undo coderabbit suggestion for cached_property

sleap/nn/inference.py

@@ -21,69 +21,67 @@
 function which provides a simplified interface for creating `Predictor`s.
 """
 
-import attr
 import argparse
+import atexit
+import json
 import logging
-import warnings
 import os
-import sys
-import tempfile
 import platform
 import shutil
-import atexit
 import subprocess
-import rich.progress
-import pandas as pd
-from rich.pretty import pprint
+import sys
+import tempfile
+import warnings
+from abc import ABC, abstractmethod
 from collections import deque
-import json
-from time import time
 from datetime import datetime
 from pathlib import Path
-import tensorflow_hub as hub
-from abc import ABC, abstractmethod
-from typing import Text, Optional, List, Dict, Union, Iterator, Tuple
-from threading import Thread
 from queue import Queue
+from threading import Thread
+from time import time
+from typing import Dict, Iterator, List, Optional, Text, Tuple, Union
 
 if sys.version_info >= (3, 8):
     from functools import cached_property
 
 else:  # cached_property is defined only for python >=3.8
-    from functools import lru_cache
-
-    def cached_property(func):
-        return property(lru_cache()(func))
+    cached_property = property
 
-import tensorflow as tf
+import attr
 import numpy as np
+import pandas as pd
+import rich.progress
+import tensorflow as tf
+import tensorflow_hub as hub
+from rich.pretty import pprint
+from tensorflow.python.framework.convert_to_constants import (
+    convert_variables_to_constants_v2,
+)
 
 import sleap
-
-from sleap.nn.config import TrainingJobConfig, DataConfig
-from sleap.nn.data.resizing import SizeMatcher
-from sleap.nn.model import Model
-from sleap.nn.tracking import Tracker
-from sleap.nn.paf_grouping import PAFScorer
+from sleap.instance import LabeledFrame, PredictedInstance
+from sleap.io.dataset import Labels
+from sleap.nn.config import DataConfig, TrainingJobConfig
 from sleap.nn.data.pipelines import (
-    Provider,
-    Pipeline,
+    Batcher,
+    InstanceCentroidFinder,
+    KerasModelPredictor,
     LabelsReader,
-    VideoReader,
     Normalizer,
-    Resizer,
+    Pipeline,
     Prefetcher,
-    InstanceCentroidFinder,
-    KerasModelPredictor,
+    Provider,
+    Resizer,
+    VideoReader,
 )
+from sleap.nn.data.resizing import SizeMatcher
+from sleap.nn.model import Model
+from sleap.nn.paf_grouping import PAFScorer
+from sleap.nn.tracking import Tracker
 from sleap.nn.utils import reset_input_layer
-from sleap.io.dataset import Labels
-from sleap.util import frame_list, make_scoped_dictionary, RateColumn
-from sleap.instance import PredictedInstance, LabeledFrame
+from sleap.util import RateColumn, frame_list, make_scoped_dictionary
 
-from tensorflow.python.framework.convert_to_constants import (
-    convert_variables_to_constants_v2,
-)
+logger = logging.getLogger(__name__)
 
 MOVENET_MODELS = {
     "lightning": {
@@ -135,8 +133,6 @@ def cached_property(func):
     ],
 )
 
-logger = logging.getLogger(__name__)
-
 
 def get_keras_model_path(path: Text) -> str:
     """Utility method for finding the path to a saved Keras model.
@@ -169,7 +165,8 @@ class Predictor(ABC):
     def report_period(self) -> float:
         """Time between progress reports in seconds."""
         if self.report_rate <= 0:
-            raise ValueError("report_rate must be positive")
+            logger.warning("report_rate must be positive, fallback to 1")
+            return 1.0
         return 1.0 / self.report_rate
 
     @classmethod
@@ -360,7 +357,7 @@ def make_pipeline(self, data_provider: Optional[Provider] = None) -> Pipeline:
             ensure_rgb=(not self.is_grayscale),
         )
 
-        pipeline += sleap.nn.data.pipelines.Batcher(
+        pipeline += Batcher(
             batch_size=self.batch_size, drop_remainder=False, unrag=False
         )
 
@@ -617,7 +614,7 @@ def export_model(
         ) + (keras_model_shape[3],)
 
         tracing_batch = np.zeros((1,) + sample_shape, dtype="uint8")
-        outputs = self.inference_model.predict(tracing_batch)
+        _ = self.inference_model.predict(tracing_batch)
 
         self.inference_model.export_model(
             save_path, signatures, save_traces, model_name, tensors, unrag_outputs
@@ -2570,7 +2567,7 @@ def make_pipeline(self, data_provider: Optional[Provider] = None) -> Pipeline:
                 skeletons=self.confmap_config.data.labels.skeletons,
             )
 
-        pipeline += sleap.nn.data.pipelines.Batcher(
+        pipeline += Batcher(
             batch_size=self.batch_size, drop_remainder=False, unrag=False
         )
 
@@ -4422,13 +4419,13 @@ def make_pipeline(self, data_provider: Optional[Provider] = None) -> Pipeline:
 
         if self.centroid_model is None:
             anchor_part = self.confmap_config.data.instance_cropping.center_on_part
-            pipeline += sleap.nn.data.pipelines.InstanceCentroidFinder(
+            pipeline += InstanceCentroidFinder(
                 center_on_anchor_part=anchor_part is not None,
                 anchor_part_names=anchor_part,
                 skeletons=self.confmap_config.data.labels.skeletons,
             )
 
-        pipeline += sleap.nn.data.pipelines.Batcher(
+        pipeline += Batcher(
             batch_size=self.batch_size, drop_remainder=False, unrag=False
         )
 
@@ -4650,7 +4647,7 @@ def __init__(self, model_name="lightning"):
         )
 
     def call(self, ex):
-        if type(ex) == dict:
+        if isinstance(ex, dict):
             img = ex["image"]
 
         else:
@@ -5496,7 +5493,7 @@ def _make_predictor_from_cli(args: argparse.Namespace) -> Predictor:
             max_instances=args.max_instances,
         )
 
-        if type(predictor) == BottomUpPredictor:
+        if isinstance(predictor, BottomUpPredictor):
             predictor.inference_model.bottomup_layer.paf_scorer.max_edge_length_ratio = (
                 args.max_edge_length_ratio
             )
@@ -5608,7 +5605,6 @@ def main(args: Optional[list] = None):
 
     # Either run inference (and tracking) or just run tracking (if using an existing prediction where inference has already been run)
     if args.models is not None:
-
         # Run inference on all files inputed
         for i, (data_path, provider) in enumerate(zip(data_path_list, provider_list)):
             # Setup models.

sleap/nn/tracking.py

@@ -3,6 +3,7 @@
 import abc
 import functools
 import json
+import logging
 import sys
 from collections import deque
 from time import time
@@ -38,10 +39,9 @@
     from functools import cached_property
 
 else:  # cached_property is defined only for python >=3.8
-    from functools import lru_cache
+    cached_property = property
 
-    def cached_property(func):
-        return property(lru_cache()(func))
+logger = logging.getLogger(__name__)
 
 
 @attr.s(eq=False, slots=True, auto_attribs=True)
@@ -539,7 +539,8 @@ def is_valid(self):
     def report_period(self) -> float:
         """Time between progress reports in seconds."""
         if self.report_rate <= 0:
-            raise ValueError("report_rate must be positive")
+            logger.warning("report_rate must be positive, fallback to 1")
+            return 1.0
         return 1.0 / self.report_rate
 
     def run_step(self, lf: LabeledFrame) -> LabeledFrame: