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from typing import Dict, List, Optional, Union
import os
from dataclasses import dataclass
from datetime import datetime
from pathlib import Path
@dataclass
class QA:
question: str
answer: Optional[str]
evidence: List[str]
category: Optional[int] = None
adversarial_answer: Optional[str] = None
@property
def final_answer(self) -> Optional[str]:
"""Get the appropriate answer based on category."""
if self.category == 5:
return self.adversarial_answer
return self.answer
@dataclass
class Turn:
speaker: str
dia_id: str
text: str
@dataclass
class Session:
session_id: int
date_time: str
turns: List[Turn]
@dataclass
class Conversation:
speaker_a: str
speaker_b: str
sessions: Dict[int, Session]
@dataclass
class EventSummary:
events: Dict[str, Dict[str, List[str]]] # session -> speaker -> events
@dataclass
class Observation:
observations: Dict[str, Dict[str, List[List[str]]]] # session -> speaker -> [observation, evidence]
@dataclass
class LoCoMoSample:
"""A single sample from the LoComo dataset"""
sample_id: str
qa: List[QA]
conversation: Conversation
event_summary: EventSummary
observation: Observation
session_summary: Dict[str, str]
def parse_session(session_data: List[dict], session_id: int, date_time: str) -> Session:
"""Parse a single session's data, including turns with images by using their captions."""
turns = []
for turn in session_data:
# For turns with images, combine caption and text
text = turn.get("text", "")
if "img_url" in turn and "blip_caption" in turn:
caption_text = f"[Image: {turn['blip_caption']}]"
if text:
text = f"{caption_text} {text}"
else:
text = caption_text
turns.append(Turn(
speaker=turn["speaker"],
dia_id=turn["dia_id"],
text=text
))
return Session(session_id=session_id, date_time=date_time, turns=turns)
def parse_conversation(conv_data: dict) -> Conversation:
"""Parse conversation data."""
sessions = {}
for key, value in conv_data.items():
if key.startswith("session_") and isinstance(value, list):
session_id = int(key.split("_")[1])
date_time = conv_data.get(f"{key}_date_time")
if date_time:
session = parse_session(value, session_id, date_time)
# Only add sessions that have turns after filtering
if session.turns:
sessions[session_id] = session
return Conversation(
speaker_a=conv_data["speaker_a"],
speaker_b=conv_data["speaker_b"],
sessions=sessions
)
def load_locomo_dataset(file_path: Union[str, Path]) -> List[LoCoMoSample]:
"""
Load the LoComo dataset from a JSON file, including image-based content by using captions.
Args:
file_path: Path to the JSON file containing the dataset
Returns:
List of LoCoMoSample objects containing the parsed data
"""
if isinstance(file_path, str):
file_path = Path(file_path)
if not file_path.exists():
raise FileNotFoundError(f"Dataset file not found at {file_path}")
with open(file_path, 'r', encoding='utf-8') as f:
data = json.load(f)
samples = []
total_qa = 0
total_image_qa = 0
qa_counts_per_sample = []
for sample_idx, sample in enumerate(data):
try:
# Parse QA data
qa_list = []
sample_qa_count = 0
sample_image_qa_count = 0
for qa_idx, qa in enumerate(sample["qa"]):
try:
# Check if QA has image evidence
has_image_evidence = False
for evidence_id in qa.get("evidence", []):
if ":" not in evidence_id:
continue
turn_id = evidence_id.split(":")[1]
for session in sample["conversation"].values():
if isinstance(session, list):
for turn in session:
if turn.get("dia_id", "").endswith(turn_id):
if "img_url" in turn or "blip_caption" in turn:
has_image_evidence = True
break
if has_image_evidence:
sample_image_qa_count += 1
qa_obj = QA(
question=qa["question"],
answer=qa.get("answer"),
evidence=qa.get("evidence", []),
category=qa.get("category"),
adversarial_answer=qa.get("adversarial_answer")
)
qa_list.append(qa_obj)
sample_qa_count += 1
except KeyError as e:
print(f"Error in sample {sample_idx}, QA pair {qa_idx}:")
print(f"QA data: {qa}")
raise e
except Exception as e:
print(f"Unexpected error in sample {sample_idx}, QA pair {qa_idx}:")
print(f"QA data: {qa}")
raise e
# Parse conversation
conversation = parse_conversation(sample["conversation"])
# Parse event summary
event_summary = EventSummary(events=sample["event_summary"])
# Parse observation
observation = Observation(observations=sample["observation"])
# Get session summary
session_summary = sample.get("session_summary", {})
# Create sample object
sample_obj = LoCoMoSample(
sample_id=str(sample_idx),
qa=qa_list,
conversation=conversation,
event_summary=event_summary,
observation=observation,
session_summary=session_summary
)
samples.append(sample_obj)
total_qa += sample_qa_count
total_image_qa += sample_image_qa_count
qa_counts_per_sample.append(sample_qa_count)
# Print statistics for this sample
print(f"\nSample {sample_idx}:")
print(f" Total QAs: {sample_qa_count}")
print(f" QAs with image evidence: {sample_image_qa_count}")
except Exception as e:
print(f"Error processing sample {sample_idx}:")
print(str(e))
raise e
# Print overall statistics
print("\nOverall Statistics:")
print(f"Total QAs: {total_qa}")
print(f"Total QAs with image evidence: {total_image_qa}")
print(f"Average QAs per sample: {total_qa / len(samples):.2f}")
print(f"Min QAs in a sample: {min(qa_counts_per_sample)}")
print(f"Max QAs in a sample: {max(qa_counts_per_sample)}")
return samples
def get_dataset_statistics(samples: List[LoCoMoSample]) -> Dict:
"""
Get basic statistics about the text-only dataset.
Args:
samples: List of LoCoMoSample objects
Returns:
Dictionary containing various statistics about the dataset
"""
stats = {
"num_samples": len(samples),
"total_qa_pairs": sum(len(sample.qa) for sample in samples),
"total_sessions": sum(len(sample.conversation.sessions) for sample in samples),
"total_turns": sum(
sum(len(session.turns) for session in sample.conversation.sessions.values())
for sample in samples
),
"qa_with_adversarial": sum(
sum(1 for qa in sample.qa if qa.adversarial_answer is not None)
for sample in samples
)
}
return stats
if __name__ == "__main__":
# Example usage
dataset_path = Path(__file__).parent / "data" / "locomo10.json"
try:
print(f"Loading dataset from: {dataset_path}")
samples = load_locomo_dataset(dataset_path)
for sample_idx, sample in enumerate(samples):
print(f"\nSample {sample_idx}:")
for _,turns in sample.conversation.sessions.items():
for turn in turns.turns:
print(turn)
break
# stats = get_dataset_statistics(samples)
# print("\nDataset Statistics (Text-only content):")
# for key, value in stats.items():
# print(f"{key}: {value}")
# print(len(samples))
# for sample in samples:
# print(sample)
# break
except Exception as e:
print(f"Error loading dataset: {e}")
raise |