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@davidberenstein1957 davidberenstein1957 commented Jan 31, 2026

Closes #516

Summary

  • Add DPG benchmark for evaluating image understanding
  • Support category filtering: entity, attribute, relation, global, other
  • Fetch data from GitHub CSV (TencentQQGYLab/ELLA repo)
  • Auxiliaries include questions and category_broad fields

Usage

from pruna.data import PrunaDataModule

# Load all categories
dm = PrunaDataModule.from_string("DPG")

# Load specific category
dm = PrunaDataModule.from_string("DPG", category="entity")

Test plan

  • PrunaDataModule.from_string("DPG") works
  • Category filter works for all 5 categories
  • Auxiliaries include questions, category_broad fields
  • Docstring tests pass

davidberenstein1957 and others added 8 commits January 22, 2026 10:58
…mpts benchmark

- Introduced `from_benchmark` method in `PrunaDataModule` to create instances from benchmark classes.
- Added `Benchmark`, `BenchmarkEntry`, and `BenchmarkRegistry` classes for managing benchmarks.
- Implemented `PartiPrompts` benchmark for text-to-image generation with various categories and challenges.
- Created utility function `benchmark_to_datasets` to convert benchmarks into datasets compatible with `PrunaDataModule`.
- Added integration tests for benchmark functionality and data module interactions.
…filtering

- Remove heavy benchmark abstraction (Benchmark class, registry, adapter, 24 subclasses)
- Extend setup_parti_prompts_dataset with category and num_samples params
- Add BenchmarkInfo dataclass for metadata (metrics, description, subsets)
- Switch PartiPrompts to prompt_with_auxiliaries_collate to preserve Category/Challenge
- Merge tests into test_datamodule.py

Reduces 964 lines to 128 lines (87% reduction)

Co-authored-by: Cursor <cursoragent@cursor.com>
Document all dataclass fields per Numpydoc PR01 with summary on new line per GL01.

Co-authored-by: Cursor <cursoragent@cursor.com>
- Add list_benchmarks() to filter benchmarks by task type
- Add get_benchmark_info() to retrieve benchmark metadata
- Add COCO, ImageNet, WikiText to benchmark_info registry

Co-authored-by: Cursor <cursoragent@cursor.com>
Update benchmark metrics to match registered names:
- clip -> clip_score
- clip_iqa -> clipiqa
- Remove unimplemented top5_accuracy

Co-authored-by: Cursor <cursoragent@cursor.com>
- Add setup_oneig_text_rendering_dataset in datasets/prompt.py
- Register OneIGTextRendering in base_datasets
- Add BenchmarkInfo entry with clip_score, clipiqa metrics
- Auxiliaries include text_content for OCR evaluation
- Add test for loading and auxiliaries

Co-authored-by: Cursor <cursoragent@cursor.com>
- Add setup_oneig_alignment_dataset in datasets/prompt.py
- Support category filter (Anime_Stylization, Portrait, General_Object)
- Register OneIGAlignment in base_datasets
- Add BenchmarkInfo entry with accuracy metric, task_type text_generation
- Auxiliaries include questions, dependencies, category
- Add test for loading with category filter

Co-authored-by: Cursor <cursoragent@cursor.com>
- Add setup_dpg_dataset in datasets/prompt.py
- Support category filter (entity, attribute, relation, global, other)
- Register DPG in base_datasets
- Add BenchmarkInfo entry with accuracy metric, task_type text_generation
- Auxiliaries include questions, category_broad
- Add test for loading with category filter

Co-authored-by: Cursor <cursoragent@cursor.com>
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Cursor Bugbot has reviewed your changes and found 3 potential issues.

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- Fix task_type from text_generation to text_to_image for DPG, OneIGAlignment, and OneIGTextRendering
- Remove unused imports in test file

Co-authored-by: Cursor <cursoragent@cursor.com>
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This PR has been inactive for 10 days and is now marked as stale.

@github-actions github-actions bot added the stale label Feb 11, 2026
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[BENCHMARK] Add DPG (Descriptive Prompt Generation) benchmarks

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