refactor: Remove too-many-statements in multiple files#2093
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This change resolves the too-many-statements Pylint warnings by extracting monolithic blocks into specialized private functions. The refactoring directly addresses code smells within: - doctr/models/detection/differentiable_binarization/base.py - doctr/models/layout/lw_detr/pytorch.py - doctr/transforms/modules/pytorch.py - doctr/utils/metrics.py Isolating these heavy operations ensures the main execution paths remain readable and lowers cognitive load without changing behavior.
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What does this PR do?
Refactors monolithic structures across multiple files to eliminate Pylint
too-many-statements(R0915) warnings. Heavy logical blocks were extracted into specialized, private helper methods with single responsibilities:_draw_polygon_on_mapsindoctr/models/detection/differentiable_binarization/base.py_extract_features,_setup_queries,_prepare_encoder_inputs,_encoder_group_predictions,_decode_and_predict,_prepare_outputs, and_compute_lossesindoctr/models/layout/lw_detr/pytorch.py_forward_simpleand_forward_with_aspect_ratioindoctr/transforms/modules/pytorch.py_get_classes,_collect_gt_by_image,_collect_detections,_match_detections, and_evaluate_classindoctr/utils/metrics.pyWhy is this change necessary?
Methods like
LWDETR.forwardandObjectDetectionMetric.summarywere handling too many distinct operations simultaneously (e.g., mixing raw tensor slicing, dictionary evaluation, and geometry constraints within nested loops). This accumulation made code maintenance highly error-prone. Splitting them drops statement counts well below limits, isolates complex tensor scopes, and heavily reduces cognitive load.How was it tested?
make qualityand Pylint score verified locally (achieved 10/10 for statement metrics on affected files).make test). Core execution logic, bounding box matching, and transforms remained fully operational.Notes for the reviewer
The external API semantics and functional behaviors remain entirely identical. Variables and mathematical thresholds were isolated cleanly within their new private methods to prevent cross-scope leaks, easing future pipeline updates and production exports.