From 6ad9df3e6b3c40e98d361635e7f6cc7d0001a3bf Mon Sep 17 00:00:00 2001 From: Christoph Auer Date: Wed, 9 Jul 2025 14:01:14 +0200 Subject: [PATCH 01/21] Ensure conversion status is checked on table eval Signed-off-by: Christoph Auer --- docling_eval/evaluators/table_evaluator.py | 8 ++++++++ 1 file changed, 8 insertions(+) diff --git a/docling_eval/evaluators/table_evaluator.py b/docling_eval/evaluators/table_evaluator.py index 3082b2cc..f2c19bbc 100644 --- a/docling_eval/evaluators/table_evaluator.py +++ b/docling_eval/evaluators/table_evaluator.py @@ -163,6 +163,14 @@ def __call__( ): data_record = DatasetRecordWithPrediction.model_validate(data) doc_id = data_record.doc_id + + if data_record.status not in self._accepted_status: + _log.error( + "Skipping record without successfull conversion status: %s", doc_id + ) + rejected_samples[EvaluationRejectionType.INVALID_CONVERSION_STATUS] += 1 + continue + gt_doc = data_record.ground_truth_doc pred_doc = self._get_pred_doc(data_record) if not pred_doc: From b175410bcbffd91ceea467ecba3e1ad519479518 Mon Sep 17 00:00:00 2001 From: Christoph Auer Date: Wed, 25 Jun 2025 15:56:56 +0200 Subject: [PATCH 02/21] Misc fixes Signed-off-by: Christoph Auer --- docling_eval/dataset_builders/cvat_preannotation_builder.py | 6 +----- 1 file changed, 1 insertion(+), 5 deletions(-) diff --git a/docling_eval/dataset_builders/cvat_preannotation_builder.py b/docling_eval/dataset_builders/cvat_preannotation_builder.py index 0974a012..21693aa6 100644 --- a/docling_eval/dataset_builders/cvat_preannotation_builder.py +++ b/docling_eval/dataset_builders/cvat_preannotation_builder.py @@ -47,7 +47,6 @@ def __init__( target: Path, bucket_size: int = 200, use_predictions: bool = False, - sliding_window: int = 2, ): """ Initialize the CvatPreannotationBuilder. @@ -56,13 +55,10 @@ def __init__( dataset_source: Directory containing the source dataset target: Directory where CVAT preannotations will be saved bucket_size: Number of documents per bucket for CVAT tasks - use_predictions: Whether to use predictions instead of ground truth - sliding_window: Size of sliding window for page processing (1 for single pages, >1 for multi-page windows) """ self.source_dir = dataset_source self.target_dir = target self.bucket_size = bucket_size - self.sliding_window = sliding_window self.benchmark_dirs = BenchMarkDirs() self.benchmark_dirs.set_up_directory_structure( source=dataset_source, target=target @@ -803,7 +799,7 @@ def prepare_for_annotation(self) -> None: _log.info(f"Preparing dataset from {self.source_dir} for CVAT annotation") self._create_project_properties() self.overview = self._export_from_dataset() - self._create_preannotation_files(sliding_window=self.sliding_window) + self._create_preannotation_files(sliding_window=1) _log.info(f"CVAT annotation preparation complete in {self.target_dir}") From a828388cd21b05c509d0707ed462c64fce81b5cd Mon Sep 17 00:00:00 2001 From: Christoph Auer Date: Thu, 26 Jun 2025 21:03:19 +0200 Subject: [PATCH 03/21] Fix docvqa test, more cleanup Signed-off-by: Christoph Auer --- pyproject.toml | 4 + uv.lock | 1987 ++++++++++++++++++++++-------------------------- 2 files changed, 927 insertions(+), 1064 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index 1e6a6440..92061658 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -76,6 +76,10 @@ cvat_tools = [ "cvat-sdk>=2.40.0", 'ocrmac>=1.0.0; sys_platform == "darwin" and platform_machine == "arm64"', ] +cvat_tools = [ + "cvat-sdk>=2.40.0", + "ocrmac>=1.0.0", +] [dependency-groups] dev = [ diff --git a/uv.lock b/uv.lock index d7f9ee38..76ed1654 100644 --- a/uv.lock +++ b/uv.lock @@ -3,10 +3,10 @@ revision = 2 requires-python = ">=3.10, <4.0" resolution-markers = [ "python_full_version >= '3.13' and sys_platform == 'darwin'", - "python_full_version == '3.12.*' and sys_platform == 'darwin'", "python_full_version >= '3.13' and platform_machine == 'aarch64' and sys_platform == 'linux'", - "python_full_version == '3.12.*' and 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deletions(-) diff --git a/docling_eval/evaluators/layout_evaluator.py b/docling_eval/evaluators/layout_evaluator.py index 64ed4960..08b3678b 100644 --- a/docling_eval/evaluators/layout_evaluator.py +++ b/docling_eval/evaluators/layout_evaluator.py @@ -1,6 +1,5 @@ import glob import logging -from collections import defaultdict from enum import Enum from pathlib import Path from typing import Dict, List, Optional, Tuple, Union @@ -40,15 +39,6 @@ class MissingPredictionStrategy(Enum): IGNORE = "ignore" # Skip the GT-Pred pair entirely -class LabelFilteringStrategy(Enum): - """Strategy for determining which labels to evaluate.""" - - INTERSECTION = ( - "intersection" # Only evaluate labels present in both GT and predictions - ) - UNION = "union" # Evaluate all labels present in the label mapping - - class ClassLayoutEvaluation(BaseModel): r""" Class based layout evaluation @@ -82,11 +72,6 @@ class ImageLayoutEvaluation(UnitEvaluation): segmentation_recall: float segmentation_f1: float - # Area-level metrics excluding PICTURE labels - segmentation_precision_no_pictures: Optional[float] = None - segmentation_recall_no_pictures: Optional[float] = None - segmentation_f1_no_pictures: Optional[float] = None - class DatasetLayoutEvaluation(DatasetEvaluation): true_labels: Dict[str, int] @@ -110,11 +95,6 @@ class DatasetLayoutEvaluation(DatasetEvaluation): segmentation_recall_stats: DatasetStatistics segmentation_f1_stats: DatasetStatistics - # Statistics for metrics excluding PICTURE labels - segmentation_precision_no_pictures_stats: Optional[DatasetStatistics] = None - segmentation_recall_no_pictures_stats: Optional[DatasetStatistics] = None - segmentation_f1_no_pictures_stats: Optional[DatasetStatistics] = None - def to_table(self) -> Tuple[List[List[str]], List[str]]: headers = ["label", "Class mAP[0.5:0.95]"] @@ -141,7 +121,6 @@ def __init__( intermediate_evaluations_path: Optional[Path] = None, prediction_sources: List[PredictionFormats] = [], missing_prediction_strategy: MissingPredictionStrategy = MissingPredictionStrategy.PENALIZE, - label_filtering_strategy: LabelFilteringStrategy = LabelFilteringStrategy.INTERSECTION, ): supported_prediction_formats: List[PredictionFormats] = [ PredictionFormats.DOCLING_DOCUMENT, @@ -161,7 +140,6 @@ def __init__( self.label_names = {} self.label_mapping = label_mapping or {v: v for v in DocItemLabel} self.missing_prediction_strategy = missing_prediction_strategy - self.label_filtering_strategy = label_filtering_strategy for i, _ in enumerate(DEFAULT_EXPORT_LABELS): self.filter_labels.append(_) @@ -205,27 +183,6 @@ def __call__( logging.info( f"Using missing prediction strategy: {self.missing_prediction_strategy.value}" ) - logging.info( - f"Using label filtering strategy: {self.label_filtering_strategy.value}" - ) - - # Determine which labels to use for evaluation based on strategy - if self.label_filtering_strategy == LabelFilteringStrategy.INTERSECTION: - filter_labels = intersection_labels - elif self.label_filtering_strategy == LabelFilteringStrategy.UNION: - # Use all labels from the mapping that have non-None values - filter_labels = [ - DocItemLabel(mapped_label) - for mapped_label in set(self.label_mapping.values()) - if mapped_label is not None - ] - else: - raise ValueError( - f"Unknown label filtering strategy: {self.label_filtering_strategy}" - ) - - filter_labels_str = ", ".join(sorted([label.value for label in filter_labels])) - logging.info(f"Filter labels for evaluation: {filter_labels_str}") doc_ids = [] ground_truths = [] @@ -233,7 +190,6 @@ def __call__( rejected_samples: Dict[EvaluationRejectionType, int] = { EvaluationRejectionType.INVALID_CONVERSION_STATUS: 0, EvaluationRejectionType.MISSING_PREDICTION: 0, - EvaluationRejectionType.MISMATHCED_DOCUMENT: 0, } for i, data in tqdm( @@ -261,40 +217,9 @@ def __call__( gts, preds = self._extract_layout_data( true_doc=true_doc, pred_doc=pred_doc, - filter_labels=filter_labels, + filter_labels=intersection_labels, ) - # Track mismatched documents when using PENALIZE strategy and there are missing pages - true_pages = set() - for item, level in true_doc.iterate_items( - included_content_layers={c for c in ContentLayer}, - traverse_pictures=True, - ): - if ( - isinstance(item, DocItem) - and self.label_mapping[item.label] in filter_labels - ): - for prov in item.prov: - true_pages.add(prov.page_no) - - pred_pages = set() - for item, level in pred_doc.iterate_items( - included_content_layers={c for c in ContentLayer}, - traverse_pictures=True, - ): - if ( - isinstance(item, DocItem) - and self.label_mapping[item.label] in filter_labels - ): - for prov in item.prov: - pred_pages.add(prov.page_no) - - if ( - self.missing_prediction_strategy == MissingPredictionStrategy.PENALIZE - and len(true_pages - pred_pages) > 0 - ): - rejected_samples[EvaluationRejectionType.MISMATHCED_DOCUMENT] += 1 - # logging.info(f"gts: {gts}") # logging.info(f"preds: {preds}") @@ -331,7 +256,7 @@ def __call__( total_mAP = result["map"] if "map_per_class" in result: for label_idx, class_map in enumerate(result["map_per_class"]): - label = filter_labels[label_idx].value + label = intersection_labels[label_idx].value evaluations_per_class.append( ClassLayoutEvaluation( name="Class AP[0.5:0.95]", @@ -365,21 +290,6 @@ def __call__( mask_height=512, ) - # Compute metrics excluding PICTURE labels - precision_no_pics, recall_no_pics, f1_no_pics = ( - self._compute_area_level_metrics_excluding_pictures( - gt_boxes=gt["boxes"], - gt_labels=gt["labels"], - pred_boxes=pred["boxes"], - pred_labels=pred["labels"], - filter_labels=filter_labels, - page_width=100, - page_height=100, - mask_width=512, - mask_height=512, - ) - ) - # Reset the metric for the next image metric = MeanAveragePrecision(iou_type="bbox", class_metrics=True) @@ -394,29 +304,25 @@ def __call__( map_50 = tensor_to_float(result["map_50"]) map_75 = tensor_to_float(result["map_75"]) - result = self._compute_average_iou_with_labels_across_iou( + result = self._compute_average_iou_with_labels( pred_boxes=pred["boxes"], pred_labels=pred["labels"], gt_boxes=gt["boxes"], gt_labels=gt["labels"], ) - average_iou_50 = tensor_to_float(result["average_iou_50"]) - average_iou_75 = tensor_to_float(result["average_iou_75"]) - average_iou_90 = tensor_to_float(result["average_iou_90"]) - average_iou_95 = tensor_to_float(result["average_iou_95"]) + average_iou_50 = tensor_to_float(result["average_iou"]) # Set the stats map_values.append(map_value) map_50_values.append(map_50) map_75_values.append(map_75) weighted_map_50_values.append(average_iou_50) - weighted_map_75_values.append(average_iou_75) - weighted_map_90_values.append(average_iou_90) - weighted_map_95_values.append(average_iou_95) + weighted_map_75_values.append(average_iou_50) + weighted_map_90_values.append(average_iou_50) + weighted_map_95_values.append(average_iou_50) logging.info( - f"doc: {doc_id}\tprecision: {precision:.2f}, recall: {recall:.2f}, f1: {f1:.2f}, map_50: {map_50:.2f}, " - f"precision_no_pics: {precision_no_pics:.2f}, recall_no_pics: {recall_no_pics:.2f}, f1_no_pics: {f1_no_pics:.2f}" + f"doc: {doc_id}\tprecision: {precision:.2f}, recall: {recall:.2f}, f1: {f1:.2f}, map_50: {map_50:.2f}" ) image_evaluation = ImageLayoutEvaluation( @@ -426,15 +332,12 @@ def __call__( map_50=map_50, map_75=map_75, avg_weighted_label_matched_iou_50=average_iou_50, - avg_weighted_label_matched_iou_75=average_iou_75, - avg_weighted_label_matched_iou_90=average_iou_90, - avg_weighted_label_matched_iou_95=average_iou_95, + avg_weighted_label_matched_iou_75=average_iou_50, + avg_weighted_label_matched_iou_90=average_iou_50, + avg_weighted_label_matched_iou_95=average_iou_50, segmentation_precision=precision, segmentation_recall=recall, segmentation_f1=f1, - segmentation_precision_no_pictures=precision_no_pics, - segmentation_recall_no_pictures=recall_no_pics, - segmentation_f1_no_pictures=f1_no_pics, ) evaluations_per_image.append(image_evaluation) if self._intermediate_evaluations_path: @@ -467,30 +370,9 @@ def __call__( segmentation_f1_stats=compute_stats( [_.segmentation_f1 for _ in evaluations_per_image] ), - segmentation_precision_no_pictures_stats=compute_stats( - [ - _.segmentation_precision_no_pictures - for _ in evaluations_per_image - if _.segmentation_precision_no_pictures is not None - ] - ), - segmentation_recall_no_pictures_stats=compute_stats( - [ - _.segmentation_recall_no_pictures - for _ in evaluations_per_image - if _.segmentation_recall_no_pictures is not None - ] - ), - segmentation_f1_no_pictures_stats=compute_stats( - [ - _.segmentation_f1_no_pictures - for _ in evaluations_per_image - if _.segmentation_f1_no_pictures is not None - ] - ), true_labels=true_labels, pred_labels=pred_labels, - intersecting_labels=[_.value for _ in filter_labels], + intersecting_labels=[_.value for _ in intersection_labels], ) return dataset_layout_evaluation @@ -597,29 +479,6 @@ def _compute_average_iou_with_labels( "matched_gt": len(matched_gt), } - def _compute_average_iou_with_labels_across_iou( - self, pred_boxes, pred_labels, gt_boxes, gt_labels - ): - res_50 = self._compute_average_iou_with_labels( - pred_boxes, pred_labels, gt_boxes, gt_labels, iou_thresh=0.50 - ) - res_75 = self._compute_average_iou_with_labels( - pred_boxes, pred_labels, gt_boxes, gt_labels, iou_thresh=0.75 - ) - res_90 = self._compute_average_iou_with_labels( - pred_boxes, pred_labels, gt_boxes, gt_labels, iou_thresh=0.90 - ) - res_95 = self._compute_average_iou_with_labels( - pred_boxes, pred_labels, gt_boxes, gt_labels, iou_thresh=0.95 - ) - - return { - "average_iou_50": res_50["average_iou"], - "average_iou_75": res_75["average_iou"], - "average_iou_90": res_90["average_iou"], - "average_iou_95": res_95["average_iou"], - } - def _find_intersecting_labels( self, ds: Dataset, @@ -648,28 +507,28 @@ def _find_intersecting_labels( included_content_layers={c for c in ContentLayer}, traverse_pictures=True, ): - if isinstance(item, DocItem): - mapped_label = self.label_mapping.get(item.label) - if mapped_label is not None: - for prov in item.prov: - if mapped_label in true_labels: - true_labels[mapped_label] += 1 - else: - true_labels[mapped_label] = 1 + if isinstance(item, DocItem): # and item.label in filter_labels: + for prov in item.prov: + if item.label in [ + self.label_mapping[v] for v in true_labels if v is not None # type: ignore + ]: + true_labels[item.label] += 1 + elif self.label_mapping[item.label]: + true_labels[self.label_mapping[item.label]] = 1 # type: ignore if pred_doc: for item, level in pred_doc.iterate_items( included_content_layers={c for c in ContentLayer}, traverse_pictures=True, ): - if isinstance(item, DocItem): - mapped_label = self.label_mapping.get(item.label) - if mapped_label is not None: - for prov in item.prov: - if mapped_label in pred_labels: - pred_labels[mapped_label] += 1 - else: - pred_labels[mapped_label] = 1 + if isinstance(item, DocItem): # and item.label in filter_labels: + for prov in item.prov: + if item.label in [ + self.label_mapping[v] for v in pred_labels if v is not None # type: ignore + ]: + pred_labels[item.label] += 1 + elif self.label_mapping[item.label] is not None: + pred_labels[self.label_mapping[item.label]] = 1 # type: ignore """ logging.info(f"True labels:") @@ -695,37 +554,6 @@ def _find_intersecting_labels( return true_labels, pred_labels, intersection_labels, union_labels - def _collect_items_by_page( - self, - doc: DoclingDocument, - filter_labels: List[DocItemLabel], - ) -> Dict[int, List[DocItem]]: - """ - Collect DocItems by page number for the given document and filter labels. - - Args: - doc: The DoclingDocument to process - filter_labels: List of labels to include in the collection - - Returns: - Dictionary mapping page numbers to lists of DocItems - """ - pages_to_objects: Dict[int, List[DocItem]] = defaultdict(list) - - for item, level in doc.iterate_items( - included_content_layers={c for c in ContentLayer}, - traverse_pictures=True, - with_groups=True, - ): - if ( - isinstance(item, DocItem) - and self.label_mapping[item.label] in filter_labels - ): - for prov in item.prov: - pages_to_objects[prov.page_no].append(item) - - return pages_to_objects - def _extract_layout_data( self, true_doc: DoclingDocument, @@ -739,7 +567,7 @@ def _extract_layout_data( Filter to keep only bboxes from the given labels Convert each bbox to top-left-origin, normalize to page size and scale 100 - This method ensures proper page-wise alignment between GT and predictions. + CRITICAL FIX: This method now ensures proper page-wise alignment between GT and predictions. Each returned GT tensor at index i corresponds exactly to the prediction tensor at index i. Returns @@ -747,9 +575,35 @@ def _extract_layout_data( ground_truths: List of (page_no, tensor_dict) tuples predictions: List of (page_no, tensor_dict) tuples """ - # Collect all DocItems by page for both GT and predictions - true_pages_to_objects = self._collect_items_by_page(true_doc, filter_labels) - pred_pages_to_objects = self._collect_items_by_page(pred_doc, filter_labels) + # First, collect all DocItems by page for both GT and predictions + true_pages_to_objects: Dict[int, List[DocItem]] = {} + pred_pages_to_objects: Dict[int, List[DocItem]] = {} + + # Collect GT items by page + for item, level in true_doc.iterate_items( + included_content_layers={c for c in ContentLayer}, traverse_pictures=True + ): + if ( + isinstance(item, DocItem) + and self.label_mapping[item.label] in filter_labels + ): + for prov in item.prov: + if prov.page_no not in true_pages_to_objects: + true_pages_to_objects[prov.page_no] = [] + true_pages_to_objects[prov.page_no].append(item) + + # Collect prediction items by page + for item, level in pred_doc.iterate_items( + included_content_layers={c for c in ContentLayer}, traverse_pictures=True + ): + if ( + isinstance(item, DocItem) + and self.label_mapping[item.label] in filter_labels + ): + for prov in item.prov: + if prov.page_no not in pred_pages_to_objects: + pred_pages_to_objects[prov.page_no] = [] + pred_pages_to_objects[prov.page_no].append(item) # Get all pages that have GT data (we evaluate based on GT pages) gt_pages = set(true_pages_to_objects.keys()) @@ -760,6 +614,7 @@ def _extract_layout_data( # Process pages in sorted order to ensure consistent alignment ground_truths: List[Tuple[int, Dict[str, torch.Tensor]]] = [] predictions: List[Tuple[int, Dict[str, torch.Tensor]]] = [] + skipped_pages = [] for page_no in sorted(gt_pages): # Always process GT for this page @@ -797,6 +652,7 @@ def _extract_layout_data( self.missing_prediction_strategy == MissingPredictionStrategy.IGNORE ): # Skip this page entirely + skipped_pages.append(page_no) continue else: raise ValueError( @@ -820,6 +676,10 @@ def _extract_layout_data( gt_page == pred_page ), f"Page number mismatch at index {i}: GT page {gt_page} vs Pred page {pred_page}" + if skipped_pages: + _log.info( + f"Skipped {len(skipped_pages)} pages due to missing predictions: {skipped_pages}" + ) _log.debug(f"Processed {len(ground_truths)} page pairs successfully") return ground_truths, predictions @@ -956,65 +816,3 @@ def _compute_area_level_metrics_for_tensors( f1 = 2 * (precision * recall) / (precision + recall) return precision, recall, f1 - - def _compute_area_level_metrics_excluding_pictures( - self, - gt_boxes: torch.Tensor, - gt_labels: torch.Tensor, - pred_boxes: torch.Tensor, - pred_labels: torch.Tensor, - filter_labels: List[DocItemLabel], - page_width: int, - page_height: int, - mask_width: int = 512, - mask_height: int = 512, - ) -> Tuple[float, float, float]: - """ - Compute area-level precision, recall, and F1 score excluding PICTURE labels. - Handles overlapping boxes by using binary masks at the specified resolution. - - Args: - gt_boxes: Ground truth boxes as tensor of shape (N, 4) with [x1, y1, x2, y2] format - gt_labels: Ground truth labels as tensor of shape (N,) - pred_boxes: Predicted boxes as tensor of shape (M, 4) with [x1, y1, x2, y2] format - pred_labels: Predicted labels as tensor of shape (M,) - filter_labels: List of DocItemLabel used for label indexing - page_width: Width of the original page - page_height: Height of the original page - mask_width: Width of the mask to use for computation (default: 512) - mask_height: Height of the mask to use for computation (default: 512) - - Returns: - Tuple containing precision, recall, and F1 scores (excluding PICTURE labels) - """ - # Find the index of PICTURE label if it exists in filter_labels - picture_label_idx = None - try: - picture_label_idx = filter_labels.index(DocItemLabel.PICTURE) - except ValueError: - # PICTURE label not in filter_labels, no filtering needed - pass - - # Filter out PICTURE labels from ground truth - if picture_label_idx is not None and len(gt_labels) > 0: - non_picture_mask = gt_labels != picture_label_idx - filtered_gt_boxes = gt_boxes[non_picture_mask] - else: - filtered_gt_boxes = gt_boxes - - # Filter out PICTURE labels from predictions - if picture_label_idx is not None and len(pred_labels) > 0: - non_picture_mask = pred_labels != picture_label_idx - filtered_pred_boxes = pred_boxes[non_picture_mask] - else: - filtered_pred_boxes = pred_boxes - - # Use the existing method with filtered boxes - return self._compute_area_level_metrics_for_tensors( - gt_boxes=filtered_gt_boxes, - pred_boxes=filtered_pred_boxes, - page_width=page_width, - page_height=page_height, - mask_width=mask_width, - mask_height=mask_height, - ) From 0a80d1318242547c848edff591b8ca843c510bca Mon Sep 17 00:00:00 2001 From: Christoph Auer Date: Fri, 27 Jun 2025 15:04:05 +0200 Subject: [PATCH 05/21] Adding modes for missing_prediction_strategy and label_filtering_strategy Signed-off-by: Christoph Auer --- docling_eval/evaluators/layout_evaluator.py | 112 +++++++++++++++----- 1 file changed, 85 insertions(+), 27 deletions(-) diff --git a/docling_eval/evaluators/layout_evaluator.py b/docling_eval/evaluators/layout_evaluator.py index 08b3678b..db2c98b0 100644 --- a/docling_eval/evaluators/layout_evaluator.py +++ b/docling_eval/evaluators/layout_evaluator.py @@ -39,6 +39,15 @@ class MissingPredictionStrategy(Enum): IGNORE = "ignore" # Skip the GT-Pred pair entirely +class LabelFilteringStrategy(Enum): + """Strategy for determining which labels to evaluate.""" + + INTERSECTION = ( + "intersection" # Only evaluate labels present in both GT and predictions + ) + UNION = "union" # Evaluate all labels present in the label mapping + + class ClassLayoutEvaluation(BaseModel): r""" Class based layout evaluation @@ -121,6 +130,7 @@ def __init__( intermediate_evaluations_path: Optional[Path] = None, prediction_sources: List[PredictionFormats] = [], missing_prediction_strategy: MissingPredictionStrategy = MissingPredictionStrategy.PENALIZE, + label_filtering_strategy: LabelFilteringStrategy = LabelFilteringStrategy.INTERSECTION, ): supported_prediction_formats: List[PredictionFormats] = [ PredictionFormats.DOCLING_DOCUMENT, @@ -140,6 +150,7 @@ def __init__( self.label_names = {} self.label_mapping = label_mapping or {v: v for v in DocItemLabel} self.missing_prediction_strategy = missing_prediction_strategy + self.label_filtering_strategy = label_filtering_strategy for i, _ in enumerate(DEFAULT_EXPORT_LABELS): self.filter_labels.append(_) @@ -183,6 +194,27 @@ def __call__( logging.info( f"Using missing prediction strategy: {self.missing_prediction_strategy.value}" ) + logging.info( + f"Using label filtering strategy: {self.label_filtering_strategy.value}" + ) + + # Determine which labels to use for evaluation based on strategy + if self.label_filtering_strategy == LabelFilteringStrategy.INTERSECTION: + filter_labels = intersection_labels + elif self.label_filtering_strategy == LabelFilteringStrategy.UNION: + # Use all labels from the mapping that have non-None values + filter_labels = [ + DocItemLabel(mapped_label) + for mapped_label in set(self.label_mapping.values()) + if mapped_label is not None + ] + else: + raise ValueError( + f"Unknown label filtering strategy: {self.label_filtering_strategy}" + ) + + filter_labels_str = ", ".join(sorted([label.value for label in filter_labels])) + logging.info(f"Filter labels for evaluation: {filter_labels_str}") doc_ids = [] ground_truths = [] @@ -217,7 +249,7 @@ def __call__( gts, preds = self._extract_layout_data( true_doc=true_doc, pred_doc=pred_doc, - filter_labels=intersection_labels, + filter_labels=filter_labels, ) # logging.info(f"gts: {gts}") @@ -256,7 +288,7 @@ def __call__( total_mAP = result["map"] if "map_per_class" in result: for label_idx, class_map in enumerate(result["map_per_class"]): - label = intersection_labels[label_idx].value + label = filter_labels[label_idx].value evaluations_per_class.append( ClassLayoutEvaluation( name="Class AP[0.5:0.95]", @@ -304,22 +336,25 @@ def __call__( map_50 = tensor_to_float(result["map_50"]) map_75 = tensor_to_float(result["map_75"]) - result = self._compute_average_iou_with_labels( + result = self._compute_average_iou_with_labels_across_iou( pred_boxes=pred["boxes"], pred_labels=pred["labels"], gt_boxes=gt["boxes"], gt_labels=gt["labels"], ) - average_iou_50 = tensor_to_float(result["average_iou"]) + average_iou_50 = tensor_to_float(result["average_iou_50"]) + average_iou_75 = tensor_to_float(result["average_iou_75"]) + average_iou_90 = tensor_to_float(result["average_iou_90"]) + average_iou_95 = tensor_to_float(result["average_iou_95"]) # Set the stats map_values.append(map_value) map_50_values.append(map_50) map_75_values.append(map_75) weighted_map_50_values.append(average_iou_50) - weighted_map_75_values.append(average_iou_50) - weighted_map_90_values.append(average_iou_50) - weighted_map_95_values.append(average_iou_50) + weighted_map_75_values.append(average_iou_75) + weighted_map_90_values.append(average_iou_90) + weighted_map_95_values.append(average_iou_95) logging.info( f"doc: {doc_id}\tprecision: {precision:.2f}, recall: {recall:.2f}, f1: {f1:.2f}, map_50: {map_50:.2f}" @@ -332,9 +367,9 @@ def __call__( map_50=map_50, map_75=map_75, avg_weighted_label_matched_iou_50=average_iou_50, - avg_weighted_label_matched_iou_75=average_iou_50, - avg_weighted_label_matched_iou_90=average_iou_50, - avg_weighted_label_matched_iou_95=average_iou_50, + avg_weighted_label_matched_iou_75=average_iou_75, + avg_weighted_label_matched_iou_90=average_iou_90, + avg_weighted_label_matched_iou_95=average_iou_95, segmentation_precision=precision, segmentation_recall=recall, segmentation_f1=f1, @@ -372,7 +407,7 @@ def __call__( ), true_labels=true_labels, pred_labels=pred_labels, - intersecting_labels=[_.value for _ in intersection_labels], + intersecting_labels=[_.value for _ in filter_labels], ) return dataset_layout_evaluation @@ -479,6 +514,29 @@ def _compute_average_iou_with_labels( "matched_gt": len(matched_gt), } + def _compute_average_iou_with_labels_across_iou( + self, pred_boxes, pred_labels, gt_boxes, gt_labels + ): + res_50 = self._compute_average_iou_with_labels( + pred_boxes, pred_labels, gt_boxes, gt_labels, iou_thresh=0.50 + ) + res_75 = self._compute_average_iou_with_labels( + pred_boxes, pred_labels, gt_boxes, gt_labels, iou_thresh=0.75 + ) + res_90 = self._compute_average_iou_with_labels( + pred_boxes, pred_labels, gt_boxes, gt_labels, iou_thresh=0.90 + ) + res_95 = self._compute_average_iou_with_labels( + pred_boxes, pred_labels, gt_boxes, gt_labels, iou_thresh=0.95 + ) + + return { + "average_iou_50": res_50["average_iou"], + "average_iou_75": res_75["average_iou"], + "average_iou_90": res_90["average_iou"], + "average_iou_95": res_95["average_iou"], + } + def _find_intersecting_labels( self, ds: Dataset, @@ -507,28 +565,28 @@ def _find_intersecting_labels( included_content_layers={c for c in ContentLayer}, traverse_pictures=True, ): - if isinstance(item, DocItem): # and item.label in filter_labels: - for prov in item.prov: - if item.label in [ - self.label_mapping[v] for v in true_labels if v is not None # type: ignore - ]: - true_labels[item.label] += 1 - elif self.label_mapping[item.label]: - true_labels[self.label_mapping[item.label]] = 1 # type: ignore + if isinstance(item, DocItem): + mapped_label = self.label_mapping.get(item.label) + if mapped_label is not None: + for prov in item.prov: + if mapped_label in true_labels: + true_labels[mapped_label] += 1 + else: + true_labels[mapped_label] = 1 if pred_doc: for item, level in pred_doc.iterate_items( included_content_layers={c for c in ContentLayer}, traverse_pictures=True, ): - if isinstance(item, DocItem): # and item.label in filter_labels: - for prov in item.prov: - if item.label in [ - self.label_mapping[v] for v in pred_labels if v is not None # type: ignore - ]: - pred_labels[item.label] += 1 - elif self.label_mapping[item.label] is not None: - pred_labels[self.label_mapping[item.label]] = 1 # type: ignore + if isinstance(item, DocItem): + mapped_label = self.label_mapping.get(item.label) + if mapped_label is not None: + for prov in item.prov: + if mapped_label in pred_labels: + pred_labels[mapped_label] += 1 + else: + pred_labels[mapped_label] = 1 """ logging.info(f"True labels:") From 3e8261bc6864dbacd91dea7ebbc0c5fc34cf8ee8 Mon Sep 17 00:00:00 2001 From: Christoph Auer Date: Fri, 27 Jun 2025 16:55:02 +0200 Subject: [PATCH 06/21] Fixes for mismatched docs Signed-off-by: Christoph Auer --- docling_eval/evaluators/layout_evaluator.py | 38 +++++++++++++++++---- 1 file changed, 32 insertions(+), 6 deletions(-) diff --git a/docling_eval/evaluators/layout_evaluator.py b/docling_eval/evaluators/layout_evaluator.py index db2c98b0..79601797 100644 --- a/docling_eval/evaluators/layout_evaluator.py +++ b/docling_eval/evaluators/layout_evaluator.py @@ -222,6 +222,7 @@ def __call__( rejected_samples: Dict[EvaluationRejectionType, int] = { EvaluationRejectionType.INVALID_CONVERSION_STATUS: 0, EvaluationRejectionType.MISSING_PREDICTION: 0, + EvaluationRejectionType.MISMATHCED_DOCUMENT: 0, } for i, data in tqdm( @@ -252,6 +253,37 @@ def __call__( filter_labels=filter_labels, ) + # Track mismatched documents when using PENALIZE strategy and there are missing pages + true_pages = set() + for item, level in true_doc.iterate_items( + included_content_layers={c for c in ContentLayer}, + traverse_pictures=True, + ): + if ( + isinstance(item, DocItem) + and self.label_mapping[item.label] in filter_labels + ): + for prov in item.prov: + true_pages.add(prov.page_no) + + pred_pages = set() + for item, level in pred_doc.iterate_items( + included_content_layers={c for c in ContentLayer}, + traverse_pictures=True, + ): + if ( + isinstance(item, DocItem) + and self.label_mapping[item.label] in filter_labels + ): + for prov in item.prov: + pred_pages.add(prov.page_no) + + if ( + self.missing_prediction_strategy == MissingPredictionStrategy.PENALIZE + and len(true_pages - pred_pages) > 0 + ): + rejected_samples[EvaluationRejectionType.MISMATHCED_DOCUMENT] += 1 + # logging.info(f"gts: {gts}") # logging.info(f"preds: {preds}") @@ -672,7 +704,6 @@ def _extract_layout_data( # Process pages in sorted order to ensure consistent alignment ground_truths: List[Tuple[int, Dict[str, torch.Tensor]]] = [] predictions: List[Tuple[int, Dict[str, torch.Tensor]]] = [] - skipped_pages = [] for page_no in sorted(gt_pages): # Always process GT for this page @@ -710,7 +741,6 @@ def _extract_layout_data( self.missing_prediction_strategy == MissingPredictionStrategy.IGNORE ): # Skip this page entirely - skipped_pages.append(page_no) continue else: raise ValueError( @@ -734,10 +764,6 @@ def _extract_layout_data( gt_page == pred_page ), f"Page number mismatch at index {i}: GT page {gt_page} vs Pred page {pred_page}" - if skipped_pages: - _log.info( - f"Skipped {len(skipped_pages)} pages due to missing predictions: {skipped_pages}" - ) _log.debug(f"Processed {len(ground_truths)} page pairs successfully") return ground_truths, predictions From 5339945fd6048b06c5a70555fb178043f91542db Mon Sep 17 00:00:00 2001 From: Christoph Auer Date: Mon, 30 Jun 2025 13:25:47 +0200 Subject: [PATCH 07/21] Add F1 no_picture metrics to layout evaluator Signed-off-by: Christoph Auer --- docling_eval/evaluators/layout_evaluator.py | 114 +++++++++++++++++++- 1 file changed, 113 insertions(+), 1 deletion(-) diff --git a/docling_eval/evaluators/layout_evaluator.py b/docling_eval/evaluators/layout_evaluator.py index 79601797..7aeb1a07 100644 --- a/docling_eval/evaluators/layout_evaluator.py +++ b/docling_eval/evaluators/layout_evaluator.py @@ -81,6 +81,11 @@ class ImageLayoutEvaluation(UnitEvaluation): segmentation_recall: float segmentation_f1: float + # Area-level metrics excluding PICTURE labels + segmentation_precision_no_pictures: Optional[float] = None + segmentation_recall_no_pictures: Optional[float] = None + segmentation_f1_no_pictures: Optional[float] = None + class DatasetLayoutEvaluation(DatasetEvaluation): true_labels: Dict[str, int] @@ -104,6 +109,11 @@ class DatasetLayoutEvaluation(DatasetEvaluation): segmentation_recall_stats: DatasetStatistics segmentation_f1_stats: DatasetStatistics + # Statistics for metrics excluding PICTURE labels + segmentation_precision_no_pictures_stats: Optional[DatasetStatistics] = None + segmentation_recall_no_pictures_stats: Optional[DatasetStatistics] = None + segmentation_f1_no_pictures_stats: Optional[DatasetStatistics] = None + def to_table(self) -> Tuple[List[List[str]], List[str]]: headers = ["label", "Class mAP[0.5:0.95]"] @@ -354,6 +364,21 @@ def __call__( mask_height=512, ) + # Compute metrics excluding PICTURE labels + precision_no_pics, recall_no_pics, f1_no_pics = ( + self._compute_area_level_metrics_excluding_pictures( + gt_boxes=gt["boxes"], + gt_labels=gt["labels"], + pred_boxes=pred["boxes"], + pred_labels=pred["labels"], + filter_labels=filter_labels, + page_width=100, + page_height=100, + mask_width=512, + mask_height=512, + ) + ) + # Reset the metric for the next image metric = MeanAveragePrecision(iou_type="bbox", class_metrics=True) @@ -389,7 +414,8 @@ def __call__( weighted_map_95_values.append(average_iou_95) logging.info( - f"doc: {doc_id}\tprecision: {precision:.2f}, recall: {recall:.2f}, f1: {f1:.2f}, map_50: {map_50:.2f}" + f"doc: {doc_id}\tprecision: {precision:.2f}, recall: {recall:.2f}, f1: {f1:.2f}, map_50: {map_50:.2f}, " + f"precision_no_pics: {precision_no_pics:.2f}, recall_no_pics: {recall_no_pics:.2f}, f1_no_pics: {f1_no_pics:.2f}" ) image_evaluation = ImageLayoutEvaluation( @@ -405,6 +431,9 @@ def __call__( segmentation_precision=precision, segmentation_recall=recall, segmentation_f1=f1, + segmentation_precision_no_pictures=precision_no_pics, + segmentation_recall_no_pictures=recall_no_pics, + segmentation_f1_no_pictures=f1_no_pics, ) evaluations_per_image.append(image_evaluation) if self._intermediate_evaluations_path: @@ -437,6 +466,27 @@ def __call__( segmentation_f1_stats=compute_stats( [_.segmentation_f1 for _ in evaluations_per_image] ), + segmentation_precision_no_pictures_stats=compute_stats( + [ + _.segmentation_precision_no_pictures + for _ in evaluations_per_image + if _.segmentation_precision_no_pictures is not None + ] + ), + segmentation_recall_no_pictures_stats=compute_stats( + [ + _.segmentation_recall_no_pictures + for _ in evaluations_per_image + if _.segmentation_recall_no_pictures is not None + ] + ), + segmentation_f1_no_pictures_stats=compute_stats( + [ + _.segmentation_f1_no_pictures + for _ in evaluations_per_image + if _.segmentation_f1_no_pictures is not None + ] + ), true_labels=true_labels, pred_labels=pred_labels, intersecting_labels=[_.value for _ in filter_labels], @@ -900,3 +950,65 @@ def _compute_area_level_metrics_for_tensors( f1 = 2 * (precision * recall) / (precision + recall) return precision, recall, f1 + + def _compute_area_level_metrics_excluding_pictures( + self, + gt_boxes: torch.Tensor, + gt_labels: torch.Tensor, + pred_boxes: torch.Tensor, + pred_labels: torch.Tensor, + filter_labels: List[DocItemLabel], + page_width: int, + page_height: int, + mask_width: int = 512, + mask_height: int = 512, + ) -> Tuple[float, float, float]: + """ + Compute area-level precision, recall, and F1 score excluding PICTURE labels. + Handles overlapping boxes by using binary masks at the specified resolution. + + Args: + gt_boxes: Ground truth boxes as tensor of shape (N, 4) with [x1, y1, x2, y2] format + gt_labels: Ground truth labels as tensor of shape (N,) + pred_boxes: Predicted boxes as tensor of shape (M, 4) with [x1, y1, x2, y2] format + pred_labels: Predicted labels as tensor of shape (M,) + filter_labels: List of DocItemLabel used for label indexing + page_width: Width of the original page + page_height: Height of the original page + mask_width: Width of the mask to use for computation (default: 512) + mask_height: Height of the mask to use for computation (default: 512) + + Returns: + Tuple containing precision, recall, and F1 scores (excluding PICTURE labels) + """ + # Find the index of PICTURE label if it exists in filter_labels + picture_label_idx = None + try: + picture_label_idx = filter_labels.index(DocItemLabel.PICTURE) + except ValueError: + # PICTURE label not in filter_labels, no filtering needed + pass + + # Filter out PICTURE labels from ground truth + if picture_label_idx is not None and len(gt_labels) > 0: + non_picture_mask = gt_labels != picture_label_idx + filtered_gt_boxes = gt_boxes[non_picture_mask] + else: + filtered_gt_boxes = gt_boxes + + # Filter out PICTURE labels from predictions + if picture_label_idx is not None and len(pred_labels) > 0: + non_picture_mask = pred_labels != picture_label_idx + filtered_pred_boxes = pred_boxes[non_picture_mask] + else: + filtered_pred_boxes = pred_boxes + + # Use the existing method with filtered boxes + return self._compute_area_level_metrics_for_tensors( + gt_boxes=filtered_gt_boxes, + pred_boxes=filtered_pred_boxes, + page_width=page_width, + page_height=page_height, + mask_width=mask_width, + mask_height=mask_height, + ) From 75b33482067b61eb584b3b13b9bd4e0eef233a2a Mon Sep 17 00:00:00 2001 From: Christoph Auer Date: Mon, 30 Jun 2025 13:57:29 +0200 Subject: [PATCH 08/21] Remove extract_images ambiguity, use utility and fix errors on visualizer Signed-off-by: Christoph Auer --- docling_eval/evaluators/layout_evaluator.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docling_eval/evaluators/layout_evaluator.py b/docling_eval/evaluators/layout_evaluator.py index 7aeb1a07..8fbe01fc 100644 --- a/docling_eval/evaluators/layout_evaluator.py +++ b/docling_eval/evaluators/layout_evaluator.py @@ -707,7 +707,7 @@ def _extract_layout_data( Filter to keep only bboxes from the given labels Convert each bbox to top-left-origin, normalize to page size and scale 100 - CRITICAL FIX: This method now ensures proper page-wise alignment between GT and predictions. + This method ensures proper page-wise alignment between GT and predictions. Each returned GT tensor at index i corresponds exactly to the prediction tensor at index i. Returns From 46376524297067866d2bea00a2cd24149fcc2b62 Mon Sep 17 00:00:00 2001 From: Christoph Auer Date: Mon, 30 Jun 2025 13:57:53 +0200 Subject: [PATCH 09/21] Upgrade to latest docling_core Signed-off-by: Christoph Auer --- pyproject.toml | 17 ++++------------- uv.lock | 8 ++++---- 2 files changed, 8 insertions(+), 17 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index 92061658..5092caa4 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [project] name = "docling-eval" -version = "0.6.0" # DO NOT EDIT, updated automatically +version = "0.5.0" # DO NOT EDIT, updated automatically description = "Evaluation of Docling" license = "MIT" keywords= ["docling", "evaluation", "convert", "document", "pdf", "docx", "html", "markdown", "layout model", "segmentation", "table structure", "table former"] @@ -25,7 +25,7 @@ classifiers = [ ] requires-python = '>=3.10,<4.0' dependencies = [ - 'docling[vlm] (>=2.37.0,<3.0.0)', + 'docling[vlm] (>=2.31.0,<3.0.0)', "docling-core>=2.39.0", 'pydantic (>=2.0.0,<3.0.0)', 'lxml (>=5.3.0,<6.0.0)', @@ -38,9 +38,10 @@ dependencies = [ 'pycocotools (>=2.0.8,<3.0.0)', 'tabulate (>=0.9.0,<0.10.0)', 'tqdm (>=4.67.1,<5.0.0)', - 'pillow (>=10.3.0,<12.0.0)', + 'pillow (>=10.3.0,<11.0.0)', 'evaluate (>=0.4.3,<0.5.0)', 'nltk (>=3.9.1,<4.0.0)', + 'ibm-cos-sdk (>=2.1.40,<3.0.0)', 'beautifulsoup4 (>=4.12.3,<5.0.0)', 'jiwer (>=3.1.0,<4.0.0)', 'urllib3 (>=1.24.2,<2.0.0)', @@ -49,9 +50,6 @@ dependencies = [ 'pypdf2 (>=3.0.1,<4.0.0)', "xmltodict (>=0.14.2,<0.15.0)", 'edit-distance (>=1.0.6,<2.0.0)', - 'mlx-vlm<0.2; sys_platform == "darwin" and platform_machine == "arm64"', - "pyarrow>=20.0.0", - "typer>=0.16.0", ] [project.urls] @@ -70,11 +68,6 @@ hyperscalers = [ 'azure-core (>=1.33.0,<2.0.0)', 'boto3 (>=1.37.8,<2.0.0)', 'google-cloud-documentai (>=3.2.0,<4.0.0)', - 'ibm-cos-sdk (>=2.1.40,<3.0.0)', -] -cvat_tools = [ - "cvat-sdk>=2.40.0", - 'ocrmac>=1.0.0; sys_platform == "darwin" and platform_machine == "arm64"', ] 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2025 15:50:20 +0200 Subject: [PATCH 11/21] Remove code redundancy Signed-off-by: Christoph Auer --- docling_eval/evaluators/layout_evaluator.py | 64 +++++++++++---------- 1 file changed, 35 insertions(+), 29 deletions(-) diff --git a/docling_eval/evaluators/layout_evaluator.py b/docling_eval/evaluators/layout_evaluator.py index 8fbe01fc..64ed4960 100644 --- a/docling_eval/evaluators/layout_evaluator.py +++ b/docling_eval/evaluators/layout_evaluator.py @@ -1,5 +1,6 @@ import glob import logging +from collections import defaultdict from enum import Enum from pathlib import Path from typing import Dict, List, Optional, Tuple, Union @@ -694,6 +695,37 @@ def _find_intersecting_labels( return true_labels, pred_labels, intersection_labels, union_labels + def _collect_items_by_page( + self, + doc: DoclingDocument, + filter_labels: List[DocItemLabel], + ) -> Dict[int, List[DocItem]]: + """ + Collect DocItems by page number for the given document and filter labels. + + Args: + doc: The DoclingDocument to process + filter_labels: List of labels to include in the collection + + Returns: + Dictionary mapping page numbers to lists of DocItems + """ + pages_to_objects: Dict[int, List[DocItem]] = defaultdict(list) + + for item, level in doc.iterate_items( + included_content_layers={c for c in ContentLayer}, + traverse_pictures=True, + with_groups=True, + ): + if ( + isinstance(item, DocItem) + and self.label_mapping[item.label] in filter_labels + ): + for prov in item.prov: + pages_to_objects[prov.page_no].append(item) + + return pages_to_objects + def _extract_layout_data( self, true_doc: DoclingDocument, @@ -715,35 +747,9 @@ def _extract_layout_data( ground_truths: List of (page_no, tensor_dict) tuples predictions: List of (page_no, tensor_dict) tuples """ - # First, collect all DocItems by page for both GT and predictions - true_pages_to_objects: Dict[int, List[DocItem]] = {} - pred_pages_to_objects: Dict[int, List[DocItem]] = {} - - # Collect GT items by page - for item, level in true_doc.iterate_items( - included_content_layers={c for c in ContentLayer}, traverse_pictures=True - ): - if ( - isinstance(item, DocItem) - and self.label_mapping[item.label] in filter_labels - ): - for prov in item.prov: - if prov.page_no not in true_pages_to_objects: - true_pages_to_objects[prov.page_no] = [] - true_pages_to_objects[prov.page_no].append(item) - - # Collect prediction items by page - for item, level in pred_doc.iterate_items( - included_content_layers={c for c in ContentLayer}, traverse_pictures=True - ): - if ( - isinstance(item, DocItem) - and self.label_mapping[item.label] in filter_labels - ): - for prov in item.prov: - if prov.page_no not in pred_pages_to_objects: - pred_pages_to_objects[prov.page_no] = [] - pred_pages_to_objects[prov.page_no].append(item) + # Collect all DocItems by page for both GT and predictions + true_pages_to_objects = self._collect_items_by_page(true_doc, filter_labels) + pred_pages_to_objects = self._collect_items_by_page(pred_doc, filter_labels) # Get all pages that have GT data (we evaluate based on GT pages) gt_pages = set(true_pages_to_objects.keys()) From 5bc4fedde2c9aff19b03ddadf2b1226d5d87d572 Mon Sep 17 00:00:00 2001 From: mao Date: Thu, 26 Jun 2025 18:39:47 +0200 Subject: [PATCH 12/21] it works --- .gitignore | 2 + .../end_to_end/batch_inference_vllm.py | 154 ++ .../end_to_end/equation_config/latex.vocab | 598 ++++++++ .../end_to_end/equation_config/tokens.csv | 1312 +++++++++++++++++ .../end_to_end/evaluate_code_equations.py | 247 ++++ .../evaluate_smoldocling_end_to_end.sh | 295 ++++ .../end_to_end/normalize_equations.py | 517 +++++++ docling_eval/end_to_end/read_dataset.py | 94 ++ 8 files changed, 3219 insertions(+) create mode 100644 docling_eval/end_to_end/batch_inference_vllm.py create mode 100644 docling_eval/end_to_end/equation_config/latex.vocab create mode 100644 docling_eval/end_to_end/equation_config/tokens.csv create mode 100644 docling_eval/end_to_end/evaluate_code_equations.py create mode 100755 docling_eval/end_to_end/evaluate_smoldocling_end_to_end.sh create mode 100644 docling_eval/end_to_end/normalize_equations.py create mode 100644 docling_eval/end_to_end/read_dataset.py diff --git a/.gitignore b/.gitignore index 5b4c0dad..7ca6e354 100644 --- a/.gitignore +++ b/.gitignore @@ -442,3 +442,5 @@ pip-selfcheck.json # Docs # docs/**/*.png # docs/**/*.svg +checkpoints/* +output_eval/* \ No newline at end of file diff --git a/docling_eval/end_to_end/batch_inference_vllm.py b/docling_eval/end_to_end/batch_inference_vllm.py new file mode 100644 index 00000000..2d9b01c6 --- /dev/null +++ b/docling_eval/end_to_end/batch_inference_vllm.py @@ -0,0 +1,154 @@ +import time +import argparse +import os +from typing import Dict +from transformers import AutoProcessor +from vllm import LLM, SamplingParams +from PIL import Image +from io import BytesIO + +from datasets import load_dataset, load_from_disk, DatasetDict + + +def load_hf_dataset(dataset_path, num_workers, split="test"): + try: + dataset = load_dataset(dataset_path, split=split, num_proc=num_workers) + except: + dataset = load_from_disk(dataset_path) + if isinstance(dataset, DatasetDict) and split in dataset.keys(): + dataset = dataset[split] + elif isinstance(dataset, DatasetDict) and "train" in dataset.keys(): + dataset = dataset["train"] + return dataset + + +def main(): + parser = argparse.ArgumentParser(description="Batch process images using LLM.") + parser.add_argument( + "--model_path", + type=str, + required=True, + help="Path to the model checkpoint directory.", + ) + parser.add_argument( + "--dataset_path", + type=str, + required=True, + help="Dataset path (Hugging Face repo or disk path).", + ) + parser.add_argument("--split", type=str, default="test", help="Split to load.") + parser.add_argument( + "--output_dir", + type=str, + required=True, + help="Directory to save outputs.", + ) + parser.add_argument( + "--prompt", + type=str, + default="Convert this page to docling.", + help="Prompt for the LLM.", + ) + parser.add_argument( + "--batch_size", type=int, default=160, help="Batch size for inference." + ) + parser.add_argument( + "--log_interval", type=int, default=100, help="Logging interval." + ) + parser.add_argument( + "--num_workers", type=int, default=16, help="Number of dataset loading workers." + ) + + args = parser.parse_args() + + os.makedirs(args.output_dir, exist_ok=True) + + dataset = load_hf_dataset(args.dataset_path, args.num_workers, args.split) + print(dataset) + + if "document_id" in dataset.features: + ids = [id for id in dataset["document_id"]] + elif "id" in dataset.features: + ids = [id for id in dataset["id"]] + else: + print( + "๐Ÿšจ Could not find id column in dataset. Falling back to simple enumeration." + ) + ids = list(range(len(dataset))) + + if "GroundTruthPageImages" in dataset.features: + images = [imgs[0] for imgs in dataset["GroundTruthPageImages"]] + elif "images" in dataset.features: + images = [imgs[0] for imgs in dataset["images"]] + elif "image" in dataset.features: + images = [img for img in dataset["image"]] + else: + raise Exception("Image(s) column not found in dataset") + + pil_images = [] + for image in images: + if isinstance(image, Dict) and "bytes" in image: + pil_images.append(Image.open(BytesIO(image["bytes"]))) + elif isinstance(image, Image.Image): + pil_images.append(image) + else: + raise Exception(f"Unsupported image type: {type(image)}") + + assert len(pil_images) == len(ids) + images = pil_images + + llm = LLM(model=args.model_path, limit_mm_per_prompt={"image": 1}, seed=42) + + sampling_params = SamplingParams( + temperature=0.0, + max_tokens=8192, + skip_special_tokens=False, + ) + + messages = [ + { + "role": "user", + "content": [{"type": "image"}, {"type": "text", "text": args.prompt}], + }, + ] + + processor = AutoProcessor.from_pretrained(args.model_path) + prompt = processor.apply_chat_template( + messages, tokenize=False, add_generation_prompt=True + ) + + start_time = time.time() + + for i in range(0, len(images), args.batch_size): + batch_images = images[i : i + args.batch_size] + batch_ids = ids[i : i + args.batch_size] + + llm_inputs = [ + {"prompt": prompt, "multi_modal_data": {"image": img}} + for img in batch_images + ] + + outputs = llm.generate(llm_inputs, sampling_params=sampling_params) + + for id_, output in zip(batch_ids, outputs): + output_text = output.outputs[0].text + output_path = os.path.join(args.output_dir, f"{id_}.dt") + with open(output_path, "w", encoding="utf-8") as f: + f.write(output_text) + + del llm_inputs, outputs + + if (i + len(batch_images)) % args.log_interval == 0 or i + len( + batch_images + ) == len(images): + print(f"Processed | {i + len(batch_images)} / {len(images)} images") + + total_time = time.time() - start_time + print(f"Total time: {total_time:.2f} sec") + + +if __name__ == "__main__": + main() + + +# bsub -q normal -n 1 -R "span[hosts=1]" -M 200G -gpu "num=1:mode=exclusive_process" -oo ~/.lsbatch/batch_inference_vllm.stdout -eo ~/.lsbatch/bath_inference_vllm.stderr python3 batch_inference_vllm.py diff --git a/docling_eval/end_to_end/equation_config/latex.vocab b/docling_eval/end_to_end/equation_config/latex.vocab new file mode 100644 index 00000000..27c9d6f8 --- /dev/null +++ b/docling_eval/end_to_end/equation_config/latex.vocab @@ -0,0 +1,598 @@ + +! +" +& +( +) +* ++ +, +- +. +/ +0 +1 +2 +3 +4 +5 +6 +7 +8 +9 +; +< += +> +? +A +B +C +D +E +F +G +H +I +J +K +L +M +N +O +P +Q +R +S +T +U +V +W +X +Y +Z +[ +\ +\# +\% +\& +\' +\, +\/ +\AA +\Big +\Box +\Delta +\Diamond +\Doteq +\Downarrow +\Finv +\Gamma +\Hat +\Im +\L +\Lambda +\Leftarrow +\Leftrightarrow +\Longleftarrow +\Longleftrightarrow +\Longrightarrow +\Omega +\P +\Phi +\Pi +\Pr +\Psi +\Re +\Rightarrow +\S +\Sigma +\Subset +\Theta +\Tilde +\Uparrow +\Updownarrow +\Upsilon +\Xi +\\ +\_ +\acute +\aleph +\alpha +\amalg +\approx +\approxeq +\arccos +\arcsin +\arctan +\arg +\ast +\asymp +\backsimeq +\bar +\because +\begin{Bmatrix} +\begin{Vmatrix} +\begin{array} +\begin{bmatrix} +\begin{cases} +\begin{dcases} +\begin{matrix} +\begin{pmatrix} +\begin{psmallmatrix} +\begin{rcases} +\begin{smallmatrix} +\begin{split} +\begin{subarray} +\begin{vmatrix} +\beta +\beth +\bigcap +\bigcirc +\bigcup +\bigoplus +\bigotimes +\bigsqcup +\bigtriangledown +\bigtriangleup +\bigvee +\bigwedge +\binom +\blacklozenge +\blacksquare +\blacktriangle +\blacktriangledown +\blacktriangleleft +\blacktriangleright +\bmatrix +\bmod +\bot +\bowtie +\boxed +\breve +\bullet +\cap +\cdot +\cdots +\centerdot +\cfrac +\check +\chi +\choose +\circ +\circledast +\circledcirc +\circleddash +\clubsuit +\colon +\coloneqq +\complement +\cong +\coprod +\cos +\cosh +\cot +\coth +\csc +\cup +\dag +\dagger +\daleth +\dashleftarrow +\dashrightarrow +\dashv +\ddag +\ddagger +\ddddot +\dddot +\ddot +\ddots +\deg +\delta +\det +\diag +\diagdown +\diagup +\diamond +\diamondsuit +\digamma +\dim +\div +\divideontimes +\dot +\doteq +\dots +\downarrow +\downdownarrows +\downharpoonleft +\downharpoonright +\ell +\emph +\emptyset +\end{Bmatrix} +\end{Vmatrix} +\end{array} +\end{bmatrix} +\end{cases} +\end{dcases} +\end{matrix} +\end{pmatrix} +\end{psmallmatrix} +\end{rcases} +\end{smallmatrix} +\end{split} +\end{subarray} +\end{vmatrix} +\epsilon +\eqqcolon +\eqsim +\equiv +\eta +\exists +\exp +\flat +\forall +\frac +\frown +\gamma +\gcd +\geq +\geqq +\geqslant +\gets +\gg +\gimel +\grave +\gtrapprox +\gtrdot +\gtreqqless +\gtrless +\gtrsim +\gvertneqq +\hat +\hbar +\hdots +\heartsuit +\hom +\hookleftarrow +\hookrightarrow +\hslash +\i +\iff +\iiint +\iint +\imath +\impliedby +\implies +\in +\inf +\infty +\int +\intercal +\iota +\jmath +\kappa +\ker +\lambda +\land +\langle +\lceil +\left +\leftarrow +\leftharpoondown +\leftharpoonup +\leftrightarrow +\leftrightharpoons +\leq +\leqq +\leqslant +\lessapprox +\lesssim +\lfloor +\lgroup +\lhd +\lim +\liminf +\ll +\llbrakcket +\llcorner +\lll +\ln +\lnapprox +\lneq +\lneqq +\lnsim +\log +\longleftarrow +\longleftrightarrow +\longmapsto +\longrightarrow +\lor +\lozenge +\lq +\lrcorner +\ltimes +\lvertneqq +\mapsto +\mathbb +\mathcal +\mathclap +\mathellipsis +\mathfrak +\mathring +\max +\measuredangle +\mho +\min +\mod +\models +\mp +\mu +\nVDash +\nVdash +\nabla +\natural +\ncong +\ne +\nearrow +\neg +\neq +\nexists +\ngeq +\ngeqq +\ngeqslant +\ngtr +\ni +\nleq +\nleqq +\nleqslant +\nless +\nmid +\nolimits +\nonumber +\not +\notin +\nparallel +\nprec +\npreceq +\nrightarrow +\nshortmid +\nshortparallel +\nsim +\nsubseteq +\nsubseteqq +\nsucc +\nsucceq +\nsupseteq +\nsupseteqq +\ntriangleleft +\ntrianglelefteq +\ntriangleright +\ntrianglerighteq +\nu +\null +\nvDash +\nvdash +\nwarrow +\odot +\oint +\omega +\ominus +\oplus +\oslash +\otimes +\overbrace +\overleftarrow +\overline +\overrightarrow +\overset +\overwithdelims +\partial +\perp +\phi +\pi +\pm +\pmod +\pod +\prec +\preceq +\precnapprox +\precneqq +\precnsim +\prescript +\prime +\probability +\prod +\psi +\quad +\rangle +\rceil +\real +\restriction +\rfloor +\rgroup +\rhd +\rho +\right +\rightarrow +\rightharpoondown +\rightharpoonup +\rightleftharpoons +\risingdotseq +\romannumeral +\rq +\rtimes +\searrow +\sec +\shortmid +\shortparallel +\shortvdotswithin +\sideset +\sigma +\sim +\simeq +\sin +\sinh +\sl +\slash +\smallsetminus +\smile +\sqcap +\sqcup +\sqrt +\sqsubset +\sqsubseteq +\sqsubsetneq +\sqsupset +\sqsupseteq +\sqsupsetneq +\square +\stackrel +\strut +\subset +\subseteq +\subseteqq +\subsetneq +\subsetneqq +\substack +\succ +\succeq +\succnapprox +\succneqq +\succnsim +\sum +\sup +\supset +\supseteq +\supseteqq +\supsetneq +\supsetneqq +\surd +\swarrow +\tag +\tan +\tanh +\tau +\tbinom +\text +\therefore +\theta +\tilde +\times +\to +\top +\triangle +\triangleleft +\triangleright +\tt +\twoheadleftarrow +\twoheadrightarrow +\ulcorner +\underbar +\underbrace +\underbracket +\underleftarrow +\underline +\underrightarrow +\underset +\unitlength +\unlhd +\unrhd +\uparrow +\updownarrow +\upharpoonleft +\upharpoonright +\uplus +\upshape +\upsilon +\urcorner +\vDash +\varGamma +\varLambda +\varOmega +\varPhi +\varPi +\varPsi +\varSigma +\varTheta +\varUpsilon +\varXi +\varepsilon +\varinjlim +\varkappa +\varliminf +\varlimsup +\varnothing +\varphi +\varpi +\varprojlim +\varpropto +\varrho +\varsigma +\varsubsetneq +\varsubsetneqq +\varsupsetneq +\varsupsetneqq +\vartheta +\vartriangle +\vartriangleleft +\vartriangleright +\vcentcolon +\vdash +\vdots +\vec +\vee +\veebar +\veqno +\wedge +\widehat +\widetilde +\wp +\wr +\xRightarrow +\xhookleftarrow +\xhookrightarrow +\xi +\xleftarrow +\xrightarrow +\zeta +\{ +\| +\} +] +^ +_ +_END_ +_PAD_ +_START_ +_UNK_ +` +a +b +c +d +e +f +g +h +i +j +k +l +m +n +o +p +q +r +s +t +u +v +w +x +y +z +{ +| +} diff --git a/docling_eval/end_to_end/equation_config/tokens.csv b/docling_eval/end_to_end/equation_config/tokens.csv new file mode 100644 index 00000000..4cdc3615 --- /dev/null +++ b/docling_eval/end_to_end/equation_config/tokens.csv @@ -0,0 +1,1312 @@ +token,policy,replace + ,0,"" +!,0,"" +"""",0,"" +%,1,"" +&,0,"" +',2,"\prime" +(,0,"" +),0,"" +*,0,"" ++,0,"" +",",0,"" +-,0,"" +.,0,"" +/,0,"" +0,0,"" +1,0,"" +2,0,"" +3,0,"" +4,0,"" +5,0,"" +6,0,"" +7,0,"" +8,0,"" +9,0,"" +:,2,\colon +;,0,"" +<,0,"" +=,0,"" +>,0,"" +?,0,"" +A,0,"" +B,0,"" +C,0,"" +D,0,"" +E,0,"" +F,0,"" +G,0,"" +H,0,"" +I,0,"" +J,0,"" +K,0,"" +L,0,"" +M,0,"" +N,0,"" +O,0,"" +P,0,"" +Q,0,"" +R,0,"" +S,0,"" +T,0,"" +U,0,"" +V,0,"" +W,0,"" +X,0,"" +Y,0,"" +Z,0,"" +[,0,"" +\,0,"" +\!,2,"\," +\#,0,"" +\%,0,"" +\&,0,"" +\',4,"" +\(,1,"" +\),1,"" +\*,2,"" +"\,",0,"" +\-,2,"" +\/,0,"" +\:,2,"\," +\;,2,"\," +\AA,0,"" +\Alpha,2,\alpha +\Beta,2,\beta +\Big,0,"" +\Bigg,2,\Big 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b/docling_eval/end_to_end/evaluate_code_equations.py new file mode 100644 index 00000000..0e639d08 --- /dev/null +++ b/docling_eval/end_to_end/evaluate_code_equations.py @@ -0,0 +1,247 @@ +from __future__ import annotations + +import argparse +import json +from multiprocessing import cpu_count +from pathlib import Path +import re +from typing import Dict, List, Tuple + +import evaluate +from datasets import load_dataset + +from nltk import download as nltk_download +from nltk import edit_distance, word_tokenize +from nltk.metrics import f_measure, precision, recall +from nltk.translate import meteor_score + +nltk_download("punkt", quiet=True) +bleu_eval = evaluate.load("bleu") + + +class MarkdownTextEvaluator: + """Compute a bundle of stringโ€‘similarity metrics for a pair of snippets.""" + + def __init__(self): + self._bleu_eval = bleu_eval + + def _compute_bleu_score(self, true_txt: str, pred_txt: str) -> float: + try: + result = self._bleu_eval.compute( + predictions=[pred_txt], references=[[true_txt]] + ) + bleu = result["bleu"] + except Exception: + bleu = 0.0 + return bleu + + def compute(self, true_txt: str, pred_txt: str) -> Dict[str, float]: + """Return dict of metrics for *one* (reference, prediction) pair.""" + true_tok = word_tokenize(true_txt) + pred_tok = word_tokenize(pred_txt) + true_set, pred_set = set(true_tok), set(pred_tok) + + bleu = self._compute_bleu_score(true_txt, pred_txt) + + try: + f1_ = f_measure(true_set, pred_set) + except Exception: + f1_ = 0.0 + try: + prec_ = precision(true_set, pred_set) + except Exception: + prec_ = 0.0 + try: + rec_ = recall(true_set, pred_set) + except Exception: + rec_ = 0.0 + try: + edit_dist = edit_distance(pred_tok, true_tok) / max( + len(pred_tok), len(true_tok) + ) + except: + edit_dist = 1.0 + try: + meteor = meteor_score.meteor_score([true_tok], pred_tok) + except Exception: + meteor = 0.0 + + return { + "bleu_score": bleu if bleu else 0.0, + "f1_score": f1_ if f1_ else 0.0, + "precision": prec_ if prec_ else 0.0, + "recall": rec_ if rec_ else 0.0, + "edit_distance": edit_dist if edit_dist is not None else 1.0, + "meteor": meteor if meteor else 0.0, + } + + +def _eval_pair(args: Tuple[int, str, str]) -> Dict[str, float]: + """Worker function executed in a child process. + + Each worker instantiates its own evaluator (lightweight) โ€“ avoids pickling + issues with *evaluate* objects. + """ + + idx, true_txt, pred_txt = args + evaluator = MarkdownTextEvaluator() + metrics = evaluator.compute(true_txt, pred_txt) + metrics["index"] = idx + return metrics + +def sanitize(text: str) -> str: + to_remove = [ + " ", + "", + " ", + "", + "", + "", + "", + ] + for s in to_remove: + text = text.replace(s, "") + + # pattern = r"^<_([^_>]+)_>\s(.*)" + # match = re.match(pattern, text, flags=re.DOTALL) + # if match: + # text = str(match.group(2)) # everything after the <_language_> + + # text = text.strip() + + return text + + +def compute_scores( + inferred_dir: Path, + gt_dataset_path: Path, + output_dir: Path, + num_workers: int, + split: str = "test", +) -> None: + """Evaluate prediction files in *inferred_dir* against the dataset *split*. + + Work is farmed out to a `concurrent.futures.ProcessPoolExecutor` to utilise + multiple CPU cores for tokenisation & metric computation. + """ + + inferred_dir = inferred_dir.expanduser().resolve() + output_dir = output_dir.expanduser().resolve() + output_dir.mkdir(parents=True, exist_ok=True) + + # Sort numerically by stem so that order matches dataset. + pred_files: List[Path] = sorted( + inferred_dir.iterdir(), key=lambda p: (p.stem.split(".")[0]) + ) + if not pred_files: + raise FileNotFoundError(f"No prediction files found in {inferred_dir}") + + gt_dataset = load_dataset( + str(gt_dataset_path), split=split, num_proc=num_workers or cpu_count() + ) + id_to_gt = { + str(rec["id"]): sanitize(rec["texts"][0]["assistant"]) for rec in gt_dataset + } + if len(pred_files) != len(gt_dataset): + raise ValueError( + "Length mismatch " + f"{len(pred_files)} predictions vs {len(gt_dataset)} references" + ) + + jobs = [] + for pred_path in inferred_dir.iterdir(): + pred_id = pred_path.stem + if pred_id not in id_to_gt: + print(f"โš ๏ธ Warning: {pred_id} not in ground truth dataset. Skipping.") + continue + + pred_txt = sanitize(pred_path.read_text(encoding="utf8", errors="ignore")) + true_txt = id_to_gt[pred_id] + jobs.append( + ( + pred_id, + true_txt, + pred_txt + ) + ) + + from concurrent.futures import ProcessPoolExecutor + from tqdm import tqdm + + per_example: List[Dict[str, float]] = [] + + if num_workers == 1: + # Serial path โ€“ useful for debugging or when requested. + for job in tqdm(jobs, desc="Evaluating", unit="sample"): + per_example.append(_eval_pair(job)) + else: + with ProcessPoolExecutor(max_workers=num_workers) as pool: + for result in tqdm( + pool.map(_eval_pair, jobs), + total=len(jobs), + desc="Evaluating", + unit="sample", + ): + per_example.append(result) + + # ------------------------------------------------------------------ + # Persist results & aggregate means + # ------------------------------------------------------------------ + per_example_path = output_dir / "per_example_scores.json" + mean_scores_path = output_dir / "mean_scores.json" + + with per_example_path.open("w", encoding="utf8") as f: + json.dump(per_example, f, indent=2) + + metric_keys = [ + "bleu_score", + "f1_score", + "precision", + "recall", + "edit_distance", + "meteor", + ] + mean_scores = { + k: sum(d[k] for d in per_example) / len(per_example) for k in metric_keys + } + + with mean_scores_path.open("w", encoding="utf8") as f: + json.dump(mean_scores, f, indent=2) + +def main() -> None: + parser = argparse.ArgumentParser( + description="Compute stringโ€‘similarity metrics for markdown/code recognition tasks (multiprocess)", + formatter_class=argparse.ArgumentDefaultsHelpFormatter, + ) + parser.add_argument( + "--file_source_path", type=Path, help="Directory with modelโ€‘generated snippet files." + ) + parser.add_argument( + "--gt_dir", type=Path, help="Path to the ๐Ÿค— dataset (local script or repo)." + ) + parser.add_argument( + "--output_dir", type=Path, help="Output directory for JSON results." + ) + parser.add_argument( + "--num_workers", + type=int, + default=cpu_count(), + help="How many processes to spawn (1 โ‡’ serial).", + ) + parser.add_argument( + "--split", type=str, default="test", help="Which dataset split to use." + ) + + args = parser.parse_args() + + compute_scores( + inferred_dir=args.file_source_path, + gt_dataset_path=args.gt_dir, + output_dir=args.output_dir, + num_workers=args.num_workers, + split=args.split, + ) + + +if __name__ == "__main__": + main() diff --git a/docling_eval/end_to_end/evaluate_smoldocling_end_to_end.sh b/docling_eval/end_to_end/evaluate_smoldocling_end_to_end.sh new file mode 100755 index 00000000..b6a34823 --- /dev/null +++ b/docling_eval/end_to_end/evaluate_smoldocling_end_to_end.sh @@ -0,0 +1,295 @@ +#!/usr/bin/env bash +set -euo pipefail + +source .venv/bin/activate + +# ------------------------------ +# Default values +# ------------------------------ +model_path="" # if not passed only evaluations will be performed, if inference data already present +# don't pass any of these params to avoid evaluating for this task +layout_dataset_path="" +ocr_dataset_path="" +table_dataset_path="" +equation_dataset_path="" +code_dataset_path="" + +output_dir="./output_eval" +num_workers=16 + +# ------------------------------ +# Parse args +# ------------------------------ +while [[ $# -gt 0 ]]; do + case $1 in + --model_path) model_path="$2"; shift 2 ;; + --layout_dataset_path) layout_dataset_path="$2"; shift 2 ;; + --ocr_dataset_path) ocr_dataset_path="$2"; shift 2 ;; + --table_dataset_path) table_dataset_path="$2"; shift 2 ;; + --equation_dataset_path) equation_dataset_path="$2"; shift 2 ;; + --code_dataset_path) code_dataset_path="$2"; shift 2 ;; + --output_dir) output_dir="$2"; shift 2 ;; + --num_workers) num_workers="$2"; shift 2 ;; + --help |-h) + echo "Usage: $0 [--model_path path] [--layout_dataset_path path] ... [--output_dir dir] [--num_workers N]" + exit 0 + ;; + *) echo "โŒ Unknown option $1"; exit 1 ;; + esac +done + +printf '%s\n' "-----------------------------------------------" +printf '| %-51s \n' "โœ… Parsed args:" +printf '| ๐Ÿ‘‰ %-25s = %s \n' "model_path" "$model_path" +printf '| ๐Ÿ‘‰ %-25s = %s \n' "layout_dataset_path" "$layout_dataset_path" +printf '| ๐Ÿ‘‰ %-25s = %s \n' "ocr_dataset_path" "$ocr_dataset_path" +printf '| ๐Ÿ‘‰ %-25s = %s \n' "table_dataset_path" "$table_dataset_path" +printf '| ๐Ÿ‘‰ %-25s = %s \n' "equation_dataset_path" "$equation_dataset_path" +printf '| ๐Ÿ‘‰ %-25s = %s \n' "code_dataset_path" "$code_dataset_path" +printf '| ๐Ÿ‘‰ %-25s = %s \n' "output_dir" "$output_dir" +printf '| ๐Ÿ‘‰ %-25s = %s \n' "num_workers" "$num_workers" +printf '%s\n' "-----------------------------------------------" + + + +skip_layout=false +if [ -z "$layout_dataset_path" ]; then + echo "๐Ÿšจ layout_dataset_path not passed, skipping the evaluation for this task." + skip_layout=true +fi + +skip_ocr=false +if [ -z "$ocr_dataset_path" ]; then + echo "๐Ÿšจ ocr_dataset_path not passed, skipping the evaluation for this task." + skip_ocr=true +fi + +skip_table=false +if [ -z "$table_dataset_path" ]; then + echo "๐Ÿšจ table_dataset_path not passed, skipping the evaluation for this task." + skip_table=true +fi + +skip_equation=false +if [ -z "$equation_dataset_path" ]; then + echo "๐Ÿšจ equation_dataset_path not passed, skipping the evaluation for this task." + skip_equation=true +fi + +skip_code=false +if [ -z "$code_dataset_path" ]; then + echo "๐Ÿšจ code_dataset_path not passed, skipping the evaluation for this task." + skip_code=true +fi + +mkdir -p "$output_dir" + +skip_inference=false +if [ -z "$model_path" ]; then + skip_inference=true + required=(layout ocr table equation code) + echo "๐Ÿšจ Model path has not been passed. This means that model output should already be present. Skipping Inference." + for name in "${required[@]}"; do + var="skip_${name}" + # if weโ€™re skipping this task, we must have the folder under inferred_path + if [ "${!var}" = "false" ]; then + if [ ! -d "${output_dir}/model_output/${name}" ]; then + echo "๐Ÿšจ Missing required folder '${name}' under '${output_dir}/model_output' for skipped task." + exit 1 + fi + fi + done + echo "โœ… All required inferred sub-folders are present." +fi + +printf '%s\n' "-----------------------------------------------" +printf '| %-18s %s\n' "โญ๏ธ Skip Inference?" "$skip_inference" +printf '| %-16s %s\n' "โญ๏ธ Skip Layout?" "$skip_layout" +printf '| %-16s %s\n' "โญ๏ธ Skip OCR?" "$skip_ocr" +printf '| %-16s %s\n' "โญ๏ธ Skip Table?" "$skip_table" +printf '| %-16s %s\n' "โญ๏ธ Skip Equation?" "$skip_equation" +printf '| %-16s %s\n' "โญ๏ธ Skip Code?" "$skip_code" +printf '%s\n' "-----------------------------------------------" + + + +# --------------------------------------------------------- +# Inference (run only if weโ€™re *not* skipping inference) +# --------------------------------------------------------- +if [ "$skip_inference" = false ]; then + declare -A data_paths=( + [layout]="$layout_dataset_path" + [ocr]="$ocr_dataset_path" + [table]="$table_dataset_path" + [equation]="$equation_dataset_path" + [code]="$code_dataset_path" + ) + + declare -A prompts=( + [layout]="Convert this page to docling." + [ocr]="Convert this page to docling." + [table]="Convert table to OTSL." + [equation]="Convert formula to LaTeX." + [code]="Convert code to text." + ) + + declare -A splits=( + [layout]="test" + [ocr]="test" + [table]="test" + [equation]="test" + [code]="test" + ) + + declare -A dataset_to_task + + for task in layout ocr table equation code; do + skip_var="skip_${task}" + if [ "${!skip_var}" = false ]; then + dataset="${data_paths[$task]}" + if [[ -v 'dataset_to_task["'"$dataset"'"]' ]]; then + # This dataset has already been assigned to another task + original_task="${dataset_to_task[$dataset]}" + echo "๐Ÿ” Detected identical dataset for $task and $original_task." + echo "๐Ÿ”— Creating symlink to reuse output from $original_task for $task." + mkdir -p "${output_dir}/model_output" + ln -sfn "${original_task}" "${output_dir}/model_output/${task}" + else + dataset_to_task[$dataset]="$task" + fi + fi + done + + for task in layout ocr table equation code; do + skip_var="skip_${task}" + if [ "${!skip_var}" = false ]; then + if [ ! -L "${output_dir}/model_output/${task}" ]; then + echo "๐Ÿš€ Performing inference for **${task^^}** on dataset ${data_paths[$task]}" + mkdir -p "${output_dir}/model_output/${task}" + uv run python3 docling_eval/end_to_end/batch_inference_vllm.py \ + --model_path "$model_path" \ + --dataset_path "${data_paths[$task]}" \ + --split "${splits[$task]}" \ + --output_dir "${output_dir}/model_output/${task}" \ + --num_workers "$num_workers" \ + --prompt "${prompts[$task]}" + else + echo "โญ๏ธ Skipping inference for ${task} (already linked to another task)." + fi + fi + done +fi + +# normalize equations +# if [ "$skip_equation" = false ]; then +# echo "๐Ÿงฎ Normalizing equations..." +# mkdir -p "${output_dir}/model_output/equation_normalized" +# uv run python3 docling_eval/end_to_end/normalize_equations.py \ +# --input_dir "${output_dir}/model_output/equation" \ +# --output_dir "${output_dir}/model_output/equation_normalized" \ +# --num_workers "$num_workers" +# printf '%s\n' "-----------------------------------------------" +# fi + +# --------------------------------------------------------- +# Evaluation & visualisation with `docling_eval` +# --------------------------------------------------------- +declare -A modality=( + [layout]="layout" + [ocr]="markdown_text" + [table]="table_structure" +) + +declare -A dataset_paths=( + [layout]="$layout_dataset_path" + [ocr]="$ocr_dataset_path" + [table]="$table_dataset_path" + [equation]="$equation_dataset_path" + [code]="$code_dataset_path" +) + +declare -A output_dirs=( + [layout]="${output_dir}/evals/layout/" + [ocr]="${output_dir}/evals/ocr/" + [table]="${output_dir}/evals/table/" + [equation]="${output_dir}/evals/equation/" + [code]="${output_dir}/evals/code/" +) + +declare -A splits=( + [layout]="test" + [ocr]="test" + [table]="test" + [equation]="test" + [code]="test" +) + +declare -A benchmarks=( + [layout]="DocLayNetV2" + [ocr]="DocLayNetV2" + [table]="FinTabNet" +) + +declare -A file_source_paths=( + [layout]="${output_dir}/model_output/layout" + [ocr]="${output_dir}/model_output/ocr" + [table]="${output_dir}/model_output/table" + [equation]="${output_dir}/model_output/equation" + [code]="${output_dir}/model_output/code" + +) +# evaluate tasks for which you can use docling_eval +for task in layout ocr table; do + skip_var="skip_${task}" + if [ "${!skip_var}" = false ]; then + echo "๐Ÿ“ Evaluating **${task}** (modality: ${modality[$task]}) โ€ฆ" + + echo " ๐Ÿ‘‰ Running create-eval โ€ฆ" + uv run docling-eval \ + create-eval \ + --benchmark ${benchmarks[$task]} \ + --gt-dir ${dataset_paths[$task]}\ + --prediction-provider File \ + --file-prediction-format doctags \ + --file-source-path ${file_source_paths[$task]} \ + --output-dir "${output_dirs[$task]}" + + echo " ๐Ÿ‘‰ Running evaluate โ€ฆ" + uv run docling-eval evaluate \ + --modality "${modality[$task]}" \ + --benchmark "${benchmarks[$task]}" \ + --output-dir "${output_dirs[$task]}" \ + --split "${splits[$task]}" + + echo " ๐Ÿ‘‰ Running visualize โ€ฆ" + uv run docling-eval visualize \ + --modality "${modality[$task]}" \ + --benchmark "${benchmarks[$task]}" \ + --output-dir "${output_dirs[$task]}" \ + --split "${splits[$task]}" + printf '%s\n' "-----------------------------------------------" + else + echo "โญ๏ธ Skipping evaluation for ${task} (skip_${task}=true)" + printf '%s\n' "-----------------------------------------------" + fi +done + + +# evaluate tasks for which you CAN'T use docling_eval +for task in code equation; do + skip_var="skip_${task}" + if [ "${!skip_var}" = false ]; then + echo "๐Ÿ“ Evaluating **${task}** โ€ฆ" + + uv run python3 docling_eval/end_to_end/evaluate_code_equations.py \ + --file_source_path ${file_source_paths[$task]} \ + --gt_dir ${dataset_paths[$task]} \ + --output_dir "${output_dirs[$task]}" \ + --num_workers "$num_workers" \ + --split "${splits[$task]}" + else + echo "โญ๏ธ Skipping evaluation for ${task} (skip_${task}=true)" + fi +done + +# bsub -q normal -n 1 -R "span[hosts=1]" -M 200G -gpu "num=1:mode=exclusive_process" -oo ~/.lsbatch/evaluate.stdout -eo ~/.lsbatch/evaluate.stderr docling_eval/end_to_end/evaluate_smoldocling_end_to_end.sh --model_path /gpfs/ZuFS1/proj/deep-search/mao/repos/docling-eval/checkpoints/19062025_last_model --layout_dataset_path /gpfs/ZuFS1/proj/deep-search/datasets/doclaynet-v2-docling-GT/gt_dataset --ocr_dataset_path /gpfs/ZuFS1/proj/deep-search/datasets/doclaynet-v2-docling-GT/gt_dataset --code_dataset_path /gpfs/ZuFS1/proj/deep-search/mao/datasets/synth_code_net_test_set_5k --equation_dataset_path /gpfs/ZuFS1/proj/deep-search/mao/datasets/im2latex230k_ood_test_set/test_hf_format --table_dataset_path /gpfs/ZuFS1/proj/deep-search/mao/datasets/FinTabNet_OTSL_v1.2_doclingdocuments/gt_dataset \ No newline at end of file diff --git a/docling_eval/end_to_end/normalize_equations.py b/docling_eval/end_to_end/normalize_equations.py new file mode 100644 index 00000000..b0abf222 --- /dev/null +++ b/docling_eval/end_to_end/normalize_equations.py @@ -0,0 +1,517 @@ +from functools import partial +import pandas as pd +import random +import re + +random.seed(45) +from typing import List, Dict +from multiprocessing import Pool, cpu_count +import argparse +from pathlib import Path + + +class RegexTokenizer: + def __init__(self, tokens: List[str]): + # Escape tokens to ensure special characters are treated as literals + escaped_tokens = [ + re.escape(token) for token in sorted(tokens, key=len, reverse=True) + ] + # Create a regex pattern that matches any of the tokens + self.pattern = re.compile("|".join(escaped_tokens)) + # Create mapping dictionaries + self.token_to_id = {token: idx for idx, token in enumerate(tokens)} + self.id_to_token = {idx: token for token, idx in self.token_to_id.items()} + + def encode(self, text: str) -> List[int]: + matches = self.pattern.findall(text) + return [ + self.token_to_id[match] for match in matches if match in self.token_to_id + ] + + def decode(self, token_ids: List[int]) -> str: + return "".join( + [ + self.id_to_token[token_id] + for token_id in token_ids + if token_id in self.id_to_token + ] + ) + + def tokenize(self, text: str) -> List[str]: + tokens = self.pattern.findall(text) + return tokens + +tokens = pd.read_csv("docling_eval/end_to_end/equation_config/tokens.csv") +all_tokens = set(tokens["token"].tolist()) +tokens_to_keep = set(tokens.loc[tokens["policy"].isin([0])]["token"].tolist()) +tokens_to_drop = set(tokens.loc[tokens["policy"] == 1]["token"].tolist()) +tokens_to_replace = dict( + zip( + tokens.loc[tokens["policy"] == 2, "token"], + tokens.loc[tokens["policy"] == 2, "replace"].fillna(""), + ) +) +remove_with_braces_tokens = set(tokens.loc[tokens["policy"] == 3]["token"].tolist()) +one_braces_tokens = set(tokens.loc[tokens["policy"] == 4]["token"].tolist()) +two_braces_tokens = set(tokens.loc[tokens["policy"] == 5]["token"].tolist()) +remove_with_braces_tokens_keep_tokens = set( + tokens.loc[tokens["policy"] == 6]["token"].tolist() +) +remove_content_tokens = set(tokens.loc[tokens["policy"] == 7]["token"].tolist()) +tokenizer = RegexTokenizer(list(all_tokens)) + + +def handle_spaces(tokenized_equation): + in_text = False + braces_counter = 0 + new_equation = [] + for token in tokenized_equation: + if not in_text and token == " ": + continue + + if token == r"\text": + in_text = True + + if in_text and token == r"{": + braces_counter += 1 + elif in_text and token == r"}": + braces_counter -= 1 + if braces_counter == 0: + in_text = False + + new_equation.append(token) + + return new_equation + +def equation_join(split_equation: List[str]) -> str: + """ + Joins a list of LaTeX tokens into a single string, adding spaces between tokens except + when inside a '\\text{...}' block, where tokens are joined without spaces. + + Parameters: + - split_equation (list of str): A list of LaTeX tokens. + + Returns: + - str: The joined LaTeX equation as a single string. + + The function handles '\\text{...}' blocks by tracking the nesting of braces to ensure + that all content within the braces is joined without additional spaces. This preserves + the intended formatting of text within LaTeX equations. + + Example: + >>> split_equation = [ + ... "A", "_", "7", "+", "A", "_", "8", "-", "2", r"\text", "{", + ... "A", " ", "i", "s", " ", "a", " ", "p", "a", "r", "a", "m", "e", "t", "e", "r", "}" + ... ] + >>> equation_join(split_equation) + 'A _ 7 + A _ 8 - 2 \\text{A is a parameter}' + """ + in_text = False + braces_counter = 0 + joined_equation = "" + for token in split_equation: + if token == r"\text": + in_text = True + joined_equation += token + " " + continue + + if in_text and token == r"{": + braces_counter += 1 + elif in_text and token == r"}": + braces_counter -= 1 + if braces_counter == 0: + in_text = False + + if in_text: + joined_equation += token + else: + joined_equation += token + " " + + return joined_equation.strip() + + +def replace_tokens( + equation_tokenized: List[str], to_replace_tokens: Dict[str, str] +) -> List[str]: + equation_tokenized = [ + to_replace_tokens[token] if token in to_replace_tokens else token + for token in equation_tokenized + ] + equation_tokenized = [token for token in equation_tokenized if token] + + return equation_tokenized + + +def remove_with_braces( + equation: str, remove_with_braces_tokens: List[str], keep_tokens: bool +): + for token in remove_with_braces_tokens: + pattern = rf"\{token}\s*\{{" + output = "" + pos = 0 + while True: + match = re.search(pattern, equation[pos:]) + if not match: + output += equation[pos:] + break + start = pos + match.start() + end = pos + match.end() + if keep_tokens: + output += ( + equation[pos : start + len(token)] + " " + ) # Append text before and including \command + else: + output += equation[pos:start] # Append text before \command{ + # Now find the matching closing brace + depth = 1 + i = end + while i < len(equation) and depth > 0: + if equation[i] == "{": + depth += 1 + elif equation[i] == "}": + depth -= 1 + i += 1 + if depth != 0: + # print(f"Unbalanced braces after position {end}") + return None + content = equation[end : i - 1] # Extract content inside braces + output += content # Append the content without \command{ and } + pos = i # Move the position forward + + equation = output + + return equation + + +def remove_tokens_and_content( + equation: str, + remove_content_tokens: List[str], +): + for token in remove_content_tokens: + pattern = rf"\{token}\s*\{{" + output = "" + pos = 0 + while True: + match = re.search(pattern, equation[pos:]) + if not match: + output += equation[pos:] + break + start = pos + match.start() + end = pos + match.end() + + output += equation[pos:start] # Append text before \command{ + # Now find the matching closing brace + depth = 1 + i = end + while i < len(equation) and depth > 0: + if equation[i] == "{": + depth += 1 + elif equation[i] == "}": + depth -= 1 + i += 1 + if depth != 0: + # print(f"Unbalanced braces after position {end}") + return None + pos = i # Move the position forward + + equation = output + + return equation + + +def normalize_one_braces_tokens(equation_tokenized: List[str], one_braces_tokens): + new_equation = [] + i = 0 + while i < len(equation_tokenized): + token = equation_tokenized[i] + new_equation.append(token) + i = i + 1 + if i < len(equation_tokenized): + next_token = equation_tokenized[i] + if token in one_braces_tokens and next_token != r"{": + new_equation.extend(["{", next_token, "}"]) + i = i + 1 + + return new_equation + + +def normalize_two_braces_tokens(equation_tokenized: List[str], two_braces_tokens): + new_equation = [] + i = 0 + while i < len(equation_tokenized): + token = equation_tokenized[i] + new_equation.append(token) + i = i + 1 + if i < len(equation_tokenized) - 1: + next_token = equation_tokenized[i] + next_next_token = equation_tokenized[i + 1] + if token in two_braces_tokens and next_token != r"{": + new_equation.extend(["{", next_token, "}", "{", next_next_token, "}"]) + i = i + 2 + + return new_equation + + +def normalize_left_right(equation: str) -> str: + left_delimiters = [ + "(", + "[", + "\\{", + "|", + "\\|", + "\\lceil", + "\\lfloor", + "\\langle", + ] + right_delimiters = [ + ")", + "]", + "\\}", + "|", + "\\|", + "\\rceil", + "\\rfloor", + "\\rangle", + ] + + words_pattern = "|".join(map(re.escape, left_delimiters)) + pattern = rf"\{{\s*\\left\s*({words_pattern})\s*\}}" + tmp_equation = re.sub(pattern, r"\\left \1", equation) + + words_pattern = "|".join(map(re.escape, right_delimiters)) + pattern = rf"\{{\s*\\right\s*({words_pattern})\s*\}}" + new_equation = re.sub(pattern, r"\\right \1", tmp_equation) + + return new_equation + + +def handle_big(equation_tokenized: List[str]) -> List[str]: + """ + Replaces instances of \\Big followed by valid tokens ( (, [, {, ), ], } ) with \\left or \\right. + """ + new_equation = [] + i = 0 + seen_left_bar = False + counter_left = 0 + counter_right = 0 + while i < len(equation_tokenized) - 1: + token = equation_tokenized[i] + next_token = equation_tokenized[i + 1] + + if ( + token == "\\Big" + and (next_token == "|" or next_token == "\\|") + and not seen_left_bar + ): + new_equation.append("\\left") + seen_left_bar = True + counter_left += 1 + elif ( + token == "\\Big" + and (next_token == "|" or next_token == "\\|") + and seen_left_bar + ): + new_equation.append("\\right") + seen_left_bar = False + counter_right += 1 + elif token == "\\Big" and next_token in [ + "(", + "[", + r"\{", + r"\lceil", + r"\lfloor", + r"\langle", + ]: + new_equation.append("\\left") + counter_left += 1 + elif token == "\\Big" and next_token in [ + ")", + "]", + r"\}", + r"\rceil", + r"\rfloor", + r"\rangle", + ]: + new_equation.append("\\right") + counter_right += 1 + else: + new_equation.append(token) + + i += 1 + # Add the last token + if equation_tokenized and equation_tokenized[-1] != "\\Big": + new_equation.append(equation_tokenized[-1]) + + if counter_left != counter_right: + return equation_tokenized + + return new_equation + + +def handle_prime(equation): + pattern = r"(?:'\s*)+" + + def replacement(match): + count = match.group().count("'") + return "^{" + "\\prime" * count + "}" + + return re.sub(pattern, replacement, equation) + + +def handle_multiple_subsequent_braces(equation): + """ + Simplify a LaTeX equation by removing unnecessary multiple curly braces. + + Args: + equation (str): The LaTeX equation to simplify. + + Returns: + str: The simplified LaTeX equation. + """ + pattern = r"\{\s*\{([^{}]+)\}\s*\}" + prev_equation = None + while equation != prev_equation: + prev_equation = equation + # Replace redundant multiple braces with a single brace + equation = re.sub(pattern, r"{\1}", equation) + return equation + +def handle_multiple_subsequent_verts(equation): + """ + Simplify a LaTeX equation by removing unnecessary multiple curly braces. + + Args: + equation (str): The LaTeX equation to simplify. + + Returns: + str: The simplified LaTeX equation. + """ + pattern = r"\\\|\s*\\\|([^\\\|]+)\\\|\s*\\\|" + prev_equation = None + while equation != prev_equation: + prev_equation = equation + # Replace redundant multiple braces with a single brace + equation = re.sub(pattern, r"\\|\1\\|", equation) + return equation + + +def normalize(equation: str) -> str: + equation = ( + equation.replace("", "") + .replace("", "") + .replace("", "") + .replace("", "") + .replace("", "") + ) + equation = remove_with_braces(equation, remove_with_braces_tokens, False) + if not equation: + return None + equation = remove_tokens_and_content(equation, remove_content_tokens) + if not equation: + return None + equation = remove_with_braces(equation, remove_with_braces_tokens_keep_tokens, True) + if not equation: + return None + equation = normalize_left_right(equation) + + equation = handle_multiple_subsequent_braces(equation) + equation = handle_multiple_subsequent_verts(equation) + equation = handle_prime(equation) + + if not equation: + return None + + equation = equation.strip() + equation_tokenized = tokenizer.tokenize(equation) + + if len(set(equation_tokenized).intersection(tokens_to_drop)) > 0: + return None + + equation_tokenized = handle_spaces(equation_tokenized) + + if not equation_tokenized: + return None + + equation_tokenized = replace_tokens(equation_tokenized, tokens_to_replace) + equation_tokenized = normalize_one_braces_tokens( + equation_tokenized, one_braces_tokens + ) + equation_tokenized = normalize_two_braces_tokens( + equation_tokenized, two_braces_tokens + ) + equation_tokenized = handle_big(equation_tokenized) + + equation = equation_join(equation_tokenized) + + equation = equation.replace(" ", " ").replace(r"\text {", r"\text{").strip() + + equation_tokenized = tokenizer.tokenize(equation) + equation_tokenized = handle_spaces(equation_tokenized) + return equation_join(equation_tokenized) + + +def _process_file(file_path: Path, out_dir: Path) -> tuple[str, bool]: + """ + Read one-line equation from *file_path*, normalize it, and write the result + to *out_dir/file_path.name*. + Returns (filename, True) on success, (filename, False) if normalization + returned None. + """ + try: + eq = file_path.read_text(encoding="utf-8").strip() + except Exception as e: + return (file_path.name, False) + + normalized_equation = normalize(eq) # may be None + flag = True + if normalized_equation is None: + normalized_equation = eq + flag = False + (out_dir / file_path.name).write_text(normalized_equation, encoding="utf-8") + + return (file_path.name, flag) + + +def main() -> None: + parser = argparse.ArgumentParser( + description="Normalize LaTeX-equation files in parallel." + ) + parser.add_argument( + "--input_dir", + type=Path, + help="Directory containing text files, each with a single equation.", + ) + parser.add_argument( + "--output_dir", + type=Path, + help="Directory to receive normalized files (created if absent).", + ) + parser.add_argument( + "-w", + "--num_workers", + type=int, + default=cpu_count(), + metavar="N", + help="Number of worker processes (default: all logical cores).", + ) + args = parser.parse_args() + + if not args.input_dir.is_dir(): + parser.error(f"{args.input_dir} is not a directory.") + args.output_dir.mkdir(parents=True, exist_ok=True) + + files = sorted(p for p in args.input_dir.iterdir() if p.is_file()) + if not files: + parser.error(f"No files found in {args.input_dir}") + + with Pool(processes=min(args.num_workers, cpu_count())) as pool: + process = partial(_process_file, out_dir=args.output_dir) + results = pool.map(process, files) + + ok = sum(flag for _, flag in results) + fail = len(results) - ok + print(f"โœ“ Normalized {ok} file(s); โœ— skipped {fail} file(s).") + + +if __name__ == "__main__": + main() diff --git a/docling_eval/end_to_end/read_dataset.py b/docling_eval/end_to_end/read_dataset.py new file mode 100644 index 00000000..0864629b --- /dev/null +++ b/docling_eval/end_to_end/read_dataset.py @@ -0,0 +1,94 @@ +from pathlib import Path +import pandas as pd + +import os +import math +from multiprocessing import Pool +from datasets.utils.py_utils import convert_file_size_to_int +from PIL import Image +from datasets import load_dataset + +def save_shard(shard_info): + """ + shard_info is a tuple of (index, shard, shard_path). + We call to_parquet on the shard, writing it out to shard_path. + """ + index, shard, shard_path = shard_info + shard.to_parquet(shard_path) + + +def to_parquet(ds, output_dir, num_proc): + os.makedirs(output_dir, exist_ok=True) + + dataset_nbytes = ds._estimate_nbytes() + max_shard_size = convert_file_size_to_int("500MB") + num_shards = max(math.ceil(dataset_nbytes / max_shard_size), 1) + + # Build all shard subsets in a list + shard_infos = [] + for i in range(num_shards): + shard = ds.shard(num_shards=num_shards, index=i, contiguous=True) + shard_path = os.path.join( + output_dir, f"{i:05d}-of-{(num_shards - 1):05d}.parquet" + ) + shard_infos.append((i, shard, shard_path)) + + # Start a worker pool and map the shards + with Pool(processes=num_proc) as pool: + pool.map(save_shard, shard_infos) + + +# Load the dataset from a local directory containing .parquet files +dataset = load_dataset( + "/gpfs/ZuFS1/proj/deep-search/mao/datasets/FinTabNet_OTSL_v1.2_doclingdocuments/gt_dataset", + split="test", +) +print(dataset) +for i, row in enumerate(dataset): + if i == 1: + break + print(row) + + +# df = pd.read_csv( +# "/gpfs/ZuFS1/proj/deep-search/mao/datasets/im2latex230k_ood_test_set/test.csv" +# ) +# df = df.sample(frac=1, random_state=42).reset_index(drop=True) # shuffle +# df = df.iloc[:10000] # select first 10k rows +# print(df) +# images_path = Path( +# "/gpfs/ZuFS1/proj/deep-search/mao/datasets/im2latex230k_ood_test_set/images" +# ) + +# rows = [] +# for i, row in df.iterrows(): +# formula = row["formula"] + +# texts = [ +# {"assistant": f"{formula}", +# "user": "Convert formula to LaTeX." +# } +# ] +# filename = row["filename"] +# images = [Image.open(images_path / filename).convert("RGB")] + +# rows.append( +# { +# "texts": texts, +# "images": images, +# "id": filename.replace(".png", "") +# } +# ) + +# from datasets import Dataset +# from datasets.features import Features, Value, Sequence, Image as HFImage + +# # Convert rows to Hugging Face dataset +# ds = Dataset.from_list(rows) + +# # Save as sharded parquet +# to_parquet( +# ds, +# "/gpfs/ZuFS1/proj/deep-search/mao/datasets/im2latex230k_ood_test_set/test", +# num_proc=128, +# ) From b05a524ef050b087a3c1d6947a39f67e1664321e Mon Sep 17 00:00:00 2001 From: mao Date: Tue, 1 Jul 2025 10:03:12 +0200 Subject: [PATCH 13/21] wip --- .gitignore | 2 +- docling_eval/end_to_end/doctag_stast.py | 28 +++++++++++++++++++ .../evaluate_smoldocling_end_to_end.sh | 4 +-- 3 files changed, 31 insertions(+), 3 deletions(-) create mode 100644 docling_eval/end_to_end/doctag_stast.py diff --git a/.gitignore b/.gitignore index 7ca6e354..5a82d274 100644 --- a/.gitignore +++ b/.gitignore @@ -443,4 +443,4 @@ pip-selfcheck.json # docs/**/*.png # docs/**/*.svg checkpoints/* -output_eval/* \ No newline at end of file +outputs_evals/* \ No newline at end of file diff --git a/docling_eval/end_to_end/doctag_stast.py b/docling_eval/end_to_end/doctag_stast.py new file mode 100644 index 00000000..df09aa85 --- /dev/null +++ b/docling_eval/end_to_end/doctag_stast.py @@ -0,0 +1,28 @@ +from pathlib import Path + +folder = Path( + "/gpfs/ZuFS1/proj/deep-search/mao/repos/docling-eval/output_eval_no_rl/model_output/ocr" +) # change this +target_string = "" + +total_files = 0 +matching_files = 0 + +for file in folder.iterdir(): + if file.is_file(): + total_files += 1 + try: + content = file.read_text(encoding="utf-8", errors="ignore") + if target_string in content: + matching_files += 1 + except Exception as e: + print(f"Error reading {file}: {e}") + +print("๐Ÿ“Š Stats:") +print(f"Total files checked: {total_files}") +print(f"Files containing tag: {matching_files}") +print( + f"Percentage: {matching_files / total_files * 100:.2f}%" + if total_files + else "N/A" +) diff --git a/docling_eval/end_to_end/evaluate_smoldocling_end_to_end.sh b/docling_eval/end_to_end/evaluate_smoldocling_end_to_end.sh index b6a34823..b6811f32 100755 --- a/docling_eval/end_to_end/evaluate_smoldocling_end_to_end.sh +++ b/docling_eval/end_to_end/evaluate_smoldocling_end_to_end.sh @@ -14,7 +14,7 @@ table_dataset_path="" equation_dataset_path="" code_dataset_path="" -output_dir="./output_eval" +output_dir="./output_eval_no_rl" num_workers=16 # ------------------------------ @@ -292,4 +292,4 @@ for task in code equation; do fi done -# bsub -q normal -n 1 -R "span[hosts=1]" -M 200G -gpu "num=1:mode=exclusive_process" -oo ~/.lsbatch/evaluate.stdout -eo ~/.lsbatch/evaluate.stderr docling_eval/end_to_end/evaluate_smoldocling_end_to_end.sh --model_path /gpfs/ZuFS1/proj/deep-search/mao/repos/docling-eval/checkpoints/19062025_last_model --layout_dataset_path /gpfs/ZuFS1/proj/deep-search/datasets/doclaynet-v2-docling-GT/gt_dataset --ocr_dataset_path /gpfs/ZuFS1/proj/deep-search/datasets/doclaynet-v2-docling-GT/gt_dataset --code_dataset_path /gpfs/ZuFS1/proj/deep-search/mao/datasets/synth_code_net_test_set_5k --equation_dataset_path /gpfs/ZuFS1/proj/deep-search/mao/datasets/im2latex230k_ood_test_set/test_hf_format --table_dataset_path /gpfs/ZuFS1/proj/deep-search/mao/datasets/FinTabNet_OTSL_v1.2_doclingdocuments/gt_dataset \ No newline at end of file +# bsub -q normal -n 1 -R "span[hosts=1]" -M 200G -gpu "num=1:mode=exclusive_process" -oo ~/.lsbatch/evaluate_2.stdout -eo ~/.lsbatch/evaluate_2stderr docling_eval/end_to_end/evaluate_smoldocling_end_to_end.sh --model_path /gpfs/ZuFS1/proj/deep-search/mao/repos/sql-reinforce/checkpoints/smoldocling/sft/SmolDocling_250M_DT_aug --layout_dataset_path /gpfs/ZuFS1/proj/deep-search/datasets/doclaynet-v2-docling-GT/gt_dataset --ocr_dataset_path /gpfs/ZuFS1/proj/deep-search/datasets/doclaynet-v2-docling-GT/gt_dataset --code_dataset_path /gpfs/ZuFS1/proj/deep-search/mao/datasets/synth_code_net_test_set_5k --equation_dataset_path /gpfs/ZuFS1/proj/deep-search/mao/datasets/im2latex230k_ood_test_set/test_hf_format --table_dataset_path /gpfs/ZuFS1/proj/deep-search/mao/datasets/FinTabNet_OTSL_v1.2_doclingdocuments/gt_dataset \ No newline at end of file From f51d7bcffa3d31656e021c6d43727b565f2179c1 Mon Sep 17 00:00:00 2001 From: mao Date: Wed, 9 Jul 2025 11:13:52 +0200 Subject: [PATCH 14/21] moving to vela --- .../evaluate_smoldocling_end_to_end.sh | 10 +- .../evaluate_smoldocling_end_to_end_vela.sh | 294 ++++++++++++++++++ 2 files changed, 301 insertions(+), 3 deletions(-) create mode 100644 docling_eval/end_to_end/evaluate_smoldocling_end_to_end_vela.sh diff --git a/docling_eval/end_to_end/evaluate_smoldocling_end_to_end.sh b/docling_eval/end_to_end/evaluate_smoldocling_end_to_end.sh index b6811f32..30682880 100755 --- a/docling_eval/end_to_end/evaluate_smoldocling_end_to_end.sh +++ b/docling_eval/end_to_end/evaluate_smoldocling_end_to_end.sh @@ -14,8 +14,8 @@ table_dataset_path="" equation_dataset_path="" code_dataset_path="" -output_dir="./output_eval_no_rl" -num_workers=16 +output_dir="./outputs_evals/output_eval_rl_new_code" +num_workers=64 # ------------------------------ # Parse args @@ -292,4 +292,8 @@ for task in code equation; do fi done -# bsub -q normal -n 1 -R "span[hosts=1]" -M 200G -gpu "num=1:mode=exclusive_process" -oo ~/.lsbatch/evaluate_2.stdout -eo ~/.lsbatch/evaluate_2stderr docling_eval/end_to_end/evaluate_smoldocling_end_to_end.sh --model_path /gpfs/ZuFS1/proj/deep-search/mao/repos/sql-reinforce/checkpoints/smoldocling/sft/SmolDocling_250M_DT_aug --layout_dataset_path /gpfs/ZuFS1/proj/deep-search/datasets/doclaynet-v2-docling-GT/gt_dataset --ocr_dataset_path /gpfs/ZuFS1/proj/deep-search/datasets/doclaynet-v2-docling-GT/gt_dataset --code_dataset_path /gpfs/ZuFS1/proj/deep-search/mao/datasets/synth_code_net_test_set_5k --equation_dataset_path /gpfs/ZuFS1/proj/deep-search/mao/datasets/im2latex230k_ood_test_set/test_hf_format --table_dataset_path /gpfs/ZuFS1/proj/deep-search/mao/datasets/FinTabNet_OTSL_v1.2_doclingdocuments/gt_dataset \ No newline at end of file +# bsub -q normal -n 1 -R "span[hosts=1]" -M 200G -gpu "num=1:mode=exclusive_process" -oo ~/.lsbatch/evaluate_gd.stdout -eo ~/.lsbatch/evaluate_gd.stderr docling_eval/end_to_end/evaluate_smoldocling_end_to_end.sh --model_path /gpfs/ZuFS1/proj/deep-search/mao/repos/docling-eval/checkpoints/granitedocling_v06.5_stg_4_RETRAINED_checkpoint-18101 --layout_dataset_path /gpfs/ZuFS1/proj/deep-search/datasets/doclaynet-v2-docling-GT/gt_dataset --ocr_dataset_path /gpfs/ZuFS1/proj/deep-search/datasets/doclaynet-v2-docling-GT/gt_dataset --code_dataset_path /gpfs/ZuFS1/proj/deep-search/mao/datasets/synth_code_net_test_set_5k --equation_dataset_path /gpfs/ZuFS1/proj/deep-search/mao/datasets/im2latex230k_ood_test_set/test_hf_format --table_dataset_path /gpfs/ZuFS1/proj/deep-search/mao/datasets/FinTabNet_OTSL_v1.2_doclingdocuments/gt_dataset --output_dir /gpfs/ZuFS1/proj/deep-search/mao/repos/docling-eval/outputs_evals/output_eval_granite_docling + + +# bsub -q normal -n 1 -R "span[hosts=1]" -M 200G -gpu "num=1:mode=exclusive_process" -oo ~/.lsbatch/evaluate.stdout -eo ~/.lsbatch/evaluate.stderr docling_eval/end_to_end/evaluate_smoldocling_end_to_end.sh --layout_dataset_path /gpfs/ZuFS1/proj/deep-search/datasets/doclaynet-v2-docling-GT/gt_dataset --ocr_dataset_path /gpfs/ZuFS1/proj/deep-search/datasets/doclaynet-v2-docling-GT/gt_dataset --code_dataset_path /gpfs/ZuFS1/proj/deep-search/mao/datasets/synth_code_net_test_set_5k --equation_dataset_path /gpfs/ZuFS1/proj/deep-search/mao/datasets/im2latex230k_ood_test_set/test_hf_format --table_dataset_path /gpfs/ZuFS1/proj/deep-search/mao/datasets/FinTabNet_OTSL_v1.2_doclingdocuments/gt_dataset + diff --git a/docling_eval/end_to_end/evaluate_smoldocling_end_to_end_vela.sh b/docling_eval/end_to_end/evaluate_smoldocling_end_to_end_vela.sh new file mode 100644 index 00000000..05415fab --- /dev/null +++ b/docling_eval/end_to_end/evaluate_smoldocling_end_to_end_vela.sh @@ -0,0 +1,294 @@ +#!/usr/bin/env bash +set -euo pipefail + +timestamp=$(date +"%Y%m%d-%H%M%S") +uuid=$(uuidgen | tr '[:upper:]' '[:lower:]' | cut -c1-8) + +# ------------------------------ +# Default values +# ------------------------------ +model_path="" # if not passed only evaluations will be performed, if inference data already present +# don't pass any of these params to avoid evaluating for this task +layout_dataset_path="" +ocr_dataset_path="" +table_dataset_path="" +equation_dataset_path="" +code_dataset_path="" + +output_dir="./outputs_evals/output_eval_${timestamp}_${uuid}" +num_workers=8 + +# ------------------------------ +# Parse args +# ------------------------------ +while [[ $# -gt 0 ]]; do + case $1 in + --model_path) model_path="$2"; shift 2 ;; + --layout_dataset_path) layout_dataset_path="$2"; shift 2 ;; + --ocr_dataset_path) ocr_dataset_path="$2"; shift 2 ;; + --table_dataset_path) table_dataset_path="$2"; shift 2 ;; + --equation_dataset_path) equation_dataset_path="$2"; shift 2 ;; + --code_dataset_path) code_dataset_path="$2"; shift 2 ;; + --output_dir) output_dir="$2"; shift 2 ;; + --num_workers) num_workers="$2"; shift 2 ;; + --help |-h) + echo "Usage: $0 [--model_path path] [--layout_dataset_path path] ... [--output_dir dir] [--num_workers N]" + exit 0 + ;; + *) echo "โŒ Unknown option $1"; exit 1 ;; + esac +done + +printf '%s\n' "-----------------------------------------------" +printf '| %-51s \n' "โœ… Parsed args:" +printf '| ๐Ÿ‘‰ %-25s = %s \n' "model_path" "$model_path" +printf '| ๐Ÿ‘‰ %-25s = %s \n' "layout_dataset_path" "$layout_dataset_path" +printf '| ๐Ÿ‘‰ %-25s = %s \n' "ocr_dataset_path" "$ocr_dataset_path" +printf '| ๐Ÿ‘‰ %-25s = %s \n' "table_dataset_path" "$table_dataset_path" +printf '| ๐Ÿ‘‰ %-25s = %s \n' "equation_dataset_path" "$equation_dataset_path" +printf '| ๐Ÿ‘‰ %-25s = %s \n' "code_dataset_path" "$code_dataset_path" +printf '| ๐Ÿ‘‰ %-25s = %s \n' "output_dir" "$output_dir" +printf '| ๐Ÿ‘‰ %-25s = %s \n' "num_workers" "$num_workers" +printf '%s\n' "-----------------------------------------------" + + + +skip_layout=false +if [ -z "$layout_dataset_path" ]; then + echo "๐Ÿšจ layout_dataset_path not passed, skipping the evaluation for this task." + skip_layout=true +fi + +skip_ocr=false +if [ -z "$ocr_dataset_path" ]; then + echo "๐Ÿšจ ocr_dataset_path not passed, skipping the evaluation for this task." + skip_ocr=true +fi + +skip_table=false +if [ -z "$table_dataset_path" ]; then + echo "๐Ÿšจ table_dataset_path not passed, skipping the evaluation for this task." + skip_table=true +fi + +skip_equation=false +if [ -z "$equation_dataset_path" ]; then + echo "๐Ÿšจ equation_dataset_path not passed, skipping the evaluation for this task." + skip_equation=true +fi + +skip_code=false +if [ -z "$code_dataset_path" ]; then + echo "๐Ÿšจ code_dataset_path not passed, skipping the evaluation for this task." + skip_code=true +fi + +mkdir -p "$output_dir" + +skip_inference=false +if [ -z "$model_path" ]; then + skip_inference=true + required=(layout ocr table equation code) + echo "๐Ÿšจ Model path has not been passed. This means that model output should already be present. Skipping Inference." + for name in "${required[@]}"; do + var="skip_${name}" + # if weโ€™re skipping this task, we must have the folder under inferred_path + if [ "${!var}" = "false" ]; then + if [ ! -d "${output_dir}/model_output/${name}" ]; then + echo "๐Ÿšจ Missing required folder '${name}' under '${output_dir}/model_output' for skipped task." + exit 1 + fi + fi + done + echo "โœ… All required inferred sub-folders are present." +fi + +printf '%s\n' "-----------------------------------------------" +printf '| %-18s %s\n' "โญ๏ธ Skip Inference?" "$skip_inference" +printf '| %-16s %s\n' "โญ๏ธ Skip Layout?" "$skip_layout" +printf '| %-16s %s\n' "โญ๏ธ Skip OCR?" "$skip_ocr" +printf '| %-16s %s\n' "โญ๏ธ Skip Table?" "$skip_table" +printf '| %-16s %s\n' "โญ๏ธ Skip Equation?" "$skip_equation" +printf '| %-16s %s\n' "โญ๏ธ Skip Code?" "$skip_code" +printf '%s\n' "-----------------------------------------------" + + + +# --------------------------------------------------------- +# Inference (run only if weโ€™re *not* skipping inference) +# --------------------------------------------------------- +if [ "$skip_inference" = false ]; then + declare -A data_paths=( + [layout]="$layout_dataset_path" + [ocr]="$ocr_dataset_path" + [table]="$table_dataset_path" + [equation]="$equation_dataset_path" + [code]="$code_dataset_path" + ) + + declare -A prompts=( + [layout]="Convert this page to docling." + [ocr]="Convert this page to docling." + [table]="Convert table to OTSL." + [equation]="Convert formula to LaTeX." + [code]="Convert code to text." + ) + + declare -A splits=( + [layout]="test" + [ocr]="test" + [table]="test" + [equation]="test" + [code]="test" + ) + + declare -A dataset_to_task + + for task in layout ocr table equation code; do + skip_var="skip_${task}" + if [ "${!skip_var}" = false ]; then + dataset="${data_paths[$task]}" + if [[ -v 'dataset_to_task["'"$dataset"'"]' ]]; then + # This dataset has already been assigned to another task + original_task="${dataset_to_task[$dataset]}" + echo "๐Ÿ” Detected identical dataset for $task and $original_task." + echo "๐Ÿ”— Creating symlink to reuse output from $original_task for $task." + mkdir -p "${output_dir}/model_output" + ln -sfn "${original_task}" "${output_dir}/model_output/${task}" + else + dataset_to_task[$dataset]="$task" + fi + fi + done + + for task in layout ocr table equation code; do + skip_var="skip_${task}" + if [ "${!skip_var}" = false ]; then + if [ ! -L "${output_dir}/model_output/${task}" ]; then + echo "๐Ÿš€ Performing inference for **${task^^}** on dataset ${data_paths[$task]}" + mkdir -p "${output_dir}/model_output/${task}" + uv run python3 docling_eval/end_to_end/batch_inference_vllm.py \ + --model_path "$model_path" \ + --dataset_path "${data_paths[$task]}" \ + --split "${splits[$task]}" \ + --output_dir "${output_dir}/model_output/${task}" \ + --num_workers "$num_workers" \ + --prompt "${prompts[$task]}" + else + echo "โญ๏ธ Skipping inference for ${task} (already linked to another task)." + fi + fi + done +fi + +# normalize equations +# if [ "$skip_equation" = false ]; then +# echo "๐Ÿงฎ Normalizing equations..." +# mkdir -p "${output_dir}/model_output/equation_normalized" +# uv run python3 docling_eval/end_to_end/normalize_equations.py \ +# --input_dir "${output_dir}/model_output/equation" \ +# --output_dir "${output_dir}/model_output/equation_normalized" \ +# --num_workers "$num_workers" +# printf '%s\n' "-----------------------------------------------" +# fi + +# --------------------------------------------------------- +# Evaluation & visualisation with `docling_eval` +# --------------------------------------------------------- +declare -A modality=( + [layout]="layout" + [ocr]="markdown_text" + [table]="table_structure" +) + +declare -A dataset_paths=( + [layout]="$layout_dataset_path" + [ocr]="$ocr_dataset_path" + [table]="$table_dataset_path" + [equation]="$equation_dataset_path" + [code]="$code_dataset_path" +) + +declare -A output_dirs=( + [layout]="${output_dir}/evals/layout/" + [ocr]="${output_dir}/evals/ocr/" + [table]="${output_dir}/evals/table/" + [equation]="${output_dir}/evals/equation/" + [code]="${output_dir}/evals/code/" +) + +declare -A splits=( + [layout]="test" + [ocr]="test" + [table]="test" + [equation]="test" + [code]="test" +) + +declare -A benchmarks=( + [layout]="DocLayNetV2" + [ocr]="DocLayNetV2" + [table]="FinTabNet" +) + +declare -A file_source_paths=( + [layout]="${output_dir}/model_output/layout" + [ocr]="${output_dir}/model_output/ocr" + [table]="${output_dir}/model_output/table" + [equation]="${output_dir}/model_output/equation" + [code]="${output_dir}/model_output/code" + +) +# evaluate tasks for which you can use docling_eval +for task in layout ocr table; do + skip_var="skip_${task}" + if [ "${!skip_var}" = false ]; then + echo "๐Ÿ“ Evaluating **${task}** (modality: ${modality[$task]}) โ€ฆ" + + echo " ๐Ÿ‘‰ Running create-eval โ€ฆ" + uv run docling-eval \ + create-eval \ + --benchmark ${benchmarks[$task]} \ + --gt-dir ${dataset_paths[$task]}\ + --prediction-provider File \ + --file-prediction-format doctags \ + --file-source-path ${file_source_paths[$task]} \ + --output-dir "${output_dirs[$task]}" + + echo " ๐Ÿ‘‰ Running evaluate โ€ฆ" + uv run docling-eval evaluate \ + --modality "${modality[$task]}" \ + --benchmark "${benchmarks[$task]}" \ + --output-dir "${output_dirs[$task]}" \ + --split "${splits[$task]}" + + echo " ๐Ÿ‘‰ Running visualize โ€ฆ" + uv run docling-eval visualize \ + --modality "${modality[$task]}" \ + --benchmark "${benchmarks[$task]}" \ + --output-dir "${output_dirs[$task]}" \ + --split "${splits[$task]}" + printf '%s\n' "-----------------------------------------------" + else + echo "โญ๏ธ Skipping evaluation for ${task} (skip_${task}=true)" + printf '%s\n' "-----------------------------------------------" + fi +done + + +# evaluate tasks for which you CAN'T use docling_eval +for task in code equation; do + skip_var="skip_${task}" + if [ "${!skip_var}" = false ]; then + echo "๐Ÿ“ Evaluating **${task}** โ€ฆ" + + uv run python3 docling_eval/end_to_end/evaluate_code_equations.py \ + --file_source_path ${file_source_paths[$task]} \ + --gt_dir ${dataset_paths[$task]} \ + --output_dir "${output_dirs[$task]}" \ + --num_workers "$num_workers" \ + --split "${splits[$task]}" + else + echo "โญ๏ธ Skipping evaluation for ${task} (skip_${task}=true)" + fi +done From 84cd2e1c7865bbb6c81a33bebc8987d3638513e2 Mon Sep 17 00:00:00 2001 From: mao Date: Thu, 26 Jun 2025 18:39:47 +0200 Subject: [PATCH 15/21] it works --- .gitignore | 3 ++- docling_eval/end_to_end/evaluate_smoldocling_end_to_end.sh | 1 + 2 files changed, 3 insertions(+), 1 deletion(-) diff --git a/.gitignore b/.gitignore index 5a82d274..df9a22e1 100644 --- a/.gitignore +++ b/.gitignore @@ -443,4 +443,5 @@ pip-selfcheck.json # docs/**/*.png # docs/**/*.svg checkpoints/* -outputs_evals/* \ No newline at end of file +outputs_evals/* +output_eval/* diff --git a/docling_eval/end_to_end/evaluate_smoldocling_end_to_end.sh b/docling_eval/end_to_end/evaluate_smoldocling_end_to_end.sh index 30682880..1c536a69 100755 --- a/docling_eval/end_to_end/evaluate_smoldocling_end_to_end.sh +++ b/docling_eval/end_to_end/evaluate_smoldocling_end_to_end.sh @@ -297,3 +297,4 @@ done # bsub -q normal -n 1 -R "span[hosts=1]" -M 200G -gpu "num=1:mode=exclusive_process" -oo ~/.lsbatch/evaluate.stdout -eo ~/.lsbatch/evaluate.stderr docling_eval/end_to_end/evaluate_smoldocling_end_to_end.sh --layout_dataset_path /gpfs/ZuFS1/proj/deep-search/datasets/doclaynet-v2-docling-GT/gt_dataset --ocr_dataset_path /gpfs/ZuFS1/proj/deep-search/datasets/doclaynet-v2-docling-GT/gt_dataset --code_dataset_path /gpfs/ZuFS1/proj/deep-search/mao/datasets/synth_code_net_test_set_5k --equation_dataset_path /gpfs/ZuFS1/proj/deep-search/mao/datasets/im2latex230k_ood_test_set/test_hf_format --table_dataset_path /gpfs/ZuFS1/proj/deep-search/mao/datasets/FinTabNet_OTSL_v1.2_doclingdocuments/gt_dataset +# bsub -q normal -n 1 -R "span[hosts=1]" -M 200G -gpu "num=1:mode=exclusive_process" -oo ~/.lsbatch/evaluate.stdout -eo ~/.lsbatch/evaluate.stderr docling_eval/end_to_end/evaluate_smoldocling_end_to_end.sh --model_path /gpfs/ZuFS1/proj/deep-search/mao/repos/docling-eval/checkpoints/19062025_last_model --layout_dataset_path /gpfs/ZuFS1/proj/deep-search/datasets/doclaynet-v2-docling-GT/gt_dataset --ocr_dataset_path /gpfs/ZuFS1/proj/deep-search/datasets/doclaynet-v2-docling-GT/gt_dataset --code_dataset_path /gpfs/ZuFS1/proj/deep-search/mao/datasets/synth_code_net_test_set_5k --equation_dataset_path /gpfs/ZuFS1/proj/deep-search/mao/datasets/im2latex230k_ood_test_set/test_hf_format --table_dataset_path /gpfs/ZuFS1/proj/deep-search/mao/datasets/FinTabNet_OTSL_v1.2_doclingdocuments/gt_dataset From 87819c185682d081d93787855973490e40789072 Mon Sep 17 00:00:00 2001 From: mao Date: Tue, 1 Jul 2025 10:03:12 +0200 Subject: [PATCH 16/21] wip --- docling_eval/end_to_end/evaluate_smoldocling_end_to_end.sh | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/docling_eval/end_to_end/evaluate_smoldocling_end_to_end.sh b/docling_eval/end_to_end/evaluate_smoldocling_end_to_end.sh index 1c536a69..091e3079 100755 --- a/docling_eval/end_to_end/evaluate_smoldocling_end_to_end.sh +++ b/docling_eval/end_to_end/evaluate_smoldocling_end_to_end.sh @@ -14,8 +14,13 @@ table_dataset_path="" equation_dataset_path="" code_dataset_path="" +<<<<<<< HEAD output_dir="./outputs_evals/output_eval_rl_new_code" num_workers=64 +======= +output_dir="./output_eval_no_rl" +num_workers=16 +>>>>>>> 5d66e06 (wip) # ------------------------------ # Parse args From c475d10cae5ffeac77f35fc19a55ec665463e949 Mon Sep 17 00:00:00 2001 From: mao Date: Wed, 9 Jul 2025 11:21:10 +0200 Subject: [PATCH 17/21] vela wip --- .../end_to_end/evaluate_smoldocling_end_to_end.sh | 5 ----- .../evaluate_smoldocling_end_to_end_vela.sh | 12 ++++++------ 2 files changed, 6 insertions(+), 11 deletions(-) diff --git a/docling_eval/end_to_end/evaluate_smoldocling_end_to_end.sh b/docling_eval/end_to_end/evaluate_smoldocling_end_to_end.sh index 091e3079..1c536a69 100755 --- a/docling_eval/end_to_end/evaluate_smoldocling_end_to_end.sh +++ b/docling_eval/end_to_end/evaluate_smoldocling_end_to_end.sh @@ -14,13 +14,8 @@ table_dataset_path="" equation_dataset_path="" code_dataset_path="" -<<<<<<< HEAD output_dir="./outputs_evals/output_eval_rl_new_code" num_workers=64 -======= -output_dir="./output_eval_no_rl" -num_workers=16 ->>>>>>> 5d66e06 (wip) # ------------------------------ # Parse args diff --git a/docling_eval/end_to_end/evaluate_smoldocling_end_to_end_vela.sh b/docling_eval/end_to_end/evaluate_smoldocling_end_to_end_vela.sh index 05415fab..d886ffa4 100644 --- a/docling_eval/end_to_end/evaluate_smoldocling_end_to_end_vela.sh +++ b/docling_eval/end_to_end/evaluate_smoldocling_end_to_end_vela.sh @@ -167,7 +167,7 @@ if [ "$skip_inference" = false ]; then if [ ! -L "${output_dir}/model_output/${task}" ]; then echo "๐Ÿš€ Performing inference for **${task^^}** on dataset ${data_paths[$task]}" mkdir -p "${output_dir}/model_output/${task}" - uv run python3 docling_eval/end_to_end/batch_inference_vllm.py \ + python3 docling_eval/end_to_end/batch_inference_vllm.py \ --model_path "$model_path" \ --dataset_path "${data_paths[$task]}" \ --split "${splits[$task]}" \ @@ -185,7 +185,7 @@ fi # if [ "$skip_equation" = false ]; then # echo "๐Ÿงฎ Normalizing equations..." # mkdir -p "${output_dir}/model_output/equation_normalized" -# uv run python3 docling_eval/end_to_end/normalize_equations.py \ +# python3 docling_eval/end_to_end/normalize_equations.py \ # --input_dir "${output_dir}/model_output/equation" \ # --output_dir "${output_dir}/model_output/equation_normalized" \ # --num_workers "$num_workers" @@ -246,7 +246,7 @@ for task in layout ocr table; do echo "๐Ÿ“ Evaluating **${task}** (modality: ${modality[$task]}) โ€ฆ" echo " ๐Ÿ‘‰ Running create-eval โ€ฆ" - uv run docling-eval \ + python3 docling-eval \ create-eval \ --benchmark ${benchmarks[$task]} \ --gt-dir ${dataset_paths[$task]}\ @@ -256,14 +256,14 @@ for task in layout ocr table; do --output-dir "${output_dirs[$task]}" echo " ๐Ÿ‘‰ Running evaluate โ€ฆ" - uv run docling-eval evaluate \ + python3 docling-eval evaluate \ --modality "${modality[$task]}" \ --benchmark "${benchmarks[$task]}" \ --output-dir "${output_dirs[$task]}" \ --split "${splits[$task]}" echo " ๐Ÿ‘‰ Running visualize โ€ฆ" - uv run docling-eval visualize \ + python3 docling-eval visualize \ --modality "${modality[$task]}" \ --benchmark "${benchmarks[$task]}" \ --output-dir "${output_dirs[$task]}" \ @@ -282,7 +282,7 @@ for task in code equation; do if [ "${!skip_var}" = false ]; then echo "๐Ÿ“ Evaluating **${task}** โ€ฆ" - uv run python3 docling_eval/end_to_end/evaluate_code_equations.py \ + python3 docling_eval/end_to_end/evaluate_code_equations.py \ --file_source_path ${file_source_paths[$task]} \ --gt_dir ${dataset_paths[$task]} \ --output_dir "${output_dirs[$task]}" \ From 287adaa7ef83d50483e95fb624584ea9547d9cf3 Mon Sep 17 00:00:00 2001 From: mao Date: Thu, 10 Jul 2025 08:52:39 +0200 Subject: [PATCH 18/21] vela works --- .../evaluate_smoldocling_end_to_end.sh | 14 ++- .../evaluate_smoldocling_end_to_end_vela.sh | 21 +++- .../end_to_end/granitedoclingeval.yaml | 95 +++++++++++++++++++ 3 files changed, 124 insertions(+), 6 deletions(-) create mode 100644 docling_eval/end_to_end/granitedoclingeval.yaml diff --git a/docling_eval/end_to_end/evaluate_smoldocling_end_to_end.sh b/docling_eval/end_to_end/evaluate_smoldocling_end_to_end.sh index 1c536a69..ea545335 100755 --- a/docling_eval/end_to_end/evaluate_smoldocling_end_to_end.sh +++ b/docling_eval/end_to_end/evaluate_smoldocling_end_to_end.sh @@ -3,6 +3,9 @@ set -euo pipefail source .venv/bin/activate +timestamp=$(date +"%Y%m%d-%H%M%S") +uuid=$(uuidgen | tr '[:upper:]' '[:lower:]' | cut -c1-8) + # ------------------------------ # Default values # ------------------------------ @@ -14,7 +17,7 @@ table_dataset_path="" equation_dataset_path="" code_dataset_path="" -output_dir="./outputs_evals/output_eval_rl_new_code" +output_dir="" num_workers=64 # ------------------------------ @@ -50,6 +53,15 @@ printf '| ๐Ÿ‘‰ %-25s = %s \n' "output_dir" "$output_dir" printf '| ๐Ÿ‘‰ %-25s = %s \n' "num_workers" "$num_workers" printf '%s\n' "-----------------------------------------------" +if [ -z "$output_dir" ]; then + if [ -n "$model_path" ]; then + # Extract basename from model path, stripping trailing slash + model_name=$(basename "${model_path%/}") + output_dir="./outputs_evals/output_eval_${model_name}" + else + output_dir="./outputs_evals/output_eval_${timestamp}_${uuid}" + fi +fi skip_layout=false diff --git a/docling_eval/end_to_end/evaluate_smoldocling_end_to_end_vela.sh b/docling_eval/end_to_end/evaluate_smoldocling_end_to_end_vela.sh index d886ffa4..0fd907d3 100644 --- a/docling_eval/end_to_end/evaluate_smoldocling_end_to_end_vela.sh +++ b/docling_eval/end_to_end/evaluate_smoldocling_end_to_end_vela.sh @@ -1,5 +1,4 @@ #!/usr/bin/env bash -set -euo pipefail timestamp=$(date +"%Y%m%d-%H%M%S") uuid=$(uuidgen | tr '[:upper:]' '[:lower:]' | cut -c1-8) @@ -15,7 +14,7 @@ table_dataset_path="" equation_dataset_path="" code_dataset_path="" -output_dir="./outputs_evals/output_eval_${timestamp}_${uuid}" +output_dir="" num_workers=8 # ------------------------------ @@ -39,6 +38,18 @@ while [[ $# -gt 0 ]]; do esac done +# if the ouput_dir is not passed and the model_path is passed then +# the output_dir is set to the name of the model +if [ -z "$output_dir" ]; then + if [ -n "$model_path" ]; then + # Extract basename from model path, stripping trailing slash + model_name=$(basename "${model_path%/}") + output_dir="./outputs_evals/output_eval_${model_name}" + else + output_dir="./outputs_evals/output_eval_${timestamp}_${uuid}" + fi +fi + printf '%s\n' "-----------------------------------------------" printf '| %-51s \n' "โœ… Parsed args:" printf '| ๐Ÿ‘‰ %-25s = %s \n' "model_path" "$model_path" @@ -246,7 +257,7 @@ for task in layout ocr table; do echo "๐Ÿ“ Evaluating **${task}** (modality: ${modality[$task]}) โ€ฆ" echo " ๐Ÿ‘‰ Running create-eval โ€ฆ" - python3 docling-eval \ + docling-eval \ create-eval \ --benchmark ${benchmarks[$task]} \ --gt-dir ${dataset_paths[$task]}\ @@ -256,14 +267,14 @@ for task in layout ocr table; do --output-dir "${output_dirs[$task]}" echo " ๐Ÿ‘‰ Running evaluate โ€ฆ" - python3 docling-eval evaluate \ + docling-eval evaluate \ --modality "${modality[$task]}" \ --benchmark "${benchmarks[$task]}" \ --output-dir "${output_dirs[$task]}" \ --split "${splits[$task]}" echo " ๐Ÿ‘‰ Running visualize โ€ฆ" - python3 docling-eval visualize \ + docling-eval visualize \ --modality "${modality[$task]}" \ --benchmark "${benchmarks[$task]}" \ --output-dir "${output_dirs[$task]}" \ diff --git a/docling_eval/end_to_end/granitedoclingeval.yaml b/docling_eval/end_to_end/granitedoclingeval.yaml new file mode 100644 index 00000000..bcb06cc6 --- /dev/null +++ b/docling_eval/end_to_end/granitedoclingeval.yaml @@ -0,0 +1,95 @@ +jobName: granitedoclingeval-mao-n1c8g1m100 # name of the generated AppWrapper and PyTorchJob objects (required) +queueName: default-queue # local queue to submit to (default: default-queue) + +numPods: 1 # total pod count including master and worker pods (default: 1) +numCpusPerPod: 8 # requested number of cpus per pod (default: 1) +numGpusPerPod: 1 # requested number of gpus per pod (default: 0) +totalMemoryPerPod: 100Gi # requested amount of memory per pod (default: 1Gi) +roceGdrResName: nvidia.com/roce_gdr +serviceAccountName: "gdr" + +priority: default-priority # default-priority (default), low-priority, or high-priority + +# container image for the pods (required) +# containerImage: us.icr.io/cil15-shared-registry/docling_rl@sha256:0f8ce26d8f79fa44817524e17230c474dc9e84ca43f8f9134d736b59cb6f8d86 +# containerImage: us.icr.io/cil15-shared-registry/docling_rl@sha256:a0d700aaf653125717ee15b23e0aba378752411f81f86a54b449c01a4b56d64b +containerImage: us.icr.io/cil15-shared-registry/docling_rl@sha256:899d1eb8133d3a6c3a28510b85bb041f4f9f37f352e7b547b867ed3876ebbfba +# containerImage: us.icr.io/cil15-shared-registry/smoldocling@sha256:3a963f056dcb6298023e18b90d1c5f77b936f52ef4e2c0d7fb48181c3f1924ae + +hostIgnoreList: + - dmf-nnnqh-gdr-gpu-worker-3-dbt4z + - dmf-nnnqh-gpu-worker-3-tqtx7 + +# numRoceGdr: 2 # <-- comment out if num pods is 1 +topologyFileConfigMap: topo-gdr-2vf-canary +environmentVariables: + - name: NCCL_DEBUG + value: WARN + - name: NCCL_IB_HCA + value: mlx5_0,mlx5_3 + - name: NCCL_IB_QPS_PER_CONNECTION + value: "8" + - name: NCCL_IB_SPLIT_DATA_ON_QPS + value: "0" + - name: NCCL_IB_PCI_RELAXED_ORDERING + value: "1" + - name: NCCL_IB_GID_INDEX + value: "3" + # - name: NCCL_SOCKET_IFNAME + # value: net1-0,net1-1 + - name: NCCL_SOCKET_IFNAME + value: lo # or eth0, check via `ip a` + - name: NCCL_ALGO + value: Ring + - name: NCCL_IGNORE_CPU_AFFINITY + value: "1" + - name: NCCL_DEBUG_SUBSYS + value: INIT,GRAPH,ENV,TUNING + # - name: NCCL_IB_DISABLE + # value: "0" + - name: NCCL_IB_DISABLE + value: "1" # โ† disable Infiniband + - name: NCCL_SOCKET_NTHREADS + value: "2" + - name: NCCL_CROSS_NIC + value: "0" + - name: TOKENIZERS_PARALLELISM + value: "false" + - name: COS_BUCKET + value: "/data" + - name: TRITON_HOME + value: /data/cache/trit-3-605/ + - name: TRITON_DUMP_DIR + value: /data/cache/trit-3-605/ + - name: TRITON_CACHE_DIR + value: /data/cache/trit-3-605/ + - name: TRITON_OVERRIDE_DIR + value: /data/cache/trit-3-605/ + - name: TORCH_RUN_FLAGS + value: --nnodes 1 --nproc_per_node=1 + - name: NLTK_DATA + value: /data/mao/nltk_data/ + +# multiNicNetworkName: multi-nic-network # <-- comment out if num pods is 1 + +setupCommands: + - cd /data/mao/docling-eval/ + - pip install -e . + - pip install --upgrade docling docling-core + - pip install azure-ai-documentintelligence azure-common azure-core google-cloud-documentai boto3 + - bash docling_eval/end_to_end/evaluate_smoldocling_end_to_end_vela.sh --model_path ./checkpoints/19062025_last_model/ --layout_dataset_path /datagpfs/eval/layout/doclaynet-v2-docling/ --ocr_dataset_path /datagpfs/eval/layout/doclaynet-v2-docling/ --code_dataset_path /datagpfs/eval/code/synth_code_net_test_5k/ --equation_dataset_path /datagpfs/eval/equation/im2latex230k_ood_test_set/ --table_dataset_path /datagpfs/eval/table/FinTabNet_OTSL_v1.2_doclingdocuments/ + + +imagePullSecrets: + - name: all-icr-io + +volumes: + - name: ahn-home + claimName: ahn-home + mountPath: /data + - name: docling-datasets + claimName: docling-datasets + mountPath: /data1 + - name: doclingdata + claimName: doclingdata + mountPath: /datagpfs \ No newline at end of file From 9d3f87666b942777d4b761d2991704f6f0ec876f Mon Sep 17 00:00:00 2001 From: mao Date: Thu, 10 Jul 2025 10:32:21 +0200 Subject: [PATCH 19/21] removed unnecesary file --- .../end_to_end/granitedoclingeval.yaml | 95 ------------------- 1 file changed, 95 deletions(-) delete mode 100644 docling_eval/end_to_end/granitedoclingeval.yaml diff --git a/docling_eval/end_to_end/granitedoclingeval.yaml b/docling_eval/end_to_end/granitedoclingeval.yaml deleted file mode 100644 index bcb06cc6..00000000 --- a/docling_eval/end_to_end/granitedoclingeval.yaml +++ /dev/null @@ -1,95 +0,0 @@ -jobName: granitedoclingeval-mao-n1c8g1m100 # name of the generated AppWrapper and PyTorchJob objects (required) -queueName: default-queue # local queue to submit to (default: default-queue) - -numPods: 1 # total pod count including master and worker pods (default: 1) -numCpusPerPod: 8 # requested number of cpus per pod (default: 1) -numGpusPerPod: 1 # requested number of gpus per pod (default: 0) -totalMemoryPerPod: 100Gi # requested amount of memory per pod (default: 1Gi) -roceGdrResName: nvidia.com/roce_gdr -serviceAccountName: "gdr" - -priority: default-priority # default-priority (default), low-priority, or high-priority - -# container image for the pods (required) -# containerImage: us.icr.io/cil15-shared-registry/docling_rl@sha256:0f8ce26d8f79fa44817524e17230c474dc9e84ca43f8f9134d736b59cb6f8d86 -# containerImage: us.icr.io/cil15-shared-registry/docling_rl@sha256:a0d700aaf653125717ee15b23e0aba378752411f81f86a54b449c01a4b56d64b -containerImage: us.icr.io/cil15-shared-registry/docling_rl@sha256:899d1eb8133d3a6c3a28510b85bb041f4f9f37f352e7b547b867ed3876ebbfba -# containerImage: us.icr.io/cil15-shared-registry/smoldocling@sha256:3a963f056dcb6298023e18b90d1c5f77b936f52ef4e2c0d7fb48181c3f1924ae - -hostIgnoreList: - - dmf-nnnqh-gdr-gpu-worker-3-dbt4z - - dmf-nnnqh-gpu-worker-3-tqtx7 - -# numRoceGdr: 2 # <-- comment out if num pods is 1 -topologyFileConfigMap: topo-gdr-2vf-canary -environmentVariables: - - name: NCCL_DEBUG - value: WARN - - name: NCCL_IB_HCA - value: mlx5_0,mlx5_3 - - name: NCCL_IB_QPS_PER_CONNECTION - value: "8" - - name: NCCL_IB_SPLIT_DATA_ON_QPS - value: "0" - - name: NCCL_IB_PCI_RELAXED_ORDERING - value: "1" - - name: NCCL_IB_GID_INDEX - value: "3" - # - name: NCCL_SOCKET_IFNAME - # value: net1-0,net1-1 - - name: NCCL_SOCKET_IFNAME - value: lo # or eth0, check via `ip a` - - name: NCCL_ALGO - value: Ring - - name: NCCL_IGNORE_CPU_AFFINITY - value: "1" - - name: NCCL_DEBUG_SUBSYS - value: INIT,GRAPH,ENV,TUNING - # - name: NCCL_IB_DISABLE - # value: "0" - - name: NCCL_IB_DISABLE - value: "1" # โ† disable Infiniband - - name: NCCL_SOCKET_NTHREADS - value: "2" - - name: NCCL_CROSS_NIC - value: "0" - - name: TOKENIZERS_PARALLELISM - value: "false" - - name: COS_BUCKET - value: "/data" - - name: TRITON_HOME - value: /data/cache/trit-3-605/ - - name: TRITON_DUMP_DIR - value: /data/cache/trit-3-605/ - - name: TRITON_CACHE_DIR - value: /data/cache/trit-3-605/ - - name: TRITON_OVERRIDE_DIR - value: /data/cache/trit-3-605/ - - name: TORCH_RUN_FLAGS - value: --nnodes 1 --nproc_per_node=1 - - name: NLTK_DATA - value: /data/mao/nltk_data/ - -# multiNicNetworkName: multi-nic-network # <-- comment out if num pods is 1 - -setupCommands: - - cd /data/mao/docling-eval/ - - pip install -e . - - pip install --upgrade docling docling-core - - pip install azure-ai-documentintelligence azure-common azure-core google-cloud-documentai boto3 - - bash docling_eval/end_to_end/evaluate_smoldocling_end_to_end_vela.sh --model_path ./checkpoints/19062025_last_model/ --layout_dataset_path /datagpfs/eval/layout/doclaynet-v2-docling/ --ocr_dataset_path /datagpfs/eval/layout/doclaynet-v2-docling/ --code_dataset_path /datagpfs/eval/code/synth_code_net_test_5k/ --equation_dataset_path /datagpfs/eval/equation/im2latex230k_ood_test_set/ --table_dataset_path /datagpfs/eval/table/FinTabNet_OTSL_v1.2_doclingdocuments/ - - -imagePullSecrets: - - name: all-icr-io - -volumes: - - name: ahn-home - claimName: ahn-home - mountPath: /data - - name: docling-datasets - claimName: docling-datasets - mountPath: /data1 - - name: doclingdata - claimName: doclingdata - mountPath: /datagpfs \ No newline at end of file From 7f6aaba3f28b6e6b76ac95138f8f34ce657e4bac Mon Sep 17 00:00:00 2001 From: mao Date: Thu, 10 Jul 2025 10:35:12 +0200 Subject: [PATCH 20/21] small fix --- docling_eval/end_to_end/evaluate_smoldocling_end_to_end.sh | 2 +- docling_eval/end_to_end/evaluate_smoldocling_end_to_end_vela.sh | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/docling_eval/end_to_end/evaluate_smoldocling_end_to_end.sh b/docling_eval/end_to_end/evaluate_smoldocling_end_to_end.sh index ea545335..88255e94 100755 --- a/docling_eval/end_to_end/evaluate_smoldocling_end_to_end.sh +++ b/docling_eval/end_to_end/evaluate_smoldocling_end_to_end.sh @@ -57,7 +57,7 @@ if [ -z "$output_dir" ]; then if [ -n "$model_path" ]; then # Extract basename from model path, stripping trailing slash model_name=$(basename "${model_path%/}") - output_dir="./outputs_evals/output_eval_${model_name}" + output_dir="./outputs_evals/output_eval_${model_name}_${timestamp}_${uuid}" else output_dir="./outputs_evals/output_eval_${timestamp}_${uuid}" fi diff --git a/docling_eval/end_to_end/evaluate_smoldocling_end_to_end_vela.sh b/docling_eval/end_to_end/evaluate_smoldocling_end_to_end_vela.sh index 0fd907d3..330b1668 100644 --- a/docling_eval/end_to_end/evaluate_smoldocling_end_to_end_vela.sh +++ b/docling_eval/end_to_end/evaluate_smoldocling_end_to_end_vela.sh @@ -44,7 +44,7 @@ if [ -z "$output_dir" ]; then if [ -n "$model_path" ]; then # Extract basename from model path, stripping trailing slash model_name=$(basename "${model_path%/}") - output_dir="./outputs_evals/output_eval_${model_name}" + output_dir="./outputs_evals/output_eval_${model_name}_${timestamp}_${uuid}" else output_dir="./outputs_evals/output_eval_${timestamp}_${uuid}" fi From e0dd6b31d5f12166016647525946f9d7ff0e7a91 Mon Sep 17 00:00:00 2001 From: mao Date: Mon, 14 Jul 2025 17:21:03 +0200 Subject: [PATCH 21/21] fix inference vllm --- docling_eval/end_to_end/batch_inference_vllm.py | 1 + docling_eval/end_to_end/evaluate_smoldocling_end_to_end.sh | 7 +------ 2 files changed, 2 insertions(+), 6 deletions(-) diff --git a/docling_eval/end_to_end/batch_inference_vllm.py b/docling_eval/end_to_end/batch_inference_vllm.py index 2d9b01c6..28a7bbec 100644 --- a/docling_eval/end_to_end/batch_inference_vllm.py +++ b/docling_eval/end_to_end/batch_inference_vllm.py @@ -103,6 +103,7 @@ def main(): temperature=0.0, max_tokens=8192, skip_special_tokens=False, + stop_token_ids=[100338], # change ) messages = [ diff --git a/docling_eval/end_to_end/evaluate_smoldocling_end_to_end.sh b/docling_eval/end_to_end/evaluate_smoldocling_end_to_end.sh index 88255e94..ddbd92ae 100755 --- a/docling_eval/end_to_end/evaluate_smoldocling_end_to_end.sh +++ b/docling_eval/end_to_end/evaluate_smoldocling_end_to_end.sh @@ -304,9 +304,4 @@ for task in code equation; do fi done -# bsub -q normal -n 1 -R "span[hosts=1]" -M 200G -gpu "num=1:mode=exclusive_process" -oo ~/.lsbatch/evaluate_gd.stdout -eo ~/.lsbatch/evaluate_gd.stderr docling_eval/end_to_end/evaluate_smoldocling_end_to_end.sh --model_path /gpfs/ZuFS1/proj/deep-search/mao/repos/docling-eval/checkpoints/granitedocling_v06.5_stg_4_RETRAINED_checkpoint-18101 --layout_dataset_path /gpfs/ZuFS1/proj/deep-search/datasets/doclaynet-v2-docling-GT/gt_dataset --ocr_dataset_path /gpfs/ZuFS1/proj/deep-search/datasets/doclaynet-v2-docling-GT/gt_dataset --code_dataset_path /gpfs/ZuFS1/proj/deep-search/mao/datasets/synth_code_net_test_set_5k --equation_dataset_path /gpfs/ZuFS1/proj/deep-search/mao/datasets/im2latex230k_ood_test_set/test_hf_format --table_dataset_path /gpfs/ZuFS1/proj/deep-search/mao/datasets/FinTabNet_OTSL_v1.2_doclingdocuments/gt_dataset --output_dir /gpfs/ZuFS1/proj/deep-search/mao/repos/docling-eval/outputs_evals/output_eval_granite_docling - - -# bsub -q normal -n 1 -R "span[hosts=1]" -M 200G -gpu "num=1:mode=exclusive_process" -oo ~/.lsbatch/evaluate.stdout -eo ~/.lsbatch/evaluate.stderr docling_eval/end_to_end/evaluate_smoldocling_end_to_end.sh --layout_dataset_path /gpfs/ZuFS1/proj/deep-search/datasets/doclaynet-v2-docling-GT/gt_dataset --ocr_dataset_path /gpfs/ZuFS1/proj/deep-search/datasets/doclaynet-v2-docling-GT/gt_dataset --code_dataset_path /gpfs/ZuFS1/proj/deep-search/mao/datasets/synth_code_net_test_set_5k --equation_dataset_path /gpfs/ZuFS1/proj/deep-search/mao/datasets/im2latex230k_ood_test_set/test_hf_format --table_dataset_path /gpfs/ZuFS1/proj/deep-search/mao/datasets/FinTabNet_OTSL_v1.2_doclingdocuments/gt_dataset - -# bsub -q normal -n 1 -R "span[hosts=1]" -M 200G -gpu "num=1:mode=exclusive_process" -oo ~/.lsbatch/evaluate.stdout -eo ~/.lsbatch/evaluate.stderr docling_eval/end_to_end/evaluate_smoldocling_end_to_end.sh --model_path /gpfs/ZuFS1/proj/deep-search/mao/repos/docling-eval/checkpoints/19062025_last_model --layout_dataset_path /gpfs/ZuFS1/proj/deep-search/datasets/doclaynet-v2-docling-GT/gt_dataset --ocr_dataset_path /gpfs/ZuFS1/proj/deep-search/datasets/doclaynet-v2-docling-GT/gt_dataset --code_dataset_path /gpfs/ZuFS1/proj/deep-search/mao/datasets/synth_code_net_test_set_5k --equation_dataset_path /gpfs/ZuFS1/proj/deep-search/mao/datasets/im2latex230k_ood_test_set/test_hf_format --table_dataset_path /gpfs/ZuFS1/proj/deep-search/mao/datasets/FinTabNet_OTSL_v1.2_doclingdocuments/gt_dataset +# bsub -q normal -n 1 -R "span[hosts=1]" -M 200G -gpu "num=1:mode=exclusive_process" -oo ~/.lsbatch/evaluate_2.stdout -eo ~/.lsbatch/evaluate_2.stderr docling_eval/end_to_end/evaluate_smoldocling_end_to_end.sh --model_path /gpfs/ZuFS1/proj/deep-search/mao/repos/docling-eval/checkpoints/granitedocling_v06.5r_stg_4_comon_debug_1_checkpoint-4808 --layout_dataset_path /gpfs/ZuFS1/proj/deep-search/datasets/doclaynet-v2-docling-GT/gt_dataset --ocr_dataset_path /gpfs/ZuFS1/proj/deep-search/datasets/doclaynet-v2-docling-GT/gt_dataset --code_dataset_path /gpfs/ZuFS1/proj/deep-search/mao/datasets/synth_code_net_test_set_5k --equation_dataset_path /gpfs/ZuFS1/proj/deep-search/mao/datasets/im2latex230k_ood_test_set/test_hf_format --table_dataset_path /gpfs/ZuFS1/proj/deep-search/mao/datasets/FinTabNet_OTSL_v1.2_doclingdocuments/gt_dataset