Repository Analysis

facebookresearch/sam3

The repository provides code for running inference and finetuning with the Meta Segment Anything Model 3 (SAM 3), links for downloading the trained model checkpoints, and example notebooks that show how to use the model.

30.5 Strong AI signal View on GitHub

Analysis Overview

This report presents the forensic synthetic code analysis of facebookresearch/sam3, a Python project with 11,173 GitHub stars. SynthScan v2.0 examined 89,045 lines of code across 224 source files, recording 1300 pattern matches distributed across 19 syntactic categories. The overall adjusted score of 30.5 places this repository in the Strong AI signal band.

The scanner applied 160+ deterministic lexical heuristics, multi-line block detectors, abstract syntax tree depth profilers, and a cross-file Jaccard similarity matrix to construct a statistically normalised synthetic code estimate. All matches are individually weighted by severity coefficient and contextual multiplier before summation, and the resulting headline score is temporally discounted to account for the repository's development history relative to the commercial emergence of large language model coding tooling (November 2022 onward).

30.5
Adjusted Score
30.5
Raw Score
100%
Time Factor
2026-07-31
Last Push
11.2K
Stars
Python
Language
89.0K
Lines of Code
224
Files
1.3K
Pattern Hits
2026-08-02
Scan Date
0.39
HC Hit Rate

What These Metrics Mean

Adjusted Score
Primary synthetic code indicator. Raw score normalised per 1,000 lines of code and multiplied by the temporal discount factor. This is the definitive comparative metric — use it to rank repositories by AI authorship density.
Raw Score
The unmodified sum of all severity-weighted, context-multiplied pattern match scores before temporal discounting. Reflects the absolute signal strength independent of when the repository was last active.
Time Factor
The temporal discount multiplier (0–100%) applied to the raw score. Repositories last updated before ChatGPT's launch (Nov 2022) receive a 5% factor. Full signal is only assigned to repositories active in the post-adoption era (Jan 2024+).
Pattern Hits
Total count of individual pattern matches across all files and categories. A high hit count with a low score may indicate a very large codebase with isolated AI snippets; a low count with a high score indicates dense, concentrated AI signatures.
HC Hit Rate
High+Critical pattern hits per file, averaged across the repository. This orthogonal signal catches repositories where a few files are densely packed with high-severity AI tells — a strong indicator even when the normalised score appears moderate due to codebase size.
Lines of Code / Files
Total lines and files analysed. The scanner examines 94 file extensions. These denominators are used to normalise the score, enabling fair comparison between repositories of vastly different sizes.

Score History

Longitudinal tracking requires multiple scan runs. Once this repository is re-scanned after new commits land, this chart will visualise how the synthetic code signal evolves over time — enabling you to detect whether AI authorship is growing, stabilising, or being actively corrected by human engineers.

No multi-scan history yet — run the scanner again to build trend data.

Severity Breakdown

Classifies detected patterns by their diagnostic confidence and structural impact. CRITICAL patterns (coefficient 10) represent definitive synthetic signatures — hallucinated imports, explicit LLM attribution metadata — virtually never produced by human authors. HIGH (5) indicates strong structural tells such as cross-file repetition or cross-linguistic idioms. MEDIUM (2) covers recognisable conversational padding and AI-specific vocabulary. LOW (1) captures subtle indicators like tautological comments and generic boilerplate that require density to carry independent signal.

CRITICAL 6HIGH 81MEDIUM 315LOW 898

Directory Score Breakdown

This horizontal bar chart decomposes the repository's raw synthetic code score by top-level directory, allowing you to pinpoint precisely which modules or components carry the highest AI authorship density. Directories with disproportionately high scores relative to their size warrant targeted manual review: concentrated AI signatures often trace back to mass-generated configuration layers, auto-ported test suites, LLM-scaffolded boilerplate classes, or entire subsystems authored under heavy copilot assistance. Use this view to prioritise your human code-review effort.

Pattern Findings

The scanner identified 1300 distinct pattern matches across 19 syntactic categories. Each entry below represents a discrete location in the source code where the engine recorded a statistically significant AI authorship indicator. Expand any category row to inspect the individual file paths, line numbers, code snippets, and the lexical context (CODE, COMMENT, or STRING) in which each match was detected.

Reading the findings table: The Severity column indicates the diagnostic confidence level (CRITICAL / HIGH / MEDIUM / LOW). The Context column identifies whether the match occurred inside executable code, an inline comment, or a string literal — comment-context matches receive a ×1.5 weight because LLMs systematically over-annotate. The ⚡ bolt icon marks clustered matches: three or more patterns within a 10-line window, each receiving an additional ×1.5 density multiplier as dense clusters constitute far stronger evidence of synthetic authorship than isolated hits.

Decorative Section Separators276 hits · 1000 pts
SeverityFileLineSnippetContext
MEDIUMsam3/model/sam3_multiplex_base.py2235 # ============================================================================COMMENT
MEDIUMsam3/model/sam3_multiplex_base.py2237 # ============================================================================COMMENT
MEDIUMsam3/model/sam3_multiplex_base.py2247 # ============================================================================COMMENT
MEDIUMsam3/model/sam3_multiplex_base.py2249 # ============================================================================COMMENT
MEDIUMsam3/model/sam3_multiplex_base.py2276 # ============================================================================COMMENT
MEDIUMsam3/model/sam3_multiplex_base.py2278 # ============================================================================COMMENT
MEDIUMsam3/model/sam3_multiplex_base.py2131 # ============================================================================COMMENT
MEDIUMsam3/model/sam3_multiplex_base.py2133 # ============================================================================COMMENT
MEDIUMsam3/model/sam3_multiplex_base.py2167 # ============================================================================COMMENT
MEDIUMsam3/model/sam3_multiplex_base.py2169 # ============================================================================COMMENT
MEDIUMsam3/model/sam3_multiplex_base.py2212 # ============================================================================COMMENT
MEDIUMsam3/model/sam3_multiplex_base.py2214 # ============================================================================COMMENT
MEDIUMsam3/model/sam3_multiplex_base.py2311 # ============================================================================COMMENT
MEDIUMsam3/model/sam3_multiplex_base.py2313 # ============================================================================COMMENT
MEDIUMsam3/model/sam3_multiplex_base.py2327 # ============================================================================COMMENT
MEDIUMsam3/model/sam3_multiplex_base.py2329 # ============================================================================COMMENT
MEDIUMsam3/model/sam3_multiplex_base.py2358 # ============================================================================COMMENT
MEDIUMsam3/model/sam3_multiplex_base.py2360 # ============================================================================COMMENT
MEDIUMsam3/model/sam3_base_predictor.py47 # ── Request dispatch ──────────────────────────────────────────────COMMENT
MEDIUMsam3/model/sam3_base_predictor.py117 # ── Session management ────────────────────────────────────────────COMMENT
MEDIUMsam3/train/configs/eval_base.yaml18# ============================================================================COMMENT
MEDIUMsam3/train/configs/eval_base.yaml20# ============================================================================COMMENT
MEDIUMsam3/train/configs/eval_base.yaml45# ============================================================================COMMENT
MEDIUMsam3/train/configs/eval_base.yaml47# ============================================================================COMMENT
MEDIUMsam3/train/configs/eval_base.yaml146# ============================================================================COMMENT
MEDIUMsam3/train/configs/eval_base.yaml148# ============================================================================COMMENT
MEDIUMsam3/train/configs/eval_base.yaml260# ============================================================================COMMENT
MEDIUMsam3/train/configs/eval_base.yaml262# ============================================================================COMMENT
MEDIUM…m3/train/configs/roboflow_v100/roboflow_v100_eval.yaml5# ============================================================================COMMENT
MEDIUM…m3/train/configs/roboflow_v100/roboflow_v100_eval.yaml7# ============================================================================COMMENT
MEDIUM…m3/train/configs/roboflow_v100/roboflow_v100_eval.yaml156# ============================================================================COMMENT
MEDIUM…m3/train/configs/roboflow_v100/roboflow_v100_eval.yaml158# ============================================================================COMMENT
MEDIUM…m3/train/configs/roboflow_v100/roboflow_v100_eval.yaml239# ============================================================================COMMENT
MEDIUM…m3/train/configs/roboflow_v100/roboflow_v100_eval.yaml241# ============================================================================COMMENT
MEDIUM…m3/train/configs/roboflow_v100/roboflow_v100_eval.yaml411# ============================================================================COMMENT
MEDIUM…m3/train/configs/roboflow_v100/roboflow_v100_eval.yaml413# ============================================================================COMMENT
MEDIUM…m3/train/configs/roboflow_v100/roboflow_v100_eval.yaml435# ============================================================================COMMENT
MEDIUM…m3/train/configs/roboflow_v100/roboflow_v100_eval.yaml437# ============================================================================COMMENT
MEDIUM…gs/roboflow_v100/roboflow_v100_full_ft_100_images.yaml5# ============================================================================COMMENT
MEDIUM…gs/roboflow_v100/roboflow_v100_full_ft_100_images.yaml7# ============================================================================COMMENT
MEDIUM…gs/roboflow_v100/roboflow_v100_full_ft_100_images.yaml156# ============================================================================COMMENT
MEDIUM…gs/roboflow_v100/roboflow_v100_full_ft_100_images.yaml158# ============================================================================COMMENT
MEDIUM…gs/roboflow_v100/roboflow_v100_full_ft_100_images.yaml239# ============================================================================COMMENT
MEDIUM…gs/roboflow_v100/roboflow_v100_full_ft_100_images.yaml241# ============================================================================COMMENT
MEDIUM…gs/roboflow_v100/roboflow_v100_full_ft_100_images.yaml411# ============================================================================COMMENT
MEDIUM…gs/roboflow_v100/roboflow_v100_full_ft_100_images.yaml413# ============================================================================COMMENT
MEDIUM…gs/roboflow_v100/roboflow_v100_full_ft_100_images.yaml435# ============================================================================COMMENT
MEDIUM…gs/roboflow_v100/roboflow_v100_full_ft_100_images.yaml437# ============================================================================COMMENT
MEDIUM…figs/saco_video_evals/saco_veval_yt1b_test_noheur.yaml5# ============================================================================COMMENT
MEDIUM…figs/saco_video_evals/saco_veval_yt1b_test_noheur.yaml7# ============================================================================COMMENT
MEDIUM…figs/saco_video_evals/saco_veval_yt1b_test_noheur.yaml17# ============================================================================COMMENT
MEDIUM…figs/saco_video_evals/saco_veval_yt1b_test_noheur.yaml19# ============================================================================COMMENT
MEDIUM…figs/saco_video_evals/saco_veval_yt1b_test_noheur.yaml59# ============================================================================COMMENT
MEDIUM…figs/saco_video_evals/saco_veval_yt1b_test_noheur.yaml61# ============================================================================COMMENT
MEDIUM…figs/saco_video_evals/saco_veval_yt1b_test_noheur.yaml156# ============================================================================COMMENT
MEDIUM…figs/saco_video_evals/saco_veval_yt1b_test_noheur.yaml158# ============================================================================COMMENT
MEDIUM…igs/saco_video_evals/saco_veval_smartglasses_test.yaml5# ============================================================================COMMENT
MEDIUM…igs/saco_video_evals/saco_veval_smartglasses_test.yaml7# ============================================================================COMMENT
MEDIUM…igs/saco_video_evals/saco_veval_smartglasses_test.yaml17# ============================================================================COMMENT
MEDIUM…igs/saco_video_evals/saco_veval_smartglasses_test.yaml19# ============================================================================COMMENT
216 more matches not shown…
Cross-Language Confusion70 hits · 601 pts
SeverityFileLineSnippetContext
HIGHsam3/model/decoder.py153 # pyre-fixme[61]: `tgt_o2m` is undefined, or not always defined.COMMENT
HIGHsam3/model/decoder.py762 # pyre-fixme[61]: `normed_output` is undefined, or not always defined.COMMENT
HIGHsam3/model/decoder.py883 # pyre-fixme[61]: `other_tgt` is undefined, or not always defined.COMMENT
HIGHsam3/model/sam3_multiplex_base.py1774 # pyre-fixme[61]: `out_frame_idx` is undefined, or not always defined.COMMENT
HIGHsam3/model/sam3_multiplex_base.py1776 # pyre-fixme[61]: `out_frame_idx` is undefined, or not always defined.COMMENT
HIGHsam3/model/sam3_multiplex_base.py1779 # pyre-fixme[61]: `out_obj_ids` is undefined, or not always defined.COMMENT
HIGHsam3/model/sam3_multiplex_base.py1784 # pyre-fixme[61]: `out_obj_ids` is undefined, or not always defined.COMMENT
HIGHsam3/model/sam3_multiplex_base.py1802 # pyre-fixme[61]: `out_obj_ids` is undefined, or notCOMMENT
HIGHsam3/model/sam3_multiplex_base.py1826 # pyre-fixme[61]: `out_obj_ids` is undefined, or not always defined.COMMENT
HIGHsam3/model/sam3_multiplex_base.py1828 # pyre-fixme[61]: `out_obj_ids` is undefined, or not always defined.COMMENT
HIGHsam3/model/sam3_multiplex_base.py2573 # pyre-fixme[61]: `object_idx_assignment` is undefined, or notCOMMENT
HIGHsam3/model/sam3_multiplex_base.py2622 # pyre-fixme[61]: `local_image_features` is undefined, orCOMMENT
HIGHsam3/model/sam3_multiplex_base.py2627 # pyre-fixme[61]: `local_image_pos_enc` is undefined, or notCOMMENT
HIGHsam3/model/video_tracking_multiplex.py1608 # pyre-fixme[61]: `to_cat_image_feat` is undefined, or not always defined.COMMENT
HIGHsam3/model/video_tracking_multiplex.py1609 # pyre-fixme[61]: `to_cat_image_pos_embed` is undefined, or not alwaysCOMMENT
HIGHsam3/model/video_tracking_multiplex.py1615 # pyre-fixme[61]: `to_cat_image_feat` is undefined, or not always defined.COMMENT
HIGHsam3/model/video_tracking_multiplex.py1617 # pyre-fixme[61]: `to_cat_image_pos_embed` is undefined, or not alwaysCOMMENT
HIGHsam3/model/video_tracking_multiplex.py3275 # pyre-fixme[61]: `existing_pointers` is undefined, or not always defined.COMMENT
HIGHsam3/model/video_tracking_multiplex.py3405 # pyre-fixme[61]: `existing_pointers` is undefined, or not always defined.COMMENT
HIGHsam3/model/video_tracking_multiplex.py3407 # pyre-fixme[61]: `existing_pointers` is undefined, or not always defined.COMMENT
HIGHsam3/model/video_tracking_multiplex.py931 # pyre-fixme[61]: `obj_ptr` is undefined, or not always defined.COMMENT
HIGHsam3/model/video_tracking_multiplex.py1017 # pyre-fixme[61]: `object_score_logits` is undefined, or not always defined.COMMENT
HIGHsam3/model/video_tracking_multiplex.py1021 # pyre-fixme[61]: `obj_ptr` is undefined, or not always defined.COMMENT
HIGHsam3/model/video_tracking_multiplex.py1378 # pyre-fixme[61]: `valid_indices` is undefined, or not alwaysCOMMENT
HIGHsam3/model/video_tracking_multiplex.py1382 # pyre-fixme[61]: `valid_indices` is undefined, or not alwaysCOMMENT
HIGHsam3/model/video_tracking_multiplex.py1515 # pyre-fixme[61]: `valid_indices` is undefined, or notCOMMENT
HIGHsam3/model/video_tracking_multiplex.py1519 # pyre-fixme[61]: `valid_indices` is undefined, or notCOMMENT
HIGHsam3/model/video_tracking_multiplex.py1791 # pyre-fixme[61]: `unconditioning_objects` is undefined, or notCOMMENT
HIGHsam3/model/video_tracking_multiplex.py1794 # pyre-fixme[61]: `unconditioning_objects` is undefined, or notCOMMENT
HIGHsam3/model/video_tracking_multiplex.py2416 # pyre-fixme[61]: `obj_ptr` is undefined, or not always defined.COMMENT
HIGHsam3/model/video_tracking_multiplex.py2417 # pyre-fixme[61]: `interact_obj_ptr` is undefined, or not alwaysCOMMENT
HIGHsam3/model/video_tracking_multiplex.py2456 # pyre-fixme[61]: `obj_ptr` is undefined, or not always defined.COMMENT
HIGHsam3/model/sam3_video_base.py850 # pyre-fixme[61]: `new_det_fa_inds` is undefined, or not always defined.COMMENT
HIGHsam3/model/sam3_video_base.py852 # pyre-fixme[61]: `new_det_obj_ids` is undefined, or not always defined.COMMENT
HIGHsam3/model/sam3_video_base.py854 # pyre-fixme[61]: `new_det_gpu_ids` is undefined, or not always defined.COMMENT
HIGHsam3/model/sam3_video_base.py857 # pyre-fixme[61]: `unmatched_trk_obj_ids` is undefined, or notCOMMENT
HIGHsam3/model/sam3_video_base.py860 # pyre-fixme[61]: `det_to_matched_trk_obj_ids` is undefined, or notCOMMENT
HIGHsam3/model/sam3_video_base.py863 # pyre-fixme[61]: `obj_ids_newly_removed` is undefined, or notCOMMENT
HIGHsam3/model/sam3_video_base.py866 # pyre-fixme[61]: `num_obj_dropped_due_to_limit` is undefined, orCOMMENT
HIGHsam3/model/sam3_video_base.py869 # pyre-fixme[61]: `trk_id_to_max_iou_high_conf_det` is undefined, orCOMMENT
HIGHsam3/model/sam3_video_base.py905 # pyre-fixme[61]: `new_det_fa_inds` is undefined, or not always defined.COMMENT
HIGHsam3/model/sam3_video_base.py907 # pyre-fixme[61]: `new_det_obj_ids` is undefined, or not always defined.COMMENT
HIGHsam3/model/sam3_video_base.py909 # pyre-fixme[61]: `new_det_gpu_ids` is undefined, or not always defined.COMMENT
HIGHsam3/model/sam3_video_base.py911 # pyre-fixme[61]: `unmatched_trk_obj_ids` is undefined, or not alwaysCOMMENT
HIGHsam3/model/sam3_video_base.py914 # pyre-fixme[61]: `det_to_matched_trk_obj_ids` is undefined, or notCOMMENT
HIGHsam3/model/sam3_video_base.py917 # pyre-fixme[61]: `obj_ids_newly_removed` is undefined, or not alwaysCOMMENT
HIGHsam3/model/sam3_video_base.py920 # pyre-fixme[61]: `num_obj_dropped_due_to_limit` is undefined, or notCOMMENT
HIGHsam3/model/sam3_video_base.py923 # pyre-fixme[61]: `trk_id_to_max_iou_high_conf_det` is undefined, or notCOMMENT
HIGHsam3/model/sam3_video_base.py991 # pyre-fixme[61]: `trk_id_to_max_iou_high_conf_det` is undefined, or notCOMMENT
HIGHsam3/model/sam3_video_base.py1001 # pyre-fixme[61]: `trk_id_to_max_iou_high_conf_det` is undefined, orCOMMENT
HIGHsam3/model/sam3_video_base.py1040 # pyre-fixme[61]: `new_det_obj_ids` is undefined, or not always defined.COMMENT
HIGHsam3/model/sam3_video_base.py1041 # pyre-fixme[61]: `new_det_gpu_ids` is undefined, or not always defined.COMMENT
HIGHsam3/model/sam3_video_base.py1048 # pyre-fixme[61]: `obj_ids_newly_removed` is undefined, or not alwaysCOMMENT
HIGHsam3/model/sam3_video_base.py1052 # pyre-fixme[61]: `obj_ids_newly_removed` is undefined, or notCOMMENT
HIGHsam3/model/sam3_video_base.py1055 # pyre-fixme[61]: `obj_ids_newly_removed` is undefined, or notCOMMENT
HIGHsam3/model/sam3_video_base.py1068 # pyre-fixme[61]: `new_det_obj_ids` is undefined, or not always defined.COMMENT
HIGHsam3/model/sam3_video_base.py1071 # pyre-fixme[61]: `new_det_obj_ids` is undefined, or not always defined.COMMENT
HIGHsam3/model/sam3_video_base.py1072 # pyre-fixme[61]: `new_det_fa_inds` is undefined, or not always defined.COMMENT
HIGHsam3/model/sam3_video_base.py1078 # pyre-fixme[61]: `new_det_obj_ids` is undefined, or not always defined.COMMENT
HIGHsam3/model/sam3_video_base.py1079 # pyre-fixme[61]: `new_det_fa_inds` is undefined, or not always defined.COMMENT
10 more matches not shown…
Hyper-Verbose Identifiers270 hits · 251 pts
SeverityFileLineSnippetContext
LOWtest/test_io_utils.py30 def test_mov_extension_routes_to_video_loader(CODE
LOWtest/test_io_utils.py43 def test_extensionless_oil_path_routes_to_video_loader(CODE
LOWtest/test_io_utils.py57 def test_extensionless_bare_hash_routes_to_video_loader(CODE
LOWtest/test_io_utils.py71 def test_extensionless_path_raises_on_decode_failure(CODE
LOWtest/test_io_utils.py86 def test_directory_routes_to_image_folder_loader(CODE
LOWtest/test_io_utils.py111 def test_unknown_extension_routes_to_video_loader(CODE
LOWscripts/extract_roboflow_vl100_results.py201def extract_config_parameters(config_path: str, keys: List[str]) -> Dict[str, Any]:CODE
LOWscripts/extract_roboflow_vl100_results.py275def analyze_experiment_results(config_path: str) -> None:CODE
LOWscripts/eval/veval/saco_yt1b_frame_prep_util.py59 def _get_yt_video_id_map_info(self):CODE
LOWscripts/eval/veval/saco_yt1b_downloader.py19def download_and_extract_frames(saco_yt1b_id, args):CODE
LOWscripts/eval/silver/download_preprocess_nga.py62def remove_non_compliant_image(item, output_folder):CODE
LOWsam3/visualization_utils.py207def visualize_formatted_frame_output(CODE
LOWsam3/visualization_utils.py512def prepare_masks_for_visualization(frame_to_output):CODE
LOWsam3/visualization_utils.py523def convert_coco_to_masklet_format(CODE
LOWsam3/visualization_utils.py584def save_side_by_side_visualization(img, gt_anns, pred_anns, noun_phrase):CODE
LOWsam3/visualization_utils.py695def get_all_annotations_for_frame(CODE
LOWsam3/model_builder.py66def _create_position_encoding(precompute_resolution=None):CODE
LOWsam3/model_builder.py125def _create_transformer_encoder(use_fa3=False) -> TransformerEncoderFusion:CODE
LOWsam3/model_builder.py165def _create_transformer_decoder(use_fa3=False) -> TransformerDecoder:CODE
LOWsam3/model_builder.py203def _create_dot_product_scoring():CODE
LOWsam3/model_builder.py217def _create_segmentation_head(compile_mode=None, use_fa3=False):CODE
LOWsam3/model_builder.py344def _create_tracker_maskmem_backbone():CODE
LOWsam3/model_builder.py380def _create_tracker_transformer():CODE
LOWsam3/model_builder.py823def _create_multiplex_maskmem_backbone(multiplex_count=16):CODE
LOWsam3/model_builder.py863def _create_multiplex_transformer(use_fa3=False, use_rope_real=False):CODE
LOWsam3/model_builder.py920def _create_multiplex_tri_backbone(CODE
LOWsam3/sam/mask_decoder.py263 def _dynamic_multimask_via_stability(self, all_mask_logits, all_iou_scores):CODE
LOWsam3/agent/agent_core.py56def _prune_messages_for_next_round(CODE
LOWsam3/agent/inference.py11def run_single_image_inference(CODE
LOWsam3/agent/helpers/boxes.py408def pairwise_point_box_distance(points: torch.Tensor, boxes: Boxes):CODE
LOWsam3/agent/helpers/masks.py44def rasterize_polygons_within_box(CODE
LOWsam3/agent/helpers/mask_overlap_removal.py67def _decode_masks_to_torch_bool(pred_masks: List, h: int, w: int) -> torch.Tensor:CODE
LOWsam3/agent/helpers/visualizer.py418 def draw_instance_predictions(self, predictions):CODE
LOWsam3/agent/helpers/visualizer.py824 def overlay_rotated_instances(self, boxes=None, labels=None, assigned_colors=None):CODE
LOWsam3/agent/helpers/visualizer.py866 def draw_and_connect_keypoints(self, keypoints):CODE
LOWsam3/agent/helpers/visualizer.py1119 def draw_rotated_box_with_label(CODE
LOWsam3/agent/helpers/visualizer.py1294 def draw_binary_mask_with_number(CODE
LOWsam3/model/sam3_tracker_base.py465 def _prepare_backbone_features(self, backbone_out):CODE
LOWsam3/model/sam3_tracker_base.py481 def _prepare_backbone_features_per_frame(self, img_batch, img_ids):CODE
LOWsam3/model/sam3_tracker_base.py559 def _prepare_memory_conditioned_features(CODE
LOWsam3/model/sam3_tracker_base.py1115 def _apply_non_overlapping_constraints(self, pred_masks):CODE
LOWsam3/model/vl_combiner.py87 def _forward_image_no_act_ckpt(self, samples):CODE
LOWsam3/model/vl_combiner.py135 def _forward_text_no_ack_ckpt(CODE
LOWsam3/model/vl_combiner.py209 def _forward_image_tri_no_act_ckpt(CODE
LOWsam3/model/sam3_multiplex_detector_utils.py185def _batched_generic_nms_mask(CODE
LOWsam3/model/sam3_multiplex_base.py374 def all_gather_python_obj_cpu(self, object_list, python_obj):CODE
LOWsam3/model/sam3_multiplex_base.py477 def _det_track_one_frame_impl(CODE
LOWsam3/model/sam3_multiplex_base.py647 def run_backbone_and_detection(CODE
LOWsam3/model/sam3_multiplex_base.py1019 def run_tracker_update_planning_phase(CODE
LOWsam3/model/sam3_multiplex_base.py1425 def _suppress_overlapping_based_on_recent_occlusion(CODE
LOWsam3/model/sam3_multiplex_base.py1517 def _create_planning_metadata(self, tracker_metadata_prev):CODE
LOWsam3/model/sam3_multiplex_base.py1527 def _post_execution_phase_hook(self, tracker_states_local, tracker_metadata_new):CODE
LOWsam3/model/sam3_multiplex_base.py1636 def _get_objects_to_suppress_based_on_most_recently_occluded(CODE
LOWsam3/model/sam3_multiplex_base.py1732 def _propogate_tracker_one_frame_local_gpu(CODE
LOWsam3/model/sam3_multiplex_base.py2830 def update_masklet_confirmation_status(CODE
LOWsam3/model/box_ops.py148def fast_diag_generalized_box_iou(boxes1, boxes2):CODE
LOWsam3/model/sam3_tracking_predictor.py1333 def _suppress_object_pw_area_shrinkage(self, pred_masks):CODE
LOWsam3/model/sam3_tracking_predictor.py1349 def _apply_object_wise_non_overlapping_constraints(CODE
LOWsam3/model/sam3_tracking_predictor.py465 def _get_orig_video_res_output(self, inference_state, any_res_masks):CODE
LOWsam3/model/sam3_tracking_predictor.py492 def _consolidate_temp_output_across_obj(CODE
210 more matches not shown…
Deep Nesting142 hits · 126 pts
SeverityFileLineSnippetContext
LOWscripts/extract_odinw_results.py52CODE
LOWscripts/eval/silver/download_fathomnet.py17CODE
LOWscripts/eval/silver/download_videos.py92CODE
LOWscripts/eval/silver/download_videos.py246CODE
LOWsam3/visualization_utils.py207CODE
LOWsam3/visualization_utils.py682CODE
LOWsam3/model_builder.py1070CODE
LOWsam3/agent/client_llm.py37CODE
LOWsam3/agent/client_llm.py131CODE
LOWsam3/agent/agent_core.py39CODE
LOWsam3/agent/agent_core.py124CODE
LOWsam3/agent/helpers/boxes.py48CODE
LOWsam3/agent/helpers/masks.py369CODE
LOWsam3/agent/helpers/visualizer.py1294CODE
LOWsam3/model/decoder.py430CODE
LOWsam3/model/data_misc.py228CODE
LOWsam3/model/sam3_tracker_base.py559CODE
LOWsam3/model/sam3_multiplex_base.py433CODE
LOWsam3/model/sam3_multiplex_base.py868CODE
LOWsam3/model/sam3_multiplex_base.py1019CODE
LOWsam3/model/sam3_multiplex_base.py1636CODE
LOWsam3/model/sam3_multiplex_base.py1732CODE
LOWsam3/model/sam3_multiplex_base.py2502CODE
LOWsam3/model/sam3_base_predictor.py50CODE
LOWsam3/model/sam3_tracking_predictor.py492CODE
LOWsam3/model/sam3_multiplex_tracking.py45CODE
LOWsam3/model/sam3_multiplex_tracking.py301CODE
LOWsam3/model/sam3_multiplex_tracking.py1363CODE
LOWsam3/model/sam3_multiplex_tracking.py1874CODE
LOWsam3/model/sam3_multiplex_tracking.py2296CODE
LOWsam3/model/sam3_multiplex_tracking.py2539CODE
LOWsam3/model/sam3_multiplex_tracking.py2747CODE
LOWsam3/model/sam3_multiplex_tracking.py3162CODE
LOWsam3/model/vitdet.py969CODE
LOWsam3/model/vitdet.py1033CODE
LOWsam3/model/sam3_image.py387CODE
LOWsam3/model/model_misc.py90CODE
LOWsam3/model/model_misc.py1062CODE
LOWsam3/model/sam3_video_inference.py253CODE
LOWsam3/model/sam3_video_inference.py653CODE
LOWsam3/model/sam3_video_inference.py1000CODE
LOWsam3/model/sam3_video_inference.py1226CODE
LOWsam3/model/sam3_video_inference.py1404CODE
LOWsam3/model/act_ckpt_utils.py19CODE
LOWsam3/model/act_ckpt_utils.py49CODE
LOWsam3/model/necks.py16CODE
LOWsam3/model/necks.py134CODE
LOWsam3/model/multiplex_utils.py149CODE
LOWsam3/model/sam3_multiplex_detector.py861CODE
LOWsam3/model/io_utils.py128CODE
LOWsam3/model/io_utils.py248CODE
LOWsam3/model/io_utils.py641CODE
LOWsam3/model/io_utils.py654CODE
LOWsam3/model/sam3_video_predictor.py256CODE
LOWsam3/model/video_tracking_multiplex.py656CODE
LOWsam3/model/video_tracking_multiplex.py935CODE
LOWsam3/model/video_tracking_multiplex.py1304CODE
LOWsam3/model/video_tracking_multiplex.py1649CODE
LOWsam3/model/video_tracking_multiplex.py1950CODE
LOWsam3/model/video_tracking_multiplex.py2863CODE
82 more matches not shown…
Structural Annotation Overuse95 hits · 115 pts
SeverityFileLineSnippetContext
LOWsam3/visualization_utils.py25 # Step 1: Random RGB samplesCOMMENT
LOWsam3/visualization_utils.py28 # Step 2: Convert to LAB for perceptual uniformityCOMMENT
LOWsam3/visualization_utils.py32 # Step 3: k-means clustering in LABCOMMENT
LOWsam3/visualization_utils.py38 # Step 4: Convert LAB back to RGBCOMMENT
LOWsam3/model/sam3_tracker_base.py584 # Step 1: condition the visual features of the current frame on previous memoriesCOMMENT
LOWsam3/model/sam3_tracker_base.py778 # Step 2: Concatenate the memories and forward through the transformer encoderCOMMENT
LOWsam3/model/sam3_multiplex_base.py503 # Step 1: run backbone and FA in a distributed manner -- this is done via Sam3MultiplexDetector,COMMENT
LOWsam3/model/sam3_multiplex_base.py520 # Step 2: each GPU propagates its local SAM2 states to get the SAM2 prediction masks.COMMENT
LOWsam3/model/sam3_multiplex_base.py554 # Step 3: based on detection outputs and the propagated SAM2 prediction masks, we make plansCOMMENT
LOWsam3/model/sam3_multiplex_base.py583 # Step 4: based on `sam2_update_plan`, each GPU executes the update w.r.t. its local SAM2 inference statesCOMMENT
LOWsam3/model/sam3_multiplex_base.py599 # Step 5: finally, build the outputs for this frame (it only needs to be done on GPU 0 sinceCOMMENT
LOWsam3/model/sam3_multiplex_base.py658 # Step 1: if text feature is not cached in `feature_cache`, compute and cache itCOMMENT
LOWsam3/model/sam3_multiplex_base.py670 # Step 2: run backbone, FA detection, and post-processing with NMSCOMMENT
LOWsam3/model/sam3_multiplex_base.py758 # Step 3: build SAM2 backbone features and store them in `feature_cache`COMMENT
LOWsam3/model/sam3_multiplex_base.py820 # Step 1: propagate the local SAM2 states to get the current frame's predictionCOMMENT
LOWsam3/model/sam3_multiplex_base.py837 # Step 2: all-gather `low_res_masks_local` into `low_res_masks_global`COMMENT
LOWsam3/model/sam3_multiplex_base.py1039 # Step 1: make the update plan and resolve heuristics on GPU 0COMMENT
LOWsam3/model/sam3_multiplex_base.py1179 # Step 4: Run SAM2 memory encoder on the current frame's prediction masksCOMMENT
LOWsam3/model/sam3_multiplex_base.py1225 # Step 4: update the SAM2 metadata based on the update planCOMMENT
LOWsam3/model/sam3_multiplex_base.py2405 # Step 1: log the frame index where each object ID first appearsCOMMENT
LOWsam3/model/sam3_multiplex_base.py2435 # Step 2: removed tracks that has not matched with detections for `hotstart_unmatch_thresh` frames with hotstartCOMMENT
LOWsam3/model/sam3_multiplex_base.py2465 # Step 3: removed tracks that overlaps with another track for `hotstart_dup_thresh` framesCOMMENT
LOWsam3/model/sam3_tracking_predictor.py1343 # NOTE: The output of this function can be a no op if none of the masks shrinked by a large factor.COMMENT
LOWsam3/model/sam3_tracking_predictor.py1205 # Step 0: clear the input on those frames where this object id has point or mask inputCOMMENT
LOWsam3/model/sam3_tracking_predictor.py1220 # Step 1: Update the object id mapping (note that it must be done after Step 0,COMMENT
LOWsam3/model/sam3_tracking_predictor.py1234 # Step 2: For per-object tensor storage, we shift their obj_idx in the dict keys.COMMENT
LOWsam3/model/sam3_tracking_predictor.py1250 # Step 3: For packed tensor storage, we index the remaining ids and rebuild the per-object slices.COMMENT
LOWsam3/model/sam3_tracking_predictor.py1277 # Step 4: Further collect the outputs on those frames in `obj_input_frames_inds`, whichCOMMENT
LOWsam3/model/sam3_multiplex_tracking.py3344 # Step 2: Remove the object from the source stateCOMMENT
LOWsam3/model/sam3_multiplex_tracking.py3349 # Step 3: Create a new singleton inference stateCOMMENT
LOWsam3/model/sam3_multiplex_tracking.py3357 # Step 4: Set up the singleton state structure for the extracted objectCOMMENT
LOWsam3/model/sam3_multiplex_tracking.py3363 # Step 5: Restore all the extracted stateCOMMENT
LOWsam3/model/sam3_multiplex_tracking.py3428 # Step 8: Add the new singleton state to the listCOMMENT
LOWsam3/model/sam3_multiplex_tracking.py3431 # Step 9: If the source state is now empty, remove itCOMMENT
LOWsam3/model/sam3_multiplex_tracking.py3186 # Step 1: Extract all the object's state data before removing itCOMMENT
LOWsam3/model/sam3_multiplex_tracking.py3384 # Step 6: Rebuild the consolidated output_dict for the singleton stateCOMMENT
LOWsam3/model/sam3_multiplex_tracking.py3414 # Step 7: Copy other important state if it existsCOMMENT
LOWsam3/model/sam3_image.py752 # Step 1: fetch the detector outputs in the current chunk from bufferCOMMENT
LOWsam3/model/sam3_image.py781 # Step 2: remove detection outputs of the previous chunk from cache to save GPU memoryCOMMENT
LOWsam3/model/sam3_image.py795 # Step 3: compute and cache detection outputs of the next chunk ahead of timeCOMMENT
LOWsam3/model/sam3_multiplex_detector.py408 # Step 1: fetch the FA outputs in the current chunk from bufferCOMMENT
LOWsam3/model/sam3_multiplex_detector.py450 # Step 2: remove FA outputs of the previous chunk from cache to save GPU memoryCOMMENT
LOWsam3/model/sam3_multiplex_detector.py464 # Step 3: compute and cache FA outputs of the next chunk ahead of timeCOMMENT
LOWsam3/model/video_tracking_multiplex.py3204 # Step 2: Encode the incoming masksCOMMENT
LOWsam3/model/video_tracking_multiplex.py3213 # Step 3: Merge the existing state with new encoded featuresCOMMENT
LOWsam3/model/video_tracking_multiplex.py3279 # Step 4: Update the set of conditioning objects at this frame.COMMENT
LOWsam3/model/video_tracking_multiplex.py3282 # Step 5: Re-encode the spatial memory if neededCOMMENT
LOWsam3/model/video_tracking_multiplex.py3346 # Step 1: Encode the incoming masksCOMMENT
LOWsam3/model/video_tracking_multiplex.py3355 # Step 2: Merge the existing state with new encoded featuresCOMMENT
LOWsam3/model/video_tracking_multiplex.py3410 # Step 3: Update the set of conditioning objects at this frameCOMMENT
LOWsam3/model/video_tracking_multiplex.py3413 # Step 4: Re-encode the spatial memory if neededCOMMENT
LOWsam3/model/video_tracking_multiplex.py1341 # Step 1: condition the visual features of the current frame on previous memoriesCOMMENT
LOWsam3/model/video_tracking_multiplex.py1593 # Step 2: Concatenate the memories and forward through the transformer encoderCOMMENT
LOWsam3/model/video_tracking_multiplex.py3191 # Step 1: Inform the multiplex state that we are adding new objectsCOMMENT
LOWsam3/model/video_tracking_multiplex_demo.py466 # Step 3: Create new singleton inference stateCOMMENT
LOWsam3/model/video_tracking_multiplex_demo.py474 # Step 4: Set up singleton state structureCOMMENT
LOWsam3/model/video_tracking_multiplex_demo.py496 # Step 5: Create new singleton multiplex state (even for 1 object, needed for obj_ptr)COMMENT
LOWsam3/model/video_tracking_multiplex_demo.py506 # Step 6: Remux extracted tensors into the singleton multiplex spaceCOMMENT
LOWsam3/model/video_tracking_multiplex_demo.py207 # Step 1: Extract all object data BEFORE removing itCOMMENT
LOWsam3/model/video_tracking_multiplex_demo.py448 # Step 2: Remove the object from source stateCOMMENT
35 more matches not shown…
Verbosity Indicators93 hits · 114 pts
SeverityFileLineSnippetContext
LOWsam3/visualization_utils.py25 # Step 1: Random RGB samplesCOMMENT
LOWsam3/visualization_utils.py28 # Step 2: Convert to LAB for perceptual uniformityCOMMENT
LOWsam3/visualization_utils.py32 # Step 3: k-means clustering in LABCOMMENT
LOWsam3/visualization_utils.py38 # Step 4: Convert LAB back to RGBCOMMENT
LOWsam3/model/sam3_tracker_base.py584 # Step 1: condition the visual features of the current frame on previous memoriesCOMMENT
LOWsam3/model/sam3_tracker_base.py778 # Step 2: Concatenate the memories and forward through the transformer encoderCOMMENT
LOWsam3/model/sam3_multiplex_base.py503 # Step 1: run backbone and FA in a distributed manner -- this is done via Sam3MultiplexDetector,COMMENT
LOWsam3/model/sam3_multiplex_base.py520 # Step 2: each GPU propagates its local SAM2 states to get the SAM2 prediction masks.COMMENT
LOWsam3/model/sam3_multiplex_base.py554 # Step 3: based on detection outputs and the propagated SAM2 prediction masks, we make plansCOMMENT
LOWsam3/model/sam3_multiplex_base.py583 # Step 4: based on `sam2_update_plan`, each GPU executes the update w.r.t. its local SAM2 inference statesCOMMENT
LOWsam3/model/sam3_multiplex_base.py599 # Step 5: finally, build the outputs for this frame (it only needs to be done on GPU 0 sinceCOMMENT
LOWsam3/model/sam3_multiplex_base.py658 # Step 1: if text feature is not cached in `feature_cache`, compute and cache itCOMMENT
LOWsam3/model/sam3_multiplex_base.py670 # Step 2: run backbone, FA detection, and post-processing with NMSCOMMENT
LOWsam3/model/sam3_multiplex_base.py758 # Step 3: build SAM2 backbone features and store them in `feature_cache`COMMENT
LOWsam3/model/sam3_multiplex_base.py820 # Step 1: propagate the local SAM2 states to get the current frame's predictionCOMMENT
LOWsam3/model/sam3_multiplex_base.py837 # Step 2: all-gather `low_res_masks_local` into `low_res_masks_global`COMMENT
LOWsam3/model/sam3_multiplex_base.py1039 # Step 1: make the update plan and resolve heuristics on GPU 0COMMENT
LOWsam3/model/sam3_multiplex_base.py1179 # Step 4: Run SAM2 memory encoder on the current frame's prediction masksCOMMENT
LOWsam3/model/sam3_multiplex_base.py1225 # Step 4: update the SAM2 metadata based on the update planCOMMENT
LOWsam3/model/sam3_multiplex_base.py2405 # Step 1: log the frame index where each object ID first appearsCOMMENT
LOWsam3/model/sam3_multiplex_base.py2435 # Step 2: removed tracks that has not matched with detections for `hotstart_unmatch_thresh` frames with hotstartCOMMENT
LOWsam3/model/sam3_multiplex_base.py2465 # Step 3: removed tracks that overlaps with another track for `hotstart_dup_thresh` framesCOMMENT
LOWsam3/model/sam3_tracking_predictor.py1205 # Step 0: clear the input on those frames where this object id has point or mask inputCOMMENT
LOWsam3/model/sam3_tracking_predictor.py1220 # Step 1: Update the object id mapping (note that it must be done after Step 0,COMMENT
LOWsam3/model/sam3_tracking_predictor.py1234 # Step 2: For per-object tensor storage, we shift their obj_idx in the dict keys.COMMENT
LOWsam3/model/sam3_tracking_predictor.py1250 # Step 3: For packed tensor storage, we index the remaining ids and rebuild the per-object slices.COMMENT
LOWsam3/model/sam3_tracking_predictor.py1277 # Step 4: Further collect the outputs on those frames in `obj_input_frames_inds`, whichCOMMENT
LOWsam3/model/sam3_multiplex_tracking.py3344 # Step 2: Remove the object from the source stateCOMMENT
LOWsam3/model/sam3_multiplex_tracking.py3349 # Step 3: Create a new singleton inference stateCOMMENT
LOWsam3/model/sam3_multiplex_tracking.py3357 # Step 4: Set up the singleton state structure for the extracted objectCOMMENT
LOWsam3/model/sam3_multiplex_tracking.py3363 # Step 5: Restore all the extracted stateCOMMENT
LOWsam3/model/sam3_multiplex_tracking.py3428 # Step 8: Add the new singleton state to the listCOMMENT
LOWsam3/model/sam3_multiplex_tracking.py3431 # Step 9: If the source state is now empty, remove itCOMMENT
LOWsam3/model/sam3_multiplex_tracking.py3186 # Step 1: Extract all the object's state data before removing itCOMMENT
LOWsam3/model/sam3_multiplex_tracking.py3384 # Step 6: Rebuild the consolidated output_dict for the singleton stateCOMMENT
LOWsam3/model/sam3_multiplex_tracking.py3414 # Step 7: Copy other important state if it existsCOMMENT
LOWsam3/model/sam3_image.py752 # Step 1: fetch the detector outputs in the current chunk from bufferCOMMENT
LOWsam3/model/sam3_image.py781 # Step 2: remove detection outputs of the previous chunk from cache to save GPU memoryCOMMENT
LOWsam3/model/sam3_image.py795 # Step 3: compute and cache detection outputs of the next chunk ahead of timeCOMMENT
LOWsam3/model/sam3_multiplex_detector.py408 # Step 1: fetch the FA outputs in the current chunk from bufferCOMMENT
LOWsam3/model/sam3_multiplex_detector.py450 # Step 2: remove FA outputs of the previous chunk from cache to save GPU memoryCOMMENT
LOWsam3/model/sam3_multiplex_detector.py464 # Step 3: compute and cache FA outputs of the next chunk ahead of timeCOMMENT
LOWsam3/model/video_tracking_multiplex.py3204 # Step 2: Encode the incoming masksCOMMENT
LOWsam3/model/video_tracking_multiplex.py3213 # Step 3: Merge the existing state with new encoded featuresCOMMENT
LOWsam3/model/video_tracking_multiplex.py3279 # Step 4: Update the set of conditioning objects at this frame.COMMENT
LOWsam3/model/video_tracking_multiplex.py3282 # Step 5: Re-encode the spatial memory if neededCOMMENT
LOWsam3/model/video_tracking_multiplex.py3346 # Step 1: Encode the incoming masksCOMMENT
LOWsam3/model/video_tracking_multiplex.py3355 # Step 2: Merge the existing state with new encoded featuresCOMMENT
LOWsam3/model/video_tracking_multiplex.py3410 # Step 3: Update the set of conditioning objects at this frameCOMMENT
LOWsam3/model/video_tracking_multiplex.py3413 # Step 4: Re-encode the spatial memory if neededCOMMENT
LOWsam3/model/video_tracking_multiplex.py1341 # Step 1: condition the visual features of the current frame on previous memoriesCOMMENT
LOWsam3/model/video_tracking_multiplex.py1593 # Step 2: Concatenate the memories and forward through the transformer encoderCOMMENT
LOWsam3/model/video_tracking_multiplex.py3191 # Step 1: Inform the multiplex state that we are adding new objectsCOMMENT
LOWsam3/model/video_tracking_multiplex_demo.py466 # Step 3: Create new singleton inference stateCOMMENT
LOWsam3/model/video_tracking_multiplex_demo.py474 # Step 4: Set up singleton state structureCOMMENT
LOWsam3/model/video_tracking_multiplex_demo.py496 # Step 5: Create new singleton multiplex state (even for 1 object, needed for obj_ptr)COMMENT
LOWsam3/model/video_tracking_multiplex_demo.py506 # Step 6: Remux extracted tensors into the singleton multiplex spaceCOMMENT
LOWsam3/model/video_tracking_multiplex_demo.py207 # Step 1: Extract all object data BEFORE removing itCOMMENT
LOWsam3/model/video_tracking_multiplex_demo.py448 # Step 2: Remove the object from source stateCOMMENT
LOWsam3/model/video_tracking_multiplex_demo.py557 # Step 1: Add object mappings at new indexCOMMENT
33 more matches not shown…
Unused Imports96 hits · 90 pts
SeverityFileLineSnippetContext
LOWscripts/eval/silver/utils.py8CODE
LOWscripts/eval/silver/utils.py12CODE
LOWscripts/eval/silver/utils.py13CODE
LOWscripts/eval/silver/utils.py16CODE
LOWscripts/eval/silver/download_fathomnet.py6CODE
LOWscripts/eval/silver/download_videos.py7CODE
LOWscripts/eval/silver/download_videos.py17CODE
LOWsam3/model_builder.py5CODE
LOWsam3/model_builder.py47CODE
LOWsam3/__init__.py5CODE
LOWsam3/__init__.py5CODE
LOWsam3/sam/__init__.py4CODE
LOWsam3/sam/__init__.py5CODE
LOWsam3/sam/__init__.py6CODE
LOWsam3/agent/helpers/rotated_boxes.py5CODE
LOWsam3/agent/helpers/rotated_boxes.py5CODE
LOWsam3/agent/helpers/rotated_boxes.py5CODE
LOWsam3/agent/helpers/rotated_boxes.py5CODE
LOWsam3/model/decoder.py11CODE
LOWsam3/model/data_misc.py8CODE
LOWsam3/model/data_misc.py9CODE
LOWsam3/model/data_misc.py10CODE
LOWsam3/model/data_misc.py11CODE
LOWsam3/model/data_misc.py11CODE
LOWsam3/model/vl_combiner.py15CODE
LOWsam3/model/sam3_multiplex_base.py1CODE
LOWsam3/model/sam3_multiplex_base.py6CODE
LOWsam3/model/sam3_multiplex_base.py8CODE
LOWsam3/model/sam3_multiplex_base.py28CODE
LOWsam3/model/sam3_base_predictor.py15CODE
LOWsam3/model/sam3_base_predictor.py226CODE
LOWsam3/model/sam3_multiplex_tracking.py2CODE
LOWsam3/model/sam3_multiplex_tracking.py3CODE
LOWsam3/model/sam3_multiplex_tracking.py23CODE
LOWsam3/model/sam3_multiplex_tracking.py30CODE
LOWsam3/model/sam3_multiplex_tracking.py33CODE
LOWsam3/model/sam3_multiplex_video_predictor.py13CODE
LOWsam3/model/sam3_multiplex_video_predictor.py13CODE
LOWsam3/model/vitdet.py17CODE
LOWsam3/model/encoder.py6CODE
LOWsam3/model/sam3_multiplex_detector.py10CODE
LOWsam3/model/sam3_multiplex_detector.py10CODE
LOWsam3/model/sam3_video_predictor.py6CODE
LOWsam3/model/sam3_video_predictor.py12CODE
LOWsam3/model/sam3_video_predictor.py15CODE
LOWsam3/model/sam3_video_predictor.py15CODE
LOWsam3/model/sam3_video_base.py13CODE
LOWsam3/model/sam3_video_base.py20CODE
LOWsam3/train/optim/optimizer.py9CODE
LOWsam3/train/data/sam3_video_dataset.py6CODE
LOWsam3/train/data/sam3_video_dataset.py7CODE
LOWsam3/train/data/sam3_video_dataset.py10CODE
LOWsam3/train/data/sam3_video_dataset.py11CODE
LOWsam3/train/data/sam3_video_dataset.py13CODE
LOWsam3/train/data/sam3_video_dataset.py14CODE
LOWsam3/train/data/sam3_video_dataset.py14CODE
LOWsam3/train/data/sam3_video_dataset.py14CODE
LOWsam3/train/data/sam3_video_dataset.py14CODE
LOWsam3/train/data/sam3_video_dataset.py14CODE
LOWsam3/train/data/sam3_video_dataset.py14CODE
36 more matches not shown…
Self-Referential Comments26 hits · 70 pts
SeverityFileLineSnippetContext
MEDIUMsam3/model_builder.py462 # Create the Tracker moduleCOMMENT
MEDIUMsam3/model_builder.py635 # Create the SAM3 modelCOMMENT
MEDIUMsam3/model_builder.py713 # Create main dot product scoringCOMMENT
MEDIUMsam3/agent/client_llm.py83 # Create the proper image_url structure with base64 dataCOMMENT
MEDIUMsam3/agent/agent_core.py442 # Create a new filename based on the original path to avoid filename length issuesCOMMENT
MEDIUMsam3/model/sam3_multiplex_base.py318 # Initialize profiling variablesCOMMENT
MEDIUMsam3/model/sam3_multiplex_base.py2694 # Create a new state if neededCOMMENT
MEDIUMsam3/model/sam3_multiplex_tracking.py1757 """This method is only used for benchmark eval (not used in the demo)."""STRING
MEDIUMsam3/model/sam1_task_predictor.py181 """This function is very similar to predict(...), however it is used for batched mode, when the model is expecteSTRING
MEDIUMsam3/model/sam3_video_inference.py910 """This method is only used for benchmark eval (not used in the demo)."""STRING
MEDIUMsam3/model/act_ckpt_utils.py60 # Create a mapping of parameter names to their default valuesCOMMENT
MEDIUMsam3/model/multiplex_utils.py353 # Create a transition matrix for muxingCOMMENT
MEDIUMsam3/model/multiplex_utils.py361 # Create a transition matrix for demuxingCOMMENT
MEDIUMsam3/model/video_tracking_multiplex.py1713 # Create a 1D conditioning mask on GPU and broadcast itCOMMENT
MEDIUMsam3/model/sam3_video_base.py2053 """This method is only used for benchmark eval (not used in the demo)."""STRING
MEDIUMsam3/train/transforms/basic_for_api.py986 # Create a color jitter transformation paramsCOMMENT
MEDIUMsam3/train/transforms/basic_for_api.py1081 # Create a random affine transformationCOMMENT
MEDIUMsam3/train/transforms/basic_for_api.py1197 # Create a random crop transformationCOMMENT
MEDIUMsam3/train/transforms/basic_for_api.py1207 # Create a random crop transformationCOMMENT
MEDIUMsam3/eval/coco_reindex.py168 # Create a test COCO fileCOMMENT
MEDIUMsam3/eval/coco_eval_offline.py150 # Creating the result fileCOMMENT
MEDIUMsam3/eval/coco_eval.py201 # This function is used for "oracle" type evaluation.COMMENT
MEDIUMsam3/eval/demo_eval.py525 # This function is used for "oracle" type evaluation.COMMENT
MEDIUMsam3/eval/cgf1_eval.py666 # This function is used for "oracle" type evaluation.COMMENT
MEDIUMsam3/perflib/triton/connected_components.py78 # This function implements a disjoint-set unionCOMMENT
MEDIUMsam3/perflib/triton/connected_components.py93 # Define the mask for lanes that still need work in this iterationCOMMENT
Excessive Try-Catch Wrapping45 hits · 65 pts
SeverityFileLineSnippetContext
MEDIUMscripts/extract_roboflow_vl100_results.py173 print(f"Error: Failed to parse JSON in {file_path}: {e}")CODE
LOWscripts/extract_roboflow_vl100_results.py175 except Exception as e:CODE
MEDIUMscripts/extract_roboflow_vl100_results.py176 print(f"Error: Failed to read {file_path}: {e}")CODE
LOWscripts/extract_roboflow_vl100_results.py231 except Exception as e:CODE
MEDIUMscripts/extract_roboflow_vl100_results.py232 print(f"Error: Failed to parse config file {config_path}: {e}")CODE
LOWscripts/extract_roboflow_vl100_results.py376 except Exception as e:CODE
MEDIUMscripts/extract_roboflow_vl100_results.py377 print(f"Error analyzing {config_file}: {e}")CODE
MEDIUMscripts/extract_odinw_results.py76 print(f"Error reading {val_stats_path}: {e}")CODE
LOWscripts/bench_compile.py152 except Exception as e:CODE
LOWscripts/eval/veval/saco_yt1b_frame_prep_util.py119 except Exception as e:CODE
LOWscripts/eval/silver/utils.py65 except Exception:CODE
MEDIUMscripts/eval/silver/utils.py61def is_valid_image(img_path):CODE
MEDIUMscripts/eval/silver/download_fathomnet.py34 print(f"Error downloading {image.url}: {e}")CODE
LOWscripts/eval/silver/download_videos.py63 except Exception as e:CODE
LOWscripts/eval/silver/download_videos.py88 except Exception as e:CODE
MEDIUMscripts/eval/silver/download_videos.py67def download_youtube_video(youtube_id, output_path=None):CODE
LOWsam3/agent/client_llm.py32 except Exception as e:CODE
MEDIUMsam3/agent/client_llm.py33 print(f"Error converting image to base64: {e}")CODE
LOWsam3/agent/client_llm.py97 except Exception as e:CODE
LOWsam3/agent/client_llm.py126 except Exception as e:CODE
LOWsam3/agent/client_llm.py179 except Exception as e:CODE
LOWsam3/agent/client_llm.py206 except Exception as e:CODE
LOWsam3/agent/client_sam3.py137 except Exception as e:CODE
LOWsam3/agent/agent_core.py35 except Exception as e:CODE
LOWsam3/agent/helpers/mask_overlap_removal.py12except Exception:CODE
LOWsam3/model/io_utils.py186 except Exception as e:CODE
LOWsam3/model/io_utils.py425 except Exception as e:CODE
LOWsam3/model/io_utils.py670 except Exception as e:CODE
LOWsam3/model/io_utils.py683 except Exception as e:CODE
MEDIUMsam3/model/io_utils.py418def _load_frames():CODE
LOWsam3/model/sam3_video_predictor.py316 except Exception as e:CODE
LOWsam3/model/utils/sam2_utils.py67 except Exception as e:CODE
MEDIUMsam3/model/utils/sam2_utils.py63def _load_frames():CODE
LOWsam3/model/utils/sam1_utils.py108 except Exception as e:CODE
LOWsam3/train/train.py54 except Exception as e:CODE
LOWsam3/train/train.py117 except Exception as e:CODE
LOWsam3/eval/postprocessors.py199 except Exception as e:CODE
LOWsam3/eval/teta_eval_toolkit/eval.py69 except Exception as err:CODE
LOWsam3/eval/teta_eval_toolkit/datasets/_base_dataset.py228 except Exception:CODE
LOWsam3/eval/teta_eval_toolkit/datasets/_base_dataset.py236 except Exception:CODE
MEDIUMsam3/eval/teta_eval_toolkit/datasets/_base_dataset.py237 print("Error loading file: %s, printing traceback." % file)CODE
LOWsam3/eval/hota_eval_toolkit/trackeval/eval.py364 except Exception as err:CODE
LOW…/hota_eval_toolkit/trackeval/datasets/_base_dataset.py228 except Exception:CODE
LOW…/hota_eval_toolkit/trackeval/datasets/_base_dataset.py236 except Exception:CODE
MEDIUM…/hota_eval_toolkit/trackeval/datasets/_base_dataset.py237 print("Error loading file: %s, printing traceback." % file)CODE
AI Structural Patterns68 hits · 62 pts
SeverityFileLineSnippetContext
LOWsam3/model_builder.py573CODE
LOWsam3/model_builder.py676CODE
LOWsam3/model_builder.py937CODE
LOWsam3/model_builder.py1070CODE
LOWsam3/model_builder.py1244CODE
LOWsam3/sam/transformer.py265CODE
LOWsam3/sam/transformer.py360CODE
LOWsam3/sam/mask_decoder.py15CODE
LOWsam3/agent/helpers/masks.py237CODE
LOWsam3/agent/helpers/visualizer.py687CODE
LOWsam3/model/decoder.py1067CODE
LOWsam3/model/decoder.py82CODE
LOWsam3/model/decoder.py209CODE
LOWsam3/model/decoder.py430CODE
LOWsam3/model/decoder.py693CODE
LOWsam3/model/decoder.py852CODE
LOWsam3/model/decoder.py900CODE
LOWsam3/model/decoder.py965CODE
LOWsam3/model/decoder.py1143CODE
LOWsam3/model/sam3_tracker_base.py27CODE
LOWsam3/model/vl_combiner.py122CODE
LOWsam3/model/vl_combiner.py322CODE
LOWsam3/model/vl_combiner.py336CODE
LOWsam3/model/sam3_multiplex_base.py43CODE
LOWsam3/model/sam3_multiplex_base.py197CODE
LOWsam3/model/text_encoder_ve.py170CODE
LOWsam3/model/text_encoder_ve.py260CODE
LOWsam3/model/memory.py30CODE
LOWsam3/model/sam3_base_predictor.py149CODE
LOWsam3/model/sam3_tracking_predictor.py57CODE
LOWsam3/model/sam3_multiplex_tracking.py1655CODE
LOWsam3/model/sam3_multiplex_tracking.py2693CODE
LOWsam3/model/vitdet.py391CODE
LOWsam3/model/vitdet.py652CODE
LOWsam3/model/vitdet.py766CODE
LOWsam3/model/sam3_image.py39CODE
LOWsam3/model/model_misc.py90CODE
LOWsam3/model/model_misc.py475CODE
LOWsam3/model/multiplex_utils.py403CODE
LOWsam3/model/multiplex_utils.py425CODE
LOWsam3/model/sam3_multiplex_detector.py356CODE
LOWsam3/model/sam3_multiplex_detector.py661CODE
LOWsam3/model/geometry_encoders.py102CODE
LOWsam3/model/sam3_video_predictor.py26CODE
LOWsam3/model/video_tracking_multiplex.py97CODE
LOWsam3/model/video_tracking_multiplex.py3438CODE
LOWsam3/model/video_tracking_multiplex_demo.py2652CODE
LOWsam3/model/sam3_video_base.py266CODE
LOWsam3/model/multiplex_mask_decoder.py17CODE
LOWsam3/train/matcher.py445CODE
LOWsam3/train/trainer.py150CODE
LOWsam3/train/loss/sam3_loss.py38CODE
LOWsam3/train/loss/loss_fns.py267CODE
LOWsam3/train/loss/loss_fns.py576CODE
LOWsam3/train/transforms/point_sampling.py196CODE
LOWsam3/train/data/sam3_video_dataset.py29CODE
LOWsam3/train/data/sam3_image_dataset.py148CODE
LOWsam3/train/data/sam3_image_dataset.py443CODE
LOWsam3/eval/saco_veval_evaluators.py338CODE
LOWsam3/eval/postprocessors.py35CODE
8 more matches not shown…
Hallucination Indicators6 hits · 60 pts
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CRITICALsam3/model/sam3_multiplex_tracking.py1652 self.tracker.transformer.encoder.forward.set_logging(True)CODE
CRITICALsam3/model/sam3_video_inference.py836 self.tracker.transformer.encoder.forward.set_logging(True)CODE
CRITICALsam3/model/sam3_image_processor.py64 self.model.inst_interactive_predictor.model.sam_mask_decoder.conv_s0(CODE
CRITICALsam3/model/sam3_image_processor.py69 self.model.inst_interactive_predictor.model.sam_mask_decoder.conv_s1(CODE
CRITICALsam3/model/sam3_image_processor.py102 self.model.inst_interactive_predictor.model.sam_mask_decoder.conv_s0(CODE
CRITICALsam3/model/sam3_image_processor.py107 self.model.inst_interactive_predictor.model.sam_mask_decoder.conv_s1(CODE
Redundant / Tautological Comments32 hits · 41 pts
SeverityFileLineSnippetContext
LOWscripts/extract_roboflow_vl100_results.py317 # Print results summaryCOMMENT
LOWscripts/extract_odinw_results.py79 # Print resultsCOMMENT
LOWscripts/qualitative_test.py287 # Print resultsCOMMENT
LOWscripts/eval/veval/saco_yt1b_frame_prep_util.py93 # Check if the output file already existsCOMMENT
LOWsam3/agent/agent_core.py43 # Check if message has content (should be a list)COMMENT
LOWsam3/agent/agent_core.py47 # Check if content item is a dict with type "image"COMMENT
LOWsam3/agent/agent_core.py232 # Check if this text_prompt has been used beforeCOMMENT
LOWsam3/agent/inference.py39 # Check if output already exists and skipCOMMENT
LOWsam3/model/sam3_multiplex_detector_utils.py76 # Check if input is batched (has batch dimension)COMMENT
LOWsam3/model/sam3_multiplex_base.py2287 # Check if objects are within hotstart windowCOMMENT
LOWsam3/model/sam3_multiplex_tracking.py2794 # Check if this is a batched state (contains multiple objects)COMMENT
LOWsam3/model/act_ckpt_utils.py69 # Set arg to default value if it's not in kwargs. Useful for primitive types or args that default toCOMMENT
LOWsam3/model/multiplex_utils.py276 # Check if all objects in this bucket are removed or are paddingsCOMMENT
LOWsam3/model/video_tracking_multiplex.py3218 # Check if prev_output needs resolution adjustmentCOMMENT
LOWsam3/model/video_tracking_multiplex_demo.py221 # Check if this frame has valid data for this objectCOMMENT
LOWsam3/model/video_tracking_multiplex_demo.py706 # Check if singleton features are in multiplexed format and demux if neededCOMMENT
LOWsam3/model/video_tracking_multiplex_demo.py749 # Check if singleton pos_enc is in multiplexed format and demux if neededCOMMENT
LOWsam3/model/video_tracking_multiplex_demo.py878 # Check if singleton features are in multiplexed format and demux if neededCOMMENT
LOWsam3/model/video_tracking_multiplex_demo.py900 # Check if features are in multiplex format before demuxingCOMMENT
LOWsam3/model/video_tracking_multiplex_demo.py974 # Check if singleton pos_enc is in multiplexed format and demux if neededCOMMENT
LOWsam3/model/video_tracking_multiplex_demo.py990 # Check if pos_enc is in multiplex format before demuxingCOMMENT
LOWsam3/model/video_tracking_multiplex_demo.py2020 # Check if this object has point inputs on this frameCOMMENT
LOWsam3/model/video_tracking_multiplex_demo.py2030 # Check if this object has mask inputs on this frameCOMMENT
LOWsam3/model/video_tracking_multiplex_demo.py2119 # Check if this object index exists in temp/output dicts (it may not if object was just added)COMMENT
LOWsam3/model/video_tracking_multiplex_demo.py2214 # Check if we have ANY valid per-object outputsCOMMENT
LOWsam3/train/nms_helper.py9# Check if Numba is availableCOMMENT
LOWsam3/train/nms_helper.py280 if len(remaining_bboxes) > 0: # Check if there are any remaining boxesCODE
LOWsam3/train/trainer.py1058 # Check if there are any duplicatesCOMMENT
LOWsam3/train/transforms/filter_query_transforms.py228 # Check if height or width of bounding box is zeroCOMMENT
LOWsam3/eval/coco_reindex.py133 # Check if reindexing is neededCOMMENT
LOWsam3/eval/saco_veval_evaluators.py800 # Set category_id to 1 for class-agnosticCOMMENT
LOWsam3/perflib/triton/nms.py49 # Check if current box is still keptCOMMENT
Over-Commented Block46 hits · 40 pts
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LOWsam3/agent/helpers/rotated_boxes.py441 # A(-w / 2, -h / 2), B(w / 2, -h / 2), C(w / 2, h / 2), D(-w / 2, h / 2)COMMENT
LOWsam3/agent/helpers/rotated_boxes.py461 # For example,COMMENT
LOWsam3/agent/helpers/visualizer.py761 areas = np.asarray([x.area() for x in masks])COMMENT
LOWsam3/model/sam3_tracker_base.py41 # whether to output multiple (3) masks for the first click on initial conditioning framesCOMMENT
LOWsam3/model/sam3_multiplex_base.py201 ckpt_path=None,COMMENT
LOWsam3/model/sam3_multiplex_base.py1861 # obj_ids_local might not be sorted, which is problematic becauseCOMMENT
LOWsam3/model/sam3_base_predictor.py341 so the response reflects the post-cleanup state and the freedCOMMENT
LOWsam3/model/sam3_base_predictor.py361 #COMMENT
LOWsam3/model/sam3_tracking_predictor.py21 def __init__(COMMENT
LOWsam3/model/sam3_multiplex_tracking.py2941 sprinkle_removal_area=self.sprinkle_removal_area,COMMENT
LOWsam3/model/video_tracking_multiplex.py141 # we only cross-attend to the temporally closest `max_cond_frames_in_attn` conditioning frames in the encoder whCOMMENT
LOWsam3/model/video_tracking_multiplex.py161 # (instead of using the transformer encoder)COMMENT
LOWsam3/model/video_tracking_multiplex.py181 # whether to feed the previously predicted low-res mask logits as a mask prompt into the SAM mask decoder duringCOMMENT
LOWsam3/model/video_tracking_multiplex.py201 max_obj_ptrs_in_encoder: int = 16,COMMENT
LOWsam3/model/video_tracking_multiplex.py241 share_necks: bool = False, # share the interactive and sam2_backbone necksCOMMENT
LOWsam3/model/video_tracking_multiplex_demo.py3281 inference_state["output_dict_per_obj"] = {}COMMENT
LOWsam3/model/sam3_video_base.py901 )COMMENT
LOWsam3/model/utils/__init__.py1# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights ReservedCOMMENT
LOWsam3/train/loss/loss_fns.py721# def __init__(COMMENT
LOWsam3/train/loss/loss_fns.py741# Unlike `Masks`, here the "multistep_pred_masks" can have multiple channels, eachCOMMENT
LOWsam3/train/loss/loss_fns.py761# src_masks,COMMENT
LOWsam3/train/loss/loss_fns.py781COMMENT
LOWsam3/train/loss/loss_fns.py801# ):COMMENT
LOWsam3/train/loss/loss_fns.py821COMMENT
LOWsam3/train/loss/loss_fns.py841# target_masks = target_masks[keep]COMMENT
LOWsam3/train/loss/loss_fns.py861# loss_multimask = sigmoid_focal_loss(COMMENT
LOWsam3/train/loss/loss_fns.py881# device=loss_multimask.device,COMMENT
LOWsam3/train/loss/loss_fns.py901# loss_on_multimask=True,COMMENT
LOWsam3/train/loss/loss_fns.py921# else:COMMENT
LOWsam3/train/loss/loss_fns.py941# def __init__(COMMENT
LOWsam3/train/loss/loss_fns.py961# )COMMENT
LOWsam3/train/loss/sigmoid_focal_loss.py41 sig = tl.sigmoid(inputs)COMMENT
LOWsam3/train/configs/eval_base.yaml1# @package _global_COMMENT
LOW…m3/train/configs/roboflow_v100/roboflow_v100_eval.yaml101 pad_scale_pos: 1.0COMMENT
LOW…m3/train/configs/roboflow_v100/roboflow_v100_eval.yaml121 # loss_bbox: 5.0COMMENT
LOW…m3/train/configs/roboflow_v100/roboflow_v100_eval.yaml141 # compute_aux: falseCOMMENT
LOW…gs/roboflow_v100/roboflow_v100_full_ft_100_images.yaml101 pad_scale_pos: 1.0COMMENT
LOW…gs/roboflow_v100/roboflow_v100_full_ft_100_images.yaml121 # loss_bbox: 5.0COMMENT
LOW…gs/roboflow_v100/roboflow_v100_full_ft_100_images.yaml141 # compute_aux: falseCOMMENT
LOWsam3/train/data/sam3_video_dataset.py41 # the maximum number of find queries (for each frame) to keep in a video; if the datapointCOMMENT
LOWsam3/eval/coco_eval.py681 # def convert_instances_to_cpp(instances, is_det=False):COMMENT
LOWsam3/eval/coco_eval.py701 # detected_instances = [COMMENT
LOWsam3/eval/coco_eval.py721COMMENT
LOWsam3/eval/coco_eval.py901 returnCOMMENT
LOWsam3/eval/demo_eval.py581 outs[f"coco_eval_bbox_{prefix}{DEMO_METRICS[i]}"] = valueCOMMENT
LOWsam3/eval/demo_eval.py601 # self.coco_evals[0]["bbox"].statsCOMMENT
Cross-File Repetition6 hits · 30 pts
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HIGHsam3/model/sam3_multiplex_tracking.py0warm up the model by running a dummy inference to compile the model. this is useful to avoid the compilation overhead inSTRING
HIGHsam3/model/sam3_video_inference.py0warm up the model by running a dummy inference to compile the model. this is useful to avoid the compilation overhead inSTRING
HIGHsam3/model/video_tracking_multiplex_demo.py0warm up the model by running a dummy inference to compile the model. this is useful to avoid the compilation overhead inSTRING
HIGHsam3/model/sam3_multiplex_tracking.py0this method is only used for benchmark eval (not used in the demo).STRING
HIGHsam3/model/sam3_video_inference.py0this method is only used for benchmark eval (not used in the demo).STRING
HIGHsam3/model/sam3_video_base.py0this method is only used for benchmark eval (not used in the demo).STRING
Docstring Block Structure5 hits · 25 pts
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HIGHsam3/agent/helpers/memory.py30 Makes a function retry itself after encountering pytorch's CUDA OOM error. It will first retry after callinSTRING
HIGHsam3/train/optim/optimizer.py113Return parameters which match the intersection of parameter constraints. Note that this returns the parameters themSTRING
HIGHsam3/train/optim/optimizer.py134Produce parameter groups corresponding to all the scheduler configs. Takes all the scheduler configs, each of whichSTRING
HIGHsam3/eval/coco_reindex.py17 Convert 0-indexed COCO JSON file to 1-indexed and save to temporary location. Args: input_json_path: PSTRING
HIGHsam3/eval/saco_veval_evaluators.py26 Find the index of a metric in CGF1_METRICS by name and IoU threshold. Args: metric_name: Name of the mSTRING
Modern Structural Boilerplate16 hits · 18 pts
SeverityFileLineSnippetContext
LOWscripts/eval/veval/saco_yt1b_annot_update.py12logger = logging.getLogger(__name__)CODE
LOWscripts/eval/veval/saco_yt1b_frame_prep_util.py14logger = logging.getLogger(__name__)CODE
LOWscripts/eval/veval/saco_yt1b_downloader.py16logger = logging.getLogger(__name__)CODE
LOWsam3/model_builder.py54def _setup_tf32() -> None:CODE
LOWsam3/__init__.py9__all__ = ["build_sam3_image_model", "build_sam3_predictor"]CODE
LOWsam3/agent/helpers/memory.py11__all__ = ["retry_if_cuda_oom"]CODE
LOWsam3/agent/helpers/color_map.py14__all__ = ["colormap", "random_color", "random_colors"]CODE
LOWsam3/agent/helpers/visualizer.py28logger = logging.getLogger(__name__)CODE
LOWsam3/agent/helpers/visualizer.py31__all__ = ["ColorMode", "VisImage", "Visualizer"]CODE
LOWsam3/model/vitdet.py456 def _setup_rel_pos(self, rel_pos_zero_init: bool = True) -> None:CODE
LOWsam3/model/vitdet.py483 def _setup_rope_freqs(self) -> None:CODE
LOWsam3/model/multiplex_utils.py17logger = logging.getLogger(__name__)CODE
LOWsam3/train/trainer.py275 def _setup_env_variables(self, env_variables_conf) -> None:CODE
LOWsam3/train/trainer.py280 def _setup_torch_dist_and_backend(self, cuda_conf, distributed_conf) -> None:CODE
LOWsam3/train/utils/distributed.py373def set_cuda_device_index(idx: int) -> None:CODE
LOWsam3/train/utils/distributed.py379def set_cpu_device() -> None:CODE
AI Slop Vocabulary4 hits · 4 pts
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LOWsam3/model/sam3_multiplex_tracking.py2346 # if fetch just return from outputCOMMENT
LOWsam3/model/sam3_video_inference.py1045 # if fetch just return from outputCOMMENT
LOWsam3/model/video_tracking_multiplex.py1745 # we simply add a projected embedding for each empty objectCOMMENT
LOWsam3/model/video_tracking_multiplex.py2958 # Importantly, we cannot simply use valid_idx_per_frame[stage_id-1] because it might be a conditional frame.COMMENT
Slop Phrases2 hits · 3 pts
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LOWsam3/model/edt.py176 # don't forget to take sqrt at the endCOMMENT
LOWsam3/model/edt.py176 # don't forget to take sqrt at the endCOMMENT
Overly Generic Function Names2 hits · 2 pts
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LOWsam3/model/sam3_base_predictor.py50 def handle_request(self, request):CODE
LOWsam3/model/sam3_video_predictor.py143 def handle_request(self, request):CODE