups[0]['lr']:.6f}"
             )

samples/classification/ram/tests/stest_model.py

@@ -18,7 +18,6 @@
 import torch
 
 if __name__ == "__main__":
-
     config = get_ram_config()
 
     # load images

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/data/synthesis/mnist/mnist_dect_vis.py

@@ -38,7 +38,6 @@ def read_labels(label_path: pathlib.Path) -> Tuple[ndarray, ndarray]:
 
 
 if __name__ == "__main__":
-
     from draugr.opencv_utilities import draw_bounding_boxes
 
     base_path = pathlib.Path(

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

@@ -72,13 +72,12 @@ 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
 
 
 if __name__ == "__main__":
-
     # Set game screen
     screen = pygame.display.set_mode(SCREEN)
 
@@ -90,7 +89,6 @@ def draw_from_points(cv_image, points):
     cam.start()
 
     while 1:  # Ze loop
-
         time.sleep(1 / 120)  # 60 frames per second
 
         image = cam.get_image()  # Get current webcam 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(