Minor refactors (comments, unused imports, unused variables)

Author: deruyter92

dlclive/benchmark.py

@@ -653,26 +653,26 @@ def _get_metadata(
 ):
     try:
         fourcc = decode_fourcc(cap.get(cv2.CAP_PROP_FOURCC))
-    except:
+    except Exception:
         fourcc = ""
     try:
         fps = round(cap.get(cv2.CAP_PROP_FPS))
-    except:
+    except Exception:
         fps = None
     try:
         pix_fmt = decode_fourcc(cap.get(cv2.CAP_PROP_CODEC_PIXEL_FORMAT))
-    except:
+    except Exception:
         pix_fmt = ""
     try:
         frame_count = round(cap.get(cv2.CAP_PROP_FRAME_COUNT))
-    except:
+    except Exception:
         frame_count = None
     try:
         orig_im_size = (
             round(cap.get(cv2.CAP_PROP_FRAME_WIDTH)),
             round(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)),
         )
-    except:
+    except Exception:
         orig_im_size = None
 
     meta = {

dlclive/check_install/check_install.py

@@ -5,19 +5,15 @@
 Licensed under GNU Lesser General Public License v3.0
 """
 
-import os
-import urllib.request
 import argparse
 import shutil
-
-import urllib.request
 import warnings
 from pathlib import Path
 
-from dlclive.utils import download_file
 from dlclibrary.dlcmodelzoo.modelzoo_download import download_huggingface_model
 
 import dlclive
+from dlclive.utils import download_file
 from dlclive.benchmark import benchmark_videos
 from dlclive.engine import Engine
 

dlclive/core/inferenceutils.py

@@ -103,7 +103,7 @@ def __contains__(self, assembly):
 
     def __add__(self, other):
         if other in self:
-            raise ValueError("Assemblies contain shared joints.")
+            raise ArithmeticError("Assemblies contain shared joints.")
 
         assembly = Assembly(self.data.shape[0])
         for link in self._links + other._links:
@@ -340,6 +340,7 @@ def calibrate(self, train_data_file):
         try:
             df.drop("single", level="individuals", axis=1, inplace=True)
         except KeyError:
+            # The "single" individual column may be absent in some training datasets; ignore if missing.
             pass
         n_bpts = len(df.columns.get_level_values("bodyparts").unique())
         if n_bpts == 1:
@@ -547,11 +548,9 @@ def push_to_stack(i):
                 d = self.calc_assembly_mahalanobis_dist(assembly, nan_policy=nan_policy)
                 if d < d_old:
                     push_to_stack(new_ind)
-                    try:
-                        _, _, link = heapq.heappop(tabu)
-                        heapq.heappush(stack, (-link.affinity, next(counter), link))
-                    except IndexError:
-                        pass
+                    if tabu:  
+                        _, _, link = heapq.heappop(tabu)  
+                        heapq.heappush(stack, (-link.affinity, next(counter), link)) 
                 else:
                     heapq.heappush(tabu, (d - d_old, next(counter), best))
                     assembly.__dict__.update(assembly._dict)
@@ -597,8 +596,8 @@ def build_assemblies(self, links):
             self._fill_assembly(
                 assembly, lookup, assembled, self.safe_edge, self.nan_policy
             )
-            for link in assembly._links:
-                i, j = link.idx
+            for assembly_link in assembly._links:
+                i, j = assembly_link.idx
                 lookup[i].pop(j)
                 lookup[j].pop(i)
             assembled.update(assembly._idx)
@@ -666,6 +665,8 @@ def build_assemblies(self, links):
                             for idx in store[j]._idx:
                                 store[idx] = store[i]
                     except KeyError:
+                        # Some links may reference indices that were never added to `store`;  
+                        # in that case we intentionally skip merging for this link
                         pass
 
         # Second pass without edge safety
@@ -1086,6 +1087,7 @@ def parse_ground_truth_data_file(h5_file):
     try:
         df.drop("single", axis=1, level="individuals", inplace=True)
     except KeyError:
+        # Ignore if the "single" individual column is absent 
         pass
     # Cast columns of dtype 'object' to float to avoid TypeError
     # further down in _parse_ground_truth_data.

dlclive/pose_estimation_pytorch/models/detectors/base.py

@@ -10,7 +10,6 @@
 #
 from __future__ import annotations
 
-import logging
 from abc import ABC, abstractmethod
 
 import torch

dlclive/pose_estimation_pytorch/models/heads/dlcrnet.py

@@ -114,7 +114,6 @@ def forward(self, x: torch.Tensor) -> dict[str, torch.Tensor]:
             features = self.convt4(x)
             stage2_in = torch.cat((stage1_hm_out, stage1_paf_out, features), dim=1)
             stage_in = stage2_in
-            stage_paf_out = stage1_paf_out
             stage_hm_out = stage1_hm_out
             for i, (hm_ref_layer, paf_ref_layer) in enumerate(
                 zip(self.hm_ref_layers, self.paf_ref_layers)

dlclive/pose_estimation_pytorch/models/necks/layers.py

@@ -11,7 +11,7 @@
 
 import torch
 import torch.nn.functional as F
-from einops import rearrange, repeat
+from einops import rearrange
 
 
 class Residual(torch.nn.Module):

dlclive/pose_estimation_pytorch/models/necks/transformer.py

@@ -171,7 +171,6 @@ def _make_position_embedding(
         with torch.no_grad():
             self.pe_h = h
             self.pe_w = w
-            length = h * w
         if pe_type != "learnable":
             self.pos_embedding = torch.nn.Parameter(
                 make_sine_position_embedding(h, w, d_model), requires_grad=False

dlclive/pose_estimation_pytorch/models/predictors/dekr_predictor.py

@@ -263,7 +263,7 @@ def max_pool(self, heatmap: torch.Tensor) -> torch.Tensor:
             # Assuming you have 'heatmap' tensor
             max_pooled_heatmap = predictor.max_pool(heatmap)
         """
-        pool1 = torch.nn.MaxPool2d(3, 1, 1)
+        pool1 = torch.nn.MaxPool2d(3, 1, 1) # TODO JR 01/2026: Are these unused variables informative?
         pool2 = torch.nn.MaxPool2d(5, 1, 2)
         pool3 = torch.nn.MaxPool2d(7, 1, 3)
         map_size = (heatmap.shape[1] + heatmap.shape[2]) / 2.0

dlclive/pose_estimation_tensorflow/graph.py

@@ -11,8 +11,6 @@
 vers = (tf.__version__).split(".")
 if int(vers[0]) == 2 or int(vers[0]) == 1 and int(vers[1]) > 12:
     tf = tf.compat.v1
-else:
-    tf = tf
 
 
 def read_graph(file):

tests/test_benchmark_script.py

@@ -1,6 +1,4 @@
-import os
 import glob
-import pathlib
 import pytest
 from dlclive import benchmark_videos, download_benchmarking_data
 from dlclive.engine import Engine
@@ -23,26 +21,24 @@ def test_benchmark_script_runs(tmp_path):
 
     for model_path in dog_models:
         print(f"Running dog model: {model_path}")
-        result = benchmark_videos(
+        benchmark_videos(
             model_path=model_path,
             model_type="base" if Engine.from_model_path(model_path) == Engine.TENSORFLOW else "pytorch",
             video_path=dog_video,
             output=str(out_dir),
             n_frames=n_frames,
             pixels=pixels
         )
-        print("Dog model result:", result)
 
     for model_path in mouse_models:
         print(f"Running mouse model: {model_path}")
-        result = benchmark_videos(
+        benchmark_videos(
             model_path=model_path,
             model_type="base" if Engine.from_model_path(model_path) == Engine.TENSORFLOW else "pytorch",
             video_path=mouse_video,
             output=str(out_dir),
             n_frames=n_frames,
             pixels=pixels
         )
-        print("Mouse model result:", result)
 
     assert any(out_dir.iterdir())