update
Browse files- .gitattributes +1 -0
- README.md +38 -122
- annotation.xlsx +0 -3
- annotation_ch.xlsx +0 -3
- extract.py +105 -0
.gitattributes
CHANGED
@@ -60,3 +60,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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*.xlsx filter=lfs diff=lfs merge=lfs -text
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annotation.xlsx filter=lfs diff=lfs merge=lfs -text
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annotation_ch.xlsx filter=lfs diff=lfs merge=lfs -text
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*.xlsx filter=lfs diff=lfs merge=lfs -text
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annotation.xlsx filter=lfs diff=lfs merge=lfs -text
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annotation_ch.xlsx filter=lfs diff=lfs merge=lfs -text
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annotation.jsonl filter=lfs diff=lfs merge=lfs -text
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README.md
CHANGED
@@ -62,129 +62,38 @@ This dataset contains aesthetic annotations for images. The annotations cover 18
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## Annotation Details
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For more detailed annotation guidelines, please refer to:
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- annotation_ch.xlsx(Chinese)
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- annotation.xlsx(English)
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<!-- - [English Documentation (Google Docs)](your_google_docs_link_here) -->
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Each image in the dataset is annotated with the following attributes:
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- 2: Very clear
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- 1: Clear
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- 0: Normal
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- -1: Blurry
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- -2: Completely blurry
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### 7. Brightness (color)
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- 1: Bright
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- 0: Normal
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- -1: Dark
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### 8. Color Aesthetics (color_aes)
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- 1: Beautiful colors
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- 0: Normal colors
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- -1: Ugly colors
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### 9. Environmental Light and Shadow Prominence (shadow_degree)
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- 2: Very prominent
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- 1: Prominent
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- 0: Normal
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- -1: No light and shadow
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### 10. Light and Shadow Aesthetics (shadow_aes)
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- 2: Very beautiful
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- 1: Beautiful
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- 0: Normal
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- -1: No light and shadow
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### 11. Emotional Response (emotion)
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- 2: Very positive
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- 1: Positive
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- 0: Normal
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- -1: Negative
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- -2: Very negative
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### 12. Detail Refinement (detail_fineness)
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- 2: Very refined
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- 1: Refined
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- 0: Normal
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- -1: Rough
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- -2: Very rough
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- -3: Hard to recognize
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- -4: Fragmented
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### 13. Detail Authenticity (detail_facticity)
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- 1: Authentic
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- 0: Neutral
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- -1: Inauthentic
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- -2: Very inauthentic
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- -3: Severely inauthentic
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### 14. Human Body Accuracy (body_correctness)
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- 1: No errors
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- 0: Neutral
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- -1: Has errors
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- -2: Has obvious errors
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- -3: Has severe errors
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- -4: No human body
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### 15. Face Quality (face)
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- 2: Very beautiful
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- 1: Beautiful
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- 0: Normal
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- -1: Has errors
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- -2: Has severe errors
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- -3: No face
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### 16. Hand Quality (hand)
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- 1: Perfect
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- 0: Basically correct
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- -1: Minor errors
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- -2: Obvious errors
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- -3: Severe errors
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- -4: No hands
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### 17. Safety Rating (safe)
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- 1: Safe
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- 0: Neutral
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- -1: Potentially harmful
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- -2: Harmful
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- -3: Very harmful
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### 18. Harm Type (harm)
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- 3: Adult content
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- 2: Horror
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- 1: Other
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- 0: Harmless
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## Additional Feature Details
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- Score -1 (Monotonous) corresponds to [0,0,0,1]
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- Score -2 (Empty) corresponds to [0,0,0,0]
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Each element in the binary array represents a yes/no answer to a specific aspect of the assessment. For detailed questions corresponding to these binary judgments, please refer to the meta_qa_en.txt file.
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### Meta Mask
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The `meta_mask` feature is used for balanced sampling during model training:
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- Elements with value 1 indicate that the corresponding binary judgment was used in training
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- Elements with value 0 indicate that the corresponding binary judgment was ignored during training
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## Annotation Details
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Each image in the dataset is annotated with the following attributes:
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1. **Overall Symmetry (adjective)**
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2. **Object Composition (collocation)**
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3. **Main Object Position (place)**
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4. **Scene Richness (richness)**
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5. **Background Quality (background)**
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6. **Overall Clarity (sharpness)**
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7. **Brightness (color)**
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8. **Color Aesthetics (color_aes)**
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9. **Environmental Light and Shadow Prominence (shadow_degree)**
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10. **Light and Shadow Aesthetics (shadow_aes)**
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11. **Emotional Response (emotion)**
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12. **Detail Refinement (detail_fineness)**
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13. **Detail Authenticity (detail_facticity)**
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14. **Human Body Accuracy (body_correctness)**
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15. **Face Quality (face)**
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16. **Hand Quality (hand)**
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17. **Safety Rating (safe)**
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18. **Harm Type (harm)**
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### Example: Scene Richness (richness)
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- **2:** Very rich
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- **1:** Rich
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- **0:** Normal
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- **-1:** Monotonous
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- **-2:** Empty
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For more detailed annotation guidelines, please refer to:
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- [annotation_deatils](https://www.notion.so/VisionReward-Image-Annotation-Details-196a0162280e80ef8359c38e9e41247e?pvs=4)
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- [annotation_deatils_ch](https://www.notion.so/VisionReward-Image-195a0162280e8044bcb4ec48d000409c?pvs=4)
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## Additional Feature Details
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- Score -1 (Monotonous) corresponds to [0,0,0,1]
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- Score -2 (Empty) corresponds to [0,0,0,0]
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Each element in the binary array represents a yes/no answer to a specific aspect of the assessment. For detailed questions corresponding to these binary judgments, please refer to the `meta_qa_en.txt` file.
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### Meta Mask
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The `meta_mask` feature is used for balanced sampling during model training:
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- Elements with value 1 indicate that the corresponding binary judgment was used in training
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- Elements with value 0 indicate that the corresponding binary judgment was ignored during training
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## Data Processing
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We provide `extract.py` for processing the dataset into JSONL format. The script can optionally extract the balanced positive/negative QA pairs used in VisionReward training by processing `meta_result` and `meta_mask` fields.
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```bash
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python extract.py [--save_imgs] [--process_qa]
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annotation.xlsx
DELETED
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version https://git-lfs.github.com/spec/v1
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oid sha256:070e5b9ebcafb1f69c1304fc113892b6a9013f283f899045f35f0c8790baed84
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size 28783535
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annotation_ch.xlsx
DELETED
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version https://git-lfs.github.com/spec/v1
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oid sha256:2b33bd01fcfbb980e687a8204b360ba50dd5160b57cdfb5f80764ebaf3a03e9a
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size 28783047
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extract.py
ADDED
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import json
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import os
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import logging
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import argparse
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from PIL import Image
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from datasets import Dataset
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import io
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# Configure logging for detailed output
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logging.basicConfig(level=logging.INFO)
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logger = logging.getLogger(__name__)
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def load_questions_from_meta_qa(meta_qa_file):
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with open(meta_qa_file, "r") as f:
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questions = [line.strip() for line in f if line.strip()]
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return questions
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def process_parquet_files(data_dir, output_jsonl, meta_qa_file=None, output_imgs=None, process_qa=False):
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"""
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Process Parquet files to generate a JSONL file with optional image export and QA list creation.
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Args:
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data_dir (str): Directory containing Parquet files.
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output_jsonl (str): Output JSONL file path.
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meta_qa_file (str, optional): Path to the meta_qa_en.txt file for QA list creation.
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output_imgs (str, optional): Directory path to save images. If None, images are not saved.
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process_qa (bool): Whether to process and include QA pairs in the output.
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Returns:
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None
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"""
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if output_imgs and not os.path.exists(output_imgs):
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os.makedirs(output_imgs)
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# Load questions only if QA processing is enabled
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questions = None
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if process_qa and meta_qa_file:
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questions = load_questions_from_meta_qa(meta_qa_file)
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jsonl_data = []
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parquet_files = [os.path.join(data_dir, f) for f in os.listdir(data_dir) if f.endswith(".parquet")]
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for parquet_file in parquet_files:
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dataset = Dataset.from_parquet(parquet_file)
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for row in dataset:
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json_item = {
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"internal_id": row["internal_id"],
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"url": row["url"],
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"annotation": row["annotation"],
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"meta_result": row["meta_result"],
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"meta_mask": row["meta_mask"],
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}
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# Optionally save images
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if output_imgs:
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img_data = row["image"]
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img_path = os.path.join(output_imgs, f"{row['internal_id']}.jpg")
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try:
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with open(img_path, "wb") as img_file:
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img_file.write(img_data)
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json_item["image_path"] = img_path
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except Exception as e:
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logger.error(f"Error saving image for internal_id {row['internal_id']}: {e}")
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# Optionally process QA pairs
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if process_qa and questions:
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qa_list = []
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meta_result = row["meta_result"]
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meta_mask = row["meta_mask"]
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for idx, mask in enumerate(meta_mask):
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if mask == 1: # Process questions only if the mask is 1
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question = questions[idx]
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answer = 'yes' if meta_result[idx] == 1 else 'no'
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qa_list.append({"question": question, "answer": answer})
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json_item["qa_list"] = qa_list
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jsonl_data.append(json_item)
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with open(output_jsonl, "w") as outfile:
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for json_item in jsonl_data:
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outfile.write(json.dumps(json_item) + "\n")
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logger.info(f"Finished writing JSONL file with {len(jsonl_data)} items.")
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if __name__ == "__main__":
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parser = argparse.ArgumentParser(description="Convert VisionReward Parquet dataset files to JSONL format with optional image extraction and QA list generation.")
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parser.add_argument("--data_dir", type=str, default='data', help="Directory containing Parquet files.")
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parser.add_argument("--output_jsonl", type=str, default='annotation.jsonl', help="Path to the output JSONL file.")
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parser.add_argument("--meta_qa_file", type=str, default="meta_qa_en.txt", help="Optional: Path to the meta_qa_en.txt file for QA list generation.")
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parser.add_argument("--save_imgs", action="store_true", help="Optional: Whether to save images.")
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parser.add_argument("--process_qa", action="store_true", help="Optional: Process and include QA pairs in the output.")
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args = parser.parse_args()
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output_imgs = 'imgs' if args.save_imgs else None
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process_parquet_files(
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data_dir=args.data_dir,
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output_jsonl=args.output_jsonl,
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meta_qa_file=args.meta_qa_file,
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output_imgs=args.output_imgs,
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process_qa=args.process_qa
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)
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