dd6c38cc7b
When multiple image columns are found, probes them (HEAD for URLs, os.path.exists for paths) and picks the first that works. Skips probing when top candidate is PIL/dict (score >= 75).
799 lines
28 KiB
Python
799 lines
28 KiB
Python
# SPDX-License-Identifier: AGPL-3.0-only - See /studio/LICENSE.AGPL-3.0
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# Copyright © 2025 Unsloth AI
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"""
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Format detection utilities for dataset processing.
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This module contains functions for detecting dataset formats (Alpaca, ShareGPT, ChatML),
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detecting multimodal/VLM dataset structures, and heuristic-based column mapping.
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"""
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import re
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def _keyword_in_column(keyword: str, col_name: str) -> bool:
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"""Word-boundary keyword match to avoid false positives like 'pic' in 'topic'."""
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return re.search(r'\b' + re.escape(keyword) + r'\b', col_name, re.IGNORECASE) is not None
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def detect_dataset_format(dataset):
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"""
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Detects dataset format by inspecting structure.
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Returns:
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dict: {
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"format": "alpaca" | "sharegpt" | "chatml" | "unknown",
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"chat_column": "messages" | "conversations" | None,
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"needs_standardization": bool,
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"sample_keys": list of keys found in messages (for debugging)
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}
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"""
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column_names = set(next(iter(dataset)).keys())
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# Check for Alpaca
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alpaca_columns = {"instruction", "output"}
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if alpaca_columns.issubset(column_names):
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return {
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"format": "alpaca",
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"chat_column": None,
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"needs_standardization": False,
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"sample_keys": []
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}
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# Check for chat-based formats (messages or conversations)
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chat_column = None
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if "messages" in column_names:
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chat_column = "messages"
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elif "conversations" in column_names:
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chat_column = "conversations"
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elif "texts" in column_names:
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chat_column = "texts"
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if chat_column:
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# Inspect the structure to determine if ShareGPT or ChatML
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try:
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sample = next(iter(dataset))
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chat_data = sample[chat_column]
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if chat_data and len(chat_data) > 0:
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first_msg = chat_data[0]
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msg_keys = set(first_msg.keys())
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# ShareGPT uses "from" and "value"
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if "from" in msg_keys or "value" in msg_keys:
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return {
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"format": "sharegpt",
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"chat_column": chat_column,
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"needs_standardization": True,
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"sample_keys": list(msg_keys)
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}
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# ChatML uses "role" and "content"
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elif "role" in msg_keys and "content" in msg_keys:
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return {
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"format": "chatml",
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"chat_column": chat_column,
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"needs_standardization": False,
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"sample_keys": list(msg_keys)
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}
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# Unknown structure but has chat column
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else:
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return {
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"format": "unknown",
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"chat_column": chat_column,
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"needs_standardization": None,
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"sample_keys": list(msg_keys)
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}
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except Exception as e:
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return {
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"format": "unknown",
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"chat_column": chat_column,
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"needs_standardization": None,
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"sample_keys": [],
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"error": str(e)
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}
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# No recognized format
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return {
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"format": "unknown",
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"chat_column": None,
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"needs_standardization": None,
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"sample_keys": []
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}
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def detect_custom_format_heuristic(dataset):
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"""
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Smart detection with priority scoring.
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Strategy for ambiguous keywords like 'task':
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1. Detect assistant first (unambiguous)
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2. Detect user using high-priority keywords first
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3. Check REMAINING columns for system keywords (including 'task')
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4. Only if no system match, use 'task' as fallback user
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"""
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sample = next(iter(dataset))
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all_columns = list(sample.keys())
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mapping = {}
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# Keywords
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assistant_words = [
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'output', 'answer', 'response', 'assistant', 'completion',
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'expected', 'recommendation', 'reply', 'result', 'target',
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'solution', 'explanation', 'solve'
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]
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# Split into high/low priority
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user_words_high_priority = [
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'input', 'question', 'query', 'prompt', 'instruction',
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'request', 'snippet', 'user', 'text',
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'problem', 'exercise'
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]
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user_words_low_priority = ['task'] # Ambiguous - can be user OR system
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user_words = user_words_high_priority + user_words_low_priority
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system_words = [
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'system', 'context', 'description', 'persona', 'role',
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'template', 'task' # Also in system
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]
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# Metadata columns to ignore
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metadata_exact_match = {
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'id', 'idx', 'index', 'key', 'timestamp', 'date',
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'metadata', 'source', 'kind', 'type', 'category',
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'score', 'label', 'tag', 'inference_mode'
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}
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metadata_prefix_patterns = [
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'problem_type', 'problem_source',
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'generation_model', 'pass_rate',
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]
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priority_patterns = {
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'generated': 100,
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'gen_': 90,
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'model_': 80,
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'predicted': 70,
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'completion': 60,
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}
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def has_keyword(col_name, keywords):
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"""Check if any keyword appears in column name."""
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col_lower = col_name.lower()
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col_normalized = col_lower.replace('_', '').replace('-', '').replace(' ', '')
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for keyword in keywords:
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if keyword in col_lower or keyword in col_normalized:
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return True
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return False
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def is_metadata(col_name):
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"""Check if column is likely metadata."""
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col_lower = col_name.lower()
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if col_lower in metadata_exact_match:
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return True
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if col_lower in metadata_prefix_patterns:
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return True
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for pattern in metadata_prefix_patterns:
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if col_lower.startswith(pattern.split('_')[0] + '_') and col_lower != pattern:
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if '_' in col_lower:
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prefix = col_lower.split('_')[0]
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if prefix in ['generation', 'pass', 'inference']:
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return True
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if len(col_lower) <= 2 and not col_lower in ['qa', 'q', 'a']:
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return True
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return False
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def get_priority_score(col_name):
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"""Calculate priority score based on column name patterns."""
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col_lower = col_name.lower()
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score = 0
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for pattern, pattern_score in priority_patterns.items():
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if pattern in col_lower:
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score += pattern_score
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return score
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def get_content_length(col_name):
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"""Get average content length for this column."""
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try:
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if col_name in sample and sample[col_name]:
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content = str(sample[col_name])
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return len(content)
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return 0
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except:
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return 0
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def score_column(col_name, keywords, role_type, num_candidates):
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"""Score a column for how likely it is to be a particular role."""
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if not has_keyword(col_name, keywords):
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return 0
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score = 0
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score += 10
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# Penalize ambiguous keywords when scoring for user
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if role_type == 'user':
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col_lower = col_name.lower()
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# If column is ONLY "task" (or task_xxx), give it lower priority for user role
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if 'task' in col_lower and not any(kw in col_lower for kw in user_words_high_priority):
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score -= 15 # Significant penalty so other user columns win
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priority_bonus = get_priority_score(col_name)
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score += priority_bonus
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if role_type in ['assistant', 'user']:
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avg_length = get_content_length(col_name)
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if num_candidates > 1:
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if avg_length > 1000:
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score += 50
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elif avg_length > 200:
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score += 30
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elif avg_length > 50:
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score += 10
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elif avg_length < 50:
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score -= 20
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else:
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if avg_length > 1000:
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score += 50
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elif avg_length > 200:
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score += 30
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elif avg_length > 50:
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score += 10
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return score
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# Filter out metadata columns
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content_columns = [col for col in all_columns if not is_metadata(col)]
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# Count candidates first
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assistant_potential = [col for col in content_columns if has_keyword(col, assistant_words)]
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user_potential = [col for col in content_columns if has_keyword(col, user_words)]
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# STEP 1: Find best ASSISTANT column
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assistant_candidates = []
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for col in assistant_potential:
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score = score_column(col, assistant_words, 'assistant', len(assistant_potential))
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if score > 0:
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assistant_candidates.append((col, score))
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if assistant_candidates:
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assistant_candidates.sort(key=lambda x: x[1], reverse=True)
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assistant_col = assistant_candidates[0][0]
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mapping[assistant_col] = 'assistant'
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else:
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assistant_col = None
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# STEP 2: Find best USER column (with penalty for ambiguous keywords)
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user_candidates = []
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for col in user_potential:
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if col == assistant_col:
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continue
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score = score_column(col, user_words, 'user', len(user_potential))
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if score > 0:
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user_candidates.append((col, score))
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if user_candidates:
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user_candidates.sort(key=lambda x: x[1], reverse=True)
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user_col = user_candidates[0][0]
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mapping[user_col] = 'user'
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else:
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user_col = None
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# STEP 3: Check ALL remaining columns for SYSTEM matches (priority check)
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remaining_columns = [col for col in content_columns if col not in mapping]
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system_col = None
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for col in remaining_columns:
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if has_keyword(col, system_words):
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# Found a system match in remaining columns
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mapping[col] = 'system'
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system_col = col
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break
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# STEP 4: Handle any additional remaining columns
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if system_col:
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remaining_columns = [col for col in remaining_columns if col != system_col]
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if len(remaining_columns) >= 1:
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remaining_col = remaining_columns[0]
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# If no strong keyword match, decide based on what's missing
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if not has_keyword(remaining_col, user_words + assistant_words):
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mapping[remaining_col] = 'system'
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elif user_col is None:
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# No user column yet, assign this as user
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mapping[remaining_col] = 'user'
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else:
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# Already have user + assistant, treat as system context
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mapping[remaining_col] = 'system'
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# VALIDATION: Ensure we have at least user + assistant
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has_user = any(role == 'user' for role in mapping.values())
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has_assistant = any(role == 'assistant' for role in mapping.values())
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if not has_user and len(remaining_columns) > 0:
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for col in remaining_columns:
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if col not in mapping:
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mapping[col] = 'user'
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has_user = True
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break
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if has_user and has_assistant:
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return mapping
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return None
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def detect_multimodal_dataset(dataset):
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"""
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Detects if dataset contains multimodal data (images and/or audio).
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Two-pass approach for each modality:
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1. Column-name heuristic (fast): checks for keywords.
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2. Value-type inspection (reliable): checks actual sample values.
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Returns:
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dict: {
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"is_image": bool,
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"multimodal_columns": list of column names containing image data,
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"modality_types": list of detected types (e.g., ["image", "audio"]),
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"is_audio": bool,
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"audio_columns": list of column names containing audio data,
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"detected_audio_column": str or None,
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"detected_text_column": str or None,
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}
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"""
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sample = next(iter(dataset))
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column_names = list(sample.keys())
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# Keywords that indicate image data
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image_keywords = [
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'image', 'img', 'pixel',
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'jpg', 'jpeg', 'png', 'webp', 'bmp', 'gif', 'tiff', 'svg',
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'photo', 'pic', 'picture', 'visual',
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'file_name', 'filename',
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]
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# Keywords that indicate audio data
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audio_keywords = ['audio', 'speech', 'wav', 'waveform', 'sound']
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multimodal_columns = []
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audio_columns = []
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modality_types = set()
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# ── Image detection ─────────────────────────────────────
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# Pass 1: column-name heuristic (word-boundary match to avoid
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# false positives like 'pic' in 'topic')
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for col_name in column_names:
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for keyword in image_keywords:
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if _keyword_in_column(keyword, col_name):
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multimodal_columns.append(col_name)
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modality_types.add(keyword)
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break
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# Pass 2: inspect actual values
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already_detected = set(multimodal_columns)
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for col_name in column_names:
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if col_name in already_detected:
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continue
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value = sample[col_name]
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if _is_image_value(value):
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multimodal_columns.append(col_name)
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modality_types.add("image")
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# ── Audio detection ─────────────────────────────────────
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# Pass 1: column-name heuristic (word-boundary match)
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for col_name in column_names:
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for keyword in audio_keywords:
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if _keyword_in_column(keyword, col_name):
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audio_columns.append(col_name)
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modality_types.add("audio")
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break
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# Pass 2: inspect actual values (catches non-obvious column names)
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already_audio = set(audio_columns)
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for col_name in column_names:
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if col_name in already_audio:
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continue
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value = sample[col_name]
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if _is_audio_value(value):
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audio_columns.append(col_name)
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modality_types.add("audio")
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# Filter out columns that are actually audio from the image list
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# (e.g. a column named "audio" with {"bytes", "path"} could match _is_image_value)
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if audio_columns:
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audio_set = set(audio_columns)
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multimodal_columns = [c for c in multimodal_columns if c not in audio_set]
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# Detect text column for audio datasets
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detected_text_col = None
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if audio_columns:
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text_keywords = ['text', 'sentence', 'transcript', 'transcription', 'label']
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for col_name in column_names:
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if col_name.lower() in text_keywords:
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detected_text_col = col_name
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break
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is_audio = len(audio_columns) > 0
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# Detect speaker_id column for TTS datasets (CSM, Orpheus, Spark)
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detected_speaker_col = None
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if audio_columns:
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speaker_keywords = ['source', 'speaker', 'speaker_id']
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for col_name in column_names:
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if col_name.lower() in speaker_keywords:
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detected_speaker_col = col_name
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break
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return {
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"is_image": len(multimodal_columns) > 0,
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"multimodal_columns": multimodal_columns,
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"modality_types": list(modality_types),
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"is_audio": is_audio,
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"audio_columns": audio_columns,
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"detected_audio_column": audio_columns[0] if audio_columns else None,
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"detected_text_column": detected_text_col,
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"detected_speaker_column": detected_speaker_col,
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}
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def _is_image_value(value) -> bool:
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"""Check if a single sample value looks like image data."""
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if value is None:
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return False
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# PIL Image instance
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try:
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from PIL.Image import Image as PILImage
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if isinstance(value, PILImage):
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return True
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except ImportError:
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pass
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# HF datasets Image feature stores decoded images as PIL or dicts with
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# {"bytes": b"...", "path": "..."} when not yet decoded.
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# Exclude audio dicts (decoded audio has "array" + "sampling_rate").
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if isinstance(value, dict):
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if "array" in value and "sampling_rate" in value:
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return False # This is audio, not image
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if "bytes" in value and "path" in value:
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# Check path extension to exclude audio files
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path = value.get("path") or ""
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if isinstance(path, str) and any(path.lower().endswith(ext) for ext in _AUDIO_EXTENSIONS):
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return False
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return True
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# Raw bytes with a known image magic header
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if isinstance(value, (bytes, bytearray)):
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return _has_image_header(value)
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# String that looks like an image file path or URL
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_IMAGE_EXTS = ('.png', '.jpg', '.jpeg', '.webp', '.gif', '.bmp', '.tiff', '.svg')
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if isinstance(value, str) and len(value) < 1000:
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lower = value.strip().lower()
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# Image URL (http://... ending in image extension)
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if lower.startswith(("http://", "https://")) and any(lower.split("?")[0].endswith(ext) for ext in _IMAGE_EXTS):
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return True
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# Image file path (relative or absolute path ending in image extension)
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if any(lower.endswith(ext) for ext in _IMAGE_EXTS):
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return True
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return False
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_AUDIO_EXTENSIONS = (
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".wav", ".mp3", ".flac", ".ogg", ".opus", ".m4a", ".aac", ".wma", ".webm",
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)
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def _is_audio_value(value) -> bool:
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"""Check if a single sample value looks like audio data."""
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if value is None:
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return False
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# HF datasets Audio feature: decoded → {"array": np.ndarray, "sampling_rate": int}
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if isinstance(value, dict):
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if "array" in value and "sampling_rate" in value:
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return True
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# Undecoded/streaming → {"bytes": b"...", "path": "some.wav"}
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if "bytes" in value or "path" in value:
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path = value.get("path") or ""
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if isinstance(path, str) and any(path.lower().endswith(ext) for ext in _AUDIO_EXTENSIONS):
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return True
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return False
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def _has_image_header(data: bytes) -> bool:
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"""Quick magic-byte check for common image formats."""
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if len(data) < 4:
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return False
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# JPEG
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if data[:2] == b'\xff\xd8':
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return True
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|
# PNG
|
|
if data[:4] == b'\x89PNG':
|
|
return True
|
|
# GIF
|
|
if data[:3] == b'GIF':
|
|
return True
|
|
# WebP
|
|
if data[:4] == b'RIFF' and len(data) >= 12 and data[8:12] == b'WEBP':
|
|
return True
|
|
# BMP
|
|
if data[:2] == b'BM':
|
|
return True
|
|
return False
|
|
|
|
|
|
def detect_vlm_dataset_structure(dataset):
|
|
"""
|
|
Detects if VLM dataset is:
|
|
- Standard VLM messages format (image objects in content)
|
|
- Llava format (image indices + separate images column)
|
|
- Simple format needing conversion (image + text columns)
|
|
"""
|
|
try:
|
|
sample = next(iter(dataset))
|
|
except StopIteration:
|
|
return {
|
|
"format": "unknown",
|
|
"needs_conversion": None,
|
|
"image_column": None,
|
|
"text_column": None,
|
|
"messages_column": None,
|
|
}
|
|
|
|
column_names = set(sample.keys())
|
|
|
|
# Check if has messages column
|
|
if "messages" in column_names:
|
|
messages = sample["messages"]
|
|
|
|
if messages and len(messages) > 0:
|
|
first_msg = messages[0]
|
|
if "content" in first_msg:
|
|
content = first_msg["content"]
|
|
|
|
if isinstance(content, list) and len(content) > 0:
|
|
if isinstance(content[0], dict) and "type" in content[0]:
|
|
|
|
# Check for llava format
|
|
has_index = any('index' in item for item in content if isinstance(item, dict))
|
|
has_images_column = 'images' in column_names
|
|
|
|
if has_index and has_images_column:
|
|
return {
|
|
"format": "vlm_messages_llava",
|
|
"needs_conversion": True,
|
|
"messages_column": "messages",
|
|
"image_column": "images",
|
|
"text_column": None,
|
|
}
|
|
|
|
# Standard VLM format
|
|
has_image = any('image' in item for item in content if isinstance(item, dict))
|
|
if has_image:
|
|
return {
|
|
"format": "vlm_messages",
|
|
"needs_conversion": False,
|
|
"messages_column": "messages",
|
|
"image_column": None,
|
|
"text_column": None,
|
|
}
|
|
|
|
# Check for ShareGPT/ChatML conversations with <image> placeholder + companion image column
|
|
# (e.g. Lin-Chen/ShareGPT4V, LLaVA-style datasets)
|
|
for chat_col in ("conversations", "messages"):
|
|
if chat_col not in column_names:
|
|
continue
|
|
chat_data = sample[chat_col]
|
|
if not isinstance(chat_data, list) or len(chat_data) == 0:
|
|
continue
|
|
first_msg = chat_data[0]
|
|
if not isinstance(first_msg, dict):
|
|
continue
|
|
# Detect ShareGPT (from/value) or ChatML (role/content) keys
|
|
msg_text = first_msg.get("value") or first_msg.get("content")
|
|
if not isinstance(msg_text, str):
|
|
continue
|
|
# Check for <image> placeholder anywhere in the conversation
|
|
has_image_placeholder = any(
|
|
"<image>" in str(m.get("value", "") or m.get("content", ""))
|
|
for m in chat_data
|
|
if isinstance(m, dict)
|
|
)
|
|
if not has_image_placeholder:
|
|
continue
|
|
# Find companion image column
|
|
image_col = None
|
|
for col in column_names:
|
|
if col == chat_col:
|
|
continue
|
|
if _keyword_in_column("image", col) or _keyword_in_column("img", col):
|
|
image_col = col
|
|
break
|
|
if image_col:
|
|
return {
|
|
"format": "sharegpt_with_images",
|
|
"needs_conversion": True,
|
|
"image_column": image_col,
|
|
"text_column": None,
|
|
"messages_column": chat_col,
|
|
}
|
|
|
|
# Find image and text columns using metadata filtering
|
|
|
|
# Define metadata patterns to EXCLUDE
|
|
metadata_patterns = {
|
|
'suffixes': ['_id', '_url', '_name', '_filename', '_uri', '_link', '_key', '_index'],
|
|
'prefixes': ['id_', 'url_', 'name_', 'filename_', 'uri_', 'link_', 'key_', 'index_'],
|
|
}
|
|
|
|
# Image-related keywords
|
|
image_keywords = ['image', 'img', 'photo', 'picture', 'pic', 'visual', 'scan', 'file_name', 'filename']
|
|
|
|
# Text-related keywords
|
|
text_keywords = ['text', 'caption', 'captions', 'description', 'answer', 'output', 'response', 'label']
|
|
|
|
def is_metadata_column(col_name):
|
|
"""Check if column name looks like metadata."""
|
|
col_lower = col_name.lower()
|
|
|
|
# Check suffixes
|
|
if any(col_lower.endswith(suffix) for suffix in metadata_patterns['suffixes']):
|
|
return True
|
|
|
|
# Check prefixes
|
|
if any(col_lower.startswith(prefix) for prefix in metadata_patterns['prefixes']):
|
|
return True
|
|
|
|
return False
|
|
|
|
def _score_image_candidate(col, sample_value):
|
|
"""Score a candidate image column by how resolvable its value is."""
|
|
# PIL Image object (highest priority - already loaded)
|
|
if hasattr(sample_value, 'size') and hasattr(sample_value, 'mode'):
|
|
return 100
|
|
|
|
# Dict with image data (bytes/path from HF Image feature)
|
|
if isinstance(sample_value, dict) and ('bytes' in sample_value or 'path' in sample_value):
|
|
return 75
|
|
|
|
if isinstance(sample_value, str):
|
|
# URL strings
|
|
if sample_value.startswith(("http://", "https://")):
|
|
return 70 if not is_metadata_column(col) else 55
|
|
# Bare file path
|
|
if is_metadata_column(col):
|
|
return 30
|
|
return 50
|
|
|
|
return 0
|
|
|
|
def _probe_image_candidate(col, sample_value):
|
|
"""Quick probe to check if an image candidate is actually reachable.
|
|
Returns True if likely valid, False if definitely broken."""
|
|
import os
|
|
|
|
# PIL / dict — already loaded, always valid
|
|
if not isinstance(sample_value, str):
|
|
return True
|
|
|
|
# Local file — check it exists
|
|
if not sample_value.startswith(("http://", "https://")):
|
|
return os.path.exists(sample_value) # bare filenames return False here, that's OK
|
|
|
|
# URL — quick HEAD request with short timeout
|
|
try:
|
|
import urllib.request
|
|
req = urllib.request.Request(sample_value, method="HEAD")
|
|
resp = urllib.request.urlopen(req, timeout=3)
|
|
return resp.status < 400
|
|
except Exception:
|
|
return False
|
|
|
|
def find_image_column():
|
|
"""Find image column by keyword match + value-based fallback.
|
|
When multiple candidates exist, probes them to find one that works."""
|
|
candidates = []
|
|
|
|
# Pass 1: keyword-matched columns
|
|
for col in column_names:
|
|
if any(_keyword_in_column(keyword, col) for keyword in image_keywords):
|
|
sample_value = sample[col]
|
|
score = _score_image_candidate(col, sample_value)
|
|
if score > 0:
|
|
candidates.append((col, score))
|
|
|
|
# Pass 2: value-based fallback — find columns with image URLs/paths
|
|
# even if the column name doesn't match image keywords
|
|
already = {c[0] for c in candidates}
|
|
for col in column_names:
|
|
if col in already:
|
|
continue
|
|
sample_value = sample[col]
|
|
if _is_image_value(sample_value):
|
|
score = _score_image_candidate(col, sample_value)
|
|
# Slightly penalise non-keyword columns so keyword matches win on ties
|
|
candidates.append((col, max(score - 5, 1)))
|
|
|
|
if not candidates:
|
|
return None
|
|
|
|
candidates.sort(key=lambda x: x[1], reverse=True)
|
|
|
|
# Single candidate or top candidate is PIL/dict — no probing needed
|
|
if len(candidates) == 1 or candidates[0][1] >= 75:
|
|
return candidates[0][0]
|
|
|
|
# Multiple string-based candidates — probe to find one that actually works
|
|
for col, score in candidates:
|
|
sample_value = sample[col]
|
|
if _probe_image_candidate(col, sample_value):
|
|
return col
|
|
|
|
# Nothing probed successfully — return highest-scored anyway and let
|
|
# conversion handle the error (it may still resolve via hf_hub_download)
|
|
return candidates[0][0]
|
|
|
|
def find_text_column():
|
|
"""Find text column by filtering out metadata and checking keywords."""
|
|
candidates = []
|
|
|
|
for col in column_names:
|
|
# Skip metadata columns
|
|
if is_metadata_column(col):
|
|
continue
|
|
|
|
# Check if contains text keywords (word-boundary match)
|
|
if any(_keyword_in_column(keyword, col) for keyword in text_keywords):
|
|
# Verify it's actually text
|
|
sample_value = sample[col]
|
|
|
|
if isinstance(sample_value, str) and len(sample_value) > 0:
|
|
# Longer text = higher priority (likely content, not just a label)
|
|
priority = min(len(sample_value), 1000) # Cap at 1000
|
|
candidates.append((col, priority))
|
|
elif isinstance(sample_value, list) and len(sample_value) > 0 and isinstance(sample_value[0], str):
|
|
# List of strings (e.g. captions list) — lower priority than plain strings
|
|
priority = min(len(sample_value[0]), 1000) // 2
|
|
candidates.append((col, priority))
|
|
|
|
# Return highest priority candidate
|
|
if candidates:
|
|
candidates.sort(key=lambda x: x[1], reverse=True)
|
|
return candidates[0][0]
|
|
|
|
return None
|
|
|
|
found_image = find_image_column()
|
|
found_text = find_text_column()
|
|
|
|
if found_image and found_text:
|
|
return {
|
|
"format": "simple_image_text",
|
|
"needs_conversion": True,
|
|
"image_column": found_image,
|
|
"text_column": found_text,
|
|
"messages_column": None,
|
|
}
|
|
|
|
return {
|
|
"format": "unknown",
|
|
"needs_conversion": None,
|
|
"image_column": found_image,
|
|
"text_column": found_text,
|
|
"messages_column": None,
|
|
}
|