flake (#316)

* flake

* update version

Author: themattinthehatt

lightning_pose/api/model.py

@@ -10,9 +10,7 @@
 from lightning_pose.data.datatypes import MultiviewPredictionResult, PredictionResult
 from lightning_pose.models import ALLOWED_MODELS
 from lightning_pose.utils import io as io_utils
-from lightning_pose.utils.predictions import (
-    generate_labeled_video as generate_labeled_video_fn,
-)
+from lightning_pose.utils.predictions import generate_labeled_video as generate_labeled_video_fn
 from lightning_pose.utils.predictions import (
     load_model_from_checkpoint,
     predict_dataset,
@@ -131,15 +129,17 @@ def predict_on_label_csv(
 
         Args:
             csv_file (str | Path): Path to the CSV file of images, keypoint locations.
-            data_dir (str | Path, optional): Root path for relative paths in the CSV file. Defaults to the
-                data_dir originally used when training.
+            data_dir (str | Path, optional): Root path for relative paths in the CSV file.
+                Defaults to the data_dir originally used when training.
             compute_metrics (bool, optional): Whether to compute pixel error and loss metrics on
                 predictions.
-            generate_labeled_images (bool, optional): Whether to save labeled images. Defaults to False.
+            generate_labeled_images (bool, optional): Whether to save labeled images.
+                Defaults to False.
             output_dir (str | Path, optional): The directory to save outputs to.
-                Defaults to `{model_dir}/image_preds/{csv_file_name}`. If set to None, outputs are not saved.
-            add_train_val_test_set (bool): When predicting on training dataset, set to true to add the `set`
-                column to the prediction output.
+                Defaults to `{model_dir}/image_preds/{csv_file_name}`.
+                If set to None, outputs are not saved.
+            add_train_val_test_set (bool): When predicting on training dataset, set to true to add
+                the `set` column to the prediction output.
         Returns:
             PredictionResult: A PredictionResult object containing the predictions and metrics.
         """
@@ -218,13 +218,17 @@ def predict_on_video_file(
 
         Args:
             video_file (str | Path): Path to the video file.
+            output_dir (str | Path, optional): The directory to save outputs to.
+                Defaults to `{model_dir}/image_preds/{csv_file_name}`.
+                If set to None, outputs are not saved.
             compute_metrics (bool, optional): Whether to compute pixel error and loss metrics on
                 predictions.
-            generate_labeled_video (bool, optional): Whether to save a labeled video. Defaults to False.
-            output_dir (str | Path, optional): The directory to save outputs to.
-                Defaults to `{model_dir}/image_preds/{csv_file_name}`. If set to None, outputs are not saved.
+            generate_labeled_video (bool, optional): Whether to save a labeled video.
+                Defaults to False.
+
         Returns:
             PredictionResult: A PredictionResult object containing the predictions and metrics.
+
         """
         self._load()
         video_file = Path(video_file)
@@ -278,15 +282,26 @@ def predict_on_video_file_multiview(
         compute_metrics: bool = True,
         generate_labeled_video: bool = False,
     ) -> MultiviewPredictionResult:
-        """Version of `predict_on_video_file` that gives models access to multiple camera views of each frame.
+        """Version of `predict_on_video_file` that accesses to multiple camera views of each frame.
 
         Arguments:
-            video_file_per_view (list[str] | list[Path]): A list of video files each from a different view of the
-                same session. Number of video files must match the `view_names` in the config file. Order of the list
-                does not matter: video files are intelligently matched to views by their filename using
-                `utils.io.collect_video_files_by_view`.
+            video_file_per_view (list[str] | list[Path]): A list of video files each from a
+                different view of the same session.
+                Number of video files must match the `view_names` in the config file.
+                Order of the list does not matter: video files are intelligently matched to views
+                by their filename using `utils.io.collect_video_files_by_view`.
+            output_dir (str | Path, optional): The directory to save outputs to.
+                Defaults to `{model_dir}/image_preds/{csv_file_name}`.
+                If set to None, outputs are not saved.
+            compute_metrics (bool, optional): Whether to compute pixel error and loss metrics on
+                predictions.
+            generate_labeled_video (bool, optional): Whether to save a labeled video.
+                Defaults to False.
 
-        See `predict_on_video_file` docstring for other arguments."""
+        Returns:
+            MultiviewPredictionResult: object containing the predictions and metrics for each view.
+
+        """
         assert self.config.is_multi_view()
         self._load()
 

lightning_pose/api/model_config.py

@@ -35,8 +35,6 @@ def is_multi_view(self):
             )
         return True
 
-    ## Eval ##
-
     def test_video_files(self) -> list[Path]:
         files = check_video_paths(
             return_absolute_path(self.cfg.eval.test_videos_directory)

lightning_pose/cli/commands/crop.py

@@ -24,7 +24,7 @@ def register_parser(subparsers):
             """\
             Crops a video or labeled frames based on model predictions.
             Requires model predictions to already have been generated using `litpose predict`.
-    
+
             Cropped videos are saved to:
                 <model_dir>/
                 └── video_preds/
@@ -45,7 +45,7 @@ def register_parser(subparsers):
                         └── a/b/c/<image_name>.png        (cropped images)\
             """
         ),
-        usage="litpose crop <model_dir> <input_path:video|csv>... --crop_ratio=CROP_RATIO --anchor_keypoints=x,y,z",
+        usage="litpose crop <model_dir> <input_path:video|csv>... --crop_ratio=CROP_RATIO --anchor_keypoints=x,y,z",  # noqa
     )
     crop_parser.add_argument(
         "model_dir", type=types.existing_model_dir, help="path to a model directory"

lightning_pose/cli/commands/predict.py

@@ -42,7 +42,7 @@ def register_parser(subparsers):
         "input_path",
         type=Path,
         nargs="+",
-        help="one or more paths. They can be video files, image files, CSV files, or directories.\n"
+        help="one or more paths; can be video files, image files, CSV files, or directories.\n"
         "    directory: predicts over videos or images in the directory.\n"
         "               saves image outputs to `image_preds/<directory_name>`\n"
         "    video file: predicts on the video\n"

lightning_pose/cli/commands/remap.py

@@ -3,8 +3,6 @@
 from pathlib import Path
 from textwrap import dedent
 
-from .. import types
-
 
 def register_parser(subparsers):
     """Register the remap command parser."""
@@ -15,7 +13,8 @@ def register_parser(subparsers):
             Remaps predictions from cropped to original coordinate space.
             Requires model predictions to already have been generated using `litpose predict`.
 
-            Remapped predictions are saved as "remapped_{preds_file}" in the same folder as preds_file.
+            Remapped predictions are saved as "remapped_{preds_file}" in the same folder as 
+            preds_file.
             """
         ),
         usage="litpose remap <preds_file> <bbox_file>",

lightning_pose/cli/commands/run_app.py

@@ -27,7 +27,10 @@ def handle(args):
     import importlib.util
     if not importlib.util.find_spec('litpose_app'):
         import sys
-        print("❌ App not installed. To install the app:\n\n    pip install lightning-pose-app\n", file=sys.stderr)
+        print(
+            "❌ App not installed. To install the app:\n\n    pip install lightning-pose-app\n",
+            file=sys.stderr,
+        )
         sys.exit(1)
 
     # Import lightning_pose modules only when needed

lightning_pose/cli/commands/train.py

@@ -29,7 +29,7 @@ def register_parser(subparsers):
         type=types.config_file,
         help="path a config file.\n"
         "Download and modify the config template from: \n"
-        "https://github.com/paninski-lab/lightning-pose/blob/main/scripts/configs/config_default.yaml",
+        "https://github.com/paninski-lab/lightning-pose/blob/main/scripts/configs/config_default.yaml",  # noqa
     )
     train_parser.add_argument(
         "--output_dir",

lightning_pose/data/augmentations.py

@@ -50,10 +50,10 @@ def imgaug_transform(params_dict: dict | DictConfig) -> iaa.Sequential:
         >>>         angle: [-90, 90]
 
         Create a pipeline with
-        - Rot90 transformation that is applied 100% of the time with rotations of 0, 90, 180, or 270 degrees.
+        - Rot90 transformation applied 100% of the time with rotations of 0, 90, 180, 270 degrees.
 
         >>> params_dict = {
-        >>>     'Rot90': {'p': 1.0, 'kwargs': {'k': [[0, 1, 2, 3]]}},  # note the (required) nested list
+        >>>     'Rot90': {'p': 1.0, 'kwargs': {'k': [[0, 1, 2, 3]]}},  # note required nested list
         >>> }
 
         In a config file, this will look like:
@@ -64,8 +64,8 @@ def imgaug_transform(params_dict: dict | DictConfig) -> iaa.Sequential:
         >>>       kwargs:
         >>>         k: [0, 1, 2, 3]
 
-        NOTE: if you pass a list of exactly 2 values to Rot90 it will be parsed as a tuple and all (discrete) rotations
-        between the two values will be sampled uniformly. 
+        NOTE: if you pass a list of exactly 2 values to Rot90 it will be parsed as a tuple and all
+        (discrete) rotations between the two values will be sampled uniformly.
         For example, `k: [0, 2]` is equivalent to `k: [0, 1, 2]`.
         If you need to _only_ sample two non-contiguous integers please raise an issue.
 

lightning_pose/data/datamodules.py

@@ -126,14 +126,14 @@ def _setup(self) -> None:
             self.val_dataset.dataset.imgaug_transform = resize_transform
             if hasattr(self.val_dataset.dataset, "dataset"):
                 # this will get triggered for multiview datasets
-                print(f"val: updating children datasets with resize imgaug pipeline")
+                print("val: updating children datasets with resize imgaug pipeline")
                 for view_name, dset in self.val_dataset.dataset.dataset.items():
                     dset.imgaug_transform = resize_transform
 
             self.test_dataset.dataset.imgaug_transform = resize_transform
             if hasattr(self.test_dataset.dataset, "dataset"):
                 # this will get triggered for multiview datasets
-                print(f"test: updating children datasets with resize imgaug pipeline")
+                print("test: updating children datasets with resize imgaug pipeline")
                 for view_name, dset in self.test_dataset.dataset.dataset.items():
                     dset.imgaug_transform = resize_transform
 

lightning_pose/data/datasets.py

@@ -95,7 +95,9 @@ def __init__(
 
         csv_data = pd.read_csv(csv_file, header=header_rows, index_col=0)
         csv_data = io_utils.fix_empty_first_row(csv_data)
-        self.keypoint_names = io_utils.get_keypoint_names(csv_file=csv_file, header_rows=header_rows)
+        self.keypoint_names = io_utils.get_keypoint_names(
+            csv_file=csv_file, header_rows=header_rows,
+        )
         self.image_names = list(csv_data.index)
         self.keypoints = torch.tensor(csv_data.to_numpy(), dtype=torch.float32)
         # convert to x,y coordinates
@@ -452,7 +454,7 @@ def check_data_images_names(self):
                 img_file_names.add(Path(heatmaps.image_names[idx]).name)
                 if len(img_file_names) > 1:
                     raise ImportError(
-                        f"Discrepancy in image file names across CSV files! "
+                        "Discrepancy in image file names across CSV files! "
                         "index:{idx}, image file names:{img_file_names}"
                     )