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import os
def load_jsonl(path):
"""
Loads a JSONL file into a dictionary keyed by 'doc_id'.
"""
data = {}
try:
with open(path, 'r', encoding='utf-8') as f:
for i, line in enumerate(f):
try:
record = json.loads(line)
# Use 'doc_id' as the unique key for comparison
if 'doc_id' not in record:
print(f"Warning: Missing 'doc_id' in {path} at line {i + 1}. Skipping.")
continue
data[record['doc_id']] = record
except json.JSONDecodeError:
print(f"Error: Could not parse JSON in {path} at line {i + 1}. Skipping.")
except FileNotFoundError:
print(f"Error: File not found at {path}")
return None
except Exception as e:
print(f"Error opening file {path}: {e}")
return None
print(f"Successfully loaded {len(data)} records from {path}.")
return data
def find_failure_cases_textvqa(ours_output_path, baseline_output_path, score_key="exact_match"):
"""
Compares two output files and finds items where 'ours' is wrong (0.0)
and 'baseline' is right (1.0).
Args:
ours_output_path (str): Path to your model's output JSONL.
baseline_output_path (str): Path to the baseline model's output JSONL.
score_key (str): The key in the JSON to find the score dictionary.
Default is "vstar_overall_acc".
Returns:
list: A list of dictionaries, each containing details of a failure case.
"""
failure_cases = []
# Load the baseline results into a dictionary for fast lookup
baseline_results = load_jsonl(baseline_output_path)
if baseline_results is None:
return []
print(f"\nProcessing {ours_output_path} against baseline...")
try:
with open(ours_output_path, 'r', encoding='utf-8') as f:
for i, line in enumerate(f):
try:
our_record = json.loads(line)
if 'doc_id' not in our_record:
print(f"Warning: Missing 'doc_id' in 'ours' file at line {i + 1}. Skipping.")
continue
doc_id = our_record['doc_id']
# Check if this doc_id exists in the baseline
if doc_id not in baseline_results:
print(f"Warning: doc_id {doc_id} from 'ours' file not found in baseline. Skipping.")
continue
baseline_record = baseline_results[doc_id]
# Check if score keys exist before accessing
if score_key not in our_record :
print(f"Warning: Missing score key '{score_key}' in 'ours' file for doc_id {doc_id}. Skipping.")
continue
if score_key not in baseline_record:
print(
f"Warning: Missing score key '{score_key}' in baseline file for doc_id {doc_id}. Skipping.")
continue
# Get the scores
if type(our_record[score_key]) is not dict or type(baseline_record[score_key]) is not dict:
our_score = our_record[score_key]
baseline_score = baseline_record[score_key]
else:
our_score = our_record[score_key]['score']
baseline_score = baseline_record[score_key]['score']
# The core logic: Find where we failed and baseline succeeded
if our_score < 0.3 and baseline_score > 0.7:
# Format a clean dictionary for case study
failure_info = {
"doc_id": doc_id,
"question": our_record['input'],
"ground_truth": baseline_record['filtered_resps'][0],
"our_prediction": our_record['filtered_resps'][0],#[score_key].get('prediction', 'N/A'),
"baseline_prediction": baseline_record['filtered_resps'][0],
"full_our_record": our_record # Optional: include for full context
}
failure_cases.append(failure_info)
except json.JSONDecodeError:
print(f"Error: Could not parse JSON in 'ours' file at line {i + 1}. Skipping.")
except KeyError:
# This catches if a sub-key like 'score' is missing
print(f"Error: Missing expected key structure in 'ours' file at line {i + 1}. Skipping.")
except FileNotFoundError:
print(f"Error: File not found at {ours_output_path}")
return []
except Exception as e:
print(f"Error opening file {ours_output_path}: {e}")
return []
print(f"\n--- Analysis Complete ---")
print(f"Found {len(failure_cases)} failure cases where 'ours' was wrong and 'baseline' was right.")
return failure_cases
def find_failure_cases_vstar(ours_output_path, baseline_output_path, score_key="vstar_overall_acc"):
"""
Compares two output files and finds items where 'ours' is wrong (0.0)
and 'baseline' is right (1.0).
Args:
ours_output_path (str): Path to your model's output JSONL.
baseline_output_path (str): Path to the baseline model's output JSONL.
score_key (str): The key in the JSON to find the score dictionary.
Default is "vstar_overall_acc".
Returns:
list: A list of dictionaries, each containing details of a failure case.
"""
failure_cases = []
# Load the baseline results into a dictionary for fast lookup
baseline_results = load_jsonl(baseline_output_path)
if baseline_results is None:
return []
print(f"\nProcessing {ours_output_path} against baseline...")
try:
with open(ours_output_path, 'r', encoding='utf-8') as f:
for i, line in enumerate(f):
try:
our_record = json.loads(line)
if 'doc_id' not in our_record:
print(f"Warning: Missing 'doc_id' in 'ours' file at line {i + 1}. Skipping.")
continue
doc_id = our_record['doc_id']
# Check if this doc_id exists in the baseline
if doc_id not in baseline_results:
print(f"Warning: doc_id {doc_id} from 'ours' file not found in baseline. Skipping.")
continue
baseline_record = baseline_results[doc_id]
# Check if score keys exist before accessing
if score_key not in our_record or 'score' not in our_record[score_key]:
print(f"Warning: Missing score key '{score_key}' in 'ours' file for doc_id {doc_id}. Skipping.")
continue
if score_key not in baseline_record or 'score' not in baseline_record[score_key]:
print(
f"Warning: Missing score key '{score_key}' in baseline file for doc_id {doc_id}. Skipping.")
continue
# Get the scores
our_score = our_record[score_key]['score']
baseline_score = baseline_record[score_key]['score']
# The core logic: Find where we failed and baseline succeeded
if our_score == 0.0 and baseline_score == 1.0:
# Format a clean dictionary for case study
failure_info = {
"doc_id": doc_id,
"question": our_record.get('doc', {}).get('text', 'N/A'),
"ground_truth": our_record.get('target', 'N/A'),
"our_prediction": our_record[score_key].get('prediction', 'N/A'),
"baseline_prediction": baseline_record[score_key].get('prediction', 'N/A'),
"full_our_record": our_record # Optional: include for full context
}
failure_cases.append(failure_info)
except json.JSONDecodeError:
print(f"Error: Could not parse JSON in 'ours' file at line {i + 1}. Skipping.")
except KeyError:
# This catches if a sub-key like 'score' is missing
print(f"Error: Missing expected key structure in 'ours' file at line {i + 1}. Skipping.")
except FileNotFoundError:
print(f"Error: File not found at {ours_output_path}")
return []
except Exception as e:
print(f"Error opening file {ours_output_path}: {e}")
return []
print(f"\n--- Analysis Complete ---")
print(f"Found {len(failure_cases)} failure cases where 'ours' was wrong and 'baseline' was right.")
return failure_cases
# --- Example Usage ---
if __name__ == "__main__":
# --- 1. Create dummy data files for the example ---
# This example has 3 cases:
# 1. We fail (B), baseline succeeds (A). -> This should be found.
# 2. We succeed (A), baseline fails (C). -> This should be ignored.
# 3. Both succeed (A). -> This should be ignored.
# # Define file paths
# baseline_file = "./logs/liuhaotian__llava-v1.5-7b/20250902_190647_samples_textvqa_val.jsonl"
# ours_file = "./logs/checkpoints__llava-v1.5-7b-roi-K15T3-152k-v1bf16Mheads-bg0p05fg0p2-filled/20251113_113923_samples_textvqa_val.jsonl"
#
# #failures = find_failure_cases_textvqa(ours_file, baseline_file, 'exact_match')
# failures = find_failure_cases_textvqa(baseline_file, ours_file , 'exact_match')
#baseline_file = "./logs/liuhaotian__llava-v1.5-7b/20250902_201850_samples_vstar_bench.jsonl"
#ours_file = "./logs/checkpoints__llava-v1.5-7b-roi-K15T3-152k-v1bf16Mheads-bg0p05fg0p2-filled/20251113_130922_samples_vstar_bench.jsonl"
# baseline_file = "../Qwen2.5-VL/logs/Qwen__Qwen2.5-VL-7B-Instruct/20251113_135313_samples_vstar_bench.jsonl"
# ours_file = "../Qwen2.5-VL/logs/output__qwen2_5vl-7b-roi-K16T3-152k-v1bf16Mheads-twiginit-filled/20251113_134244_samples_vstar_bench.jsonl"
#
# failures = find_failure_cases_vstar(baseline_file, ours_file)
# failure_ids = [case['doc_id'] for case in failures]
###### TextVQA Failure Case Analysis
#baseline_file_textvqa = "logs/output__qwen2_5vl-7b-roi-K18T3-185k-v1bf16-denoisedv2-twiginit-filled/20251209_181751_samples_textvqa_val.jsonl"
baseline_file_textvqa = "logs/Qwen__Qwen3-VL-4B-Instruct/20260122_115842_samples_textvqa_val.jsonl"
ours_file_textvqa = "logs/output__qwen3vl-4b-roi-K24T3-185k-v1bf16-twiginit/20260122_120855_samples_textvqa_val.jsonl"
failures_baseline_textvqa = find_failure_cases_textvqa(baseline_file_textvqa, ours_file_textvqa, 'exact_match')
failure_ids_baseline_textvqa = [case['doc_id'] for case in failures_baseline_textvqa]
failures_ours_textvqa = find_failure_cases_textvqa(ours_file_textvqa, baseline_file_textvqa, 'exact_match')
failure_ids_ours_textvqa = [case['doc_id'] for case in failures_ours_textvqa]
failure_ids_ours_textvqa_dict = {case['doc_id']: case for case in failures_ours_textvqa}
### print some results
for i, case in enumerate(failures_ours_textvqa[:10]):
print(f"\nCase {i + 1} (doc_id: {case['doc_id']})")
print(f" Question: {case['question']}")
print(f" Ground Truth: {case['ground_truth']}")
print(f" Our (Wrong) Prediction: {case['our_prediction']}")
print(f" Baseline (Correct) Prediction: {case['baseline_prediction']}")
# ###### InfoVQA Failure Case Analysis
# baseline_file_infovqa = "../Qwen2.5-VL/logs/Qwen__Qwen2.5-VL-7B-Instruct/20251204_093838_samples_infovqa_val.jsonl"
# ours_file_infovqa = "../Qwen2.5-VL/logs/output__qwen2_5vl-7b-roi-K18T3-185k-v1bf16-denoisedv2-twiginit-filled/20251215_131620_samples_infovqa_val.jsonl"
#
# failures_baseline_infovqa = find_failure_cases_textvqa(baseline_file_infovqa, ours_file_infovqa, 'anls')
# failure_ids_baseline_infovqa = [case['doc_id'] for case in failures_baseline_infovqa]
#
# failures_ours_infovqa = find_failure_cases_textvqa(ours_file_infovqa, baseline_file_infovqa, 'anls')
# failure_ids_ours_infovqa = [case['doc_id'] for case in failures_ours_infovqa]
#
# ##### DocVQA Failure Case Analysis
# baseline_file_docvqa = "../Qwen2.5-VL/logs/Qwen__Qwen2.5-VL-7B-Instruct/20251204_095001_samples_docvqa_val.jsonl"
# ours_file_docvqa = "../Qwen2.5-VL/logs/output__qwen2_5vl-7b-roi-K18T3-134k-gatedv2PB-wGTwRoI-twiginit-filled/20251210_143202_samples_docvqa_val.jsonl"
#
# failures_baseline_docvqa = find_failure_cases_textvqa(baseline_file_docvqa, ours_file_docvqa, 'anls')
# failure_ids_baseline_docvqa = [case['doc_id'] for case in failures_baseline_docvqa]
# failures_ours_docvqa = find_failure_cases_textvqa(ours_file_docvqa, baseline_file_docvqa, 'anls')
# failure_ids_ours_docvqa = [case['doc_id'] for case in failures_ours_docvqa]
#
# ##### ChartQA Failure Case Analysis
# baseline_file_chartqa = "../Qwen2.5-VL/logs/Qwen__Qwen2.5-VL-7B-Instruct/20251204_094359_samples_chartqa.jsonl"
# ours_file_chartqa = "../Qwen2.5-VL/logs/output__qwen2_5vl-7b-roi-K18T3-134k-gatedv2PB-wGTwRoI-twiginit-filled/20251210_142453_samples_chartqa.jsonl"
#
# failures_baseline_chartqa = find_failure_cases_textvqa(baseline_file_chartqa, ours_file_chartqa, 'relaxed_overall')
# failure_ids_baseline_chartqa = [case['doc_id'] for case in failures_baseline_chartqa]
# failures_ours_chartqa = find_failure_cases_textvqa(ours_file_chartqa, baseline_file_chartqa, 'relaxed_overall')
# failure_ids_ours_chartqa = [case['doc_id'] for case in failures_ours_chartqa]
#
#
# ###### OCRBench Failure Case Analysis
# baseline_file_ocrbench = "../Qwen2.5-VL/logs/Qwen__Qwen2.5-VL-7B-Instruct/20251204_094800_samples_ocrbench.jsonl"
# ours_file_ocrbench = "../Qwen2.5-VL/logs/output__qwen2_5vl-7b-roi-K18T3-134k-gatedv2PB-wGTwRoI-twiginit-filled/20251210_142936_samples_ocrbench.jsonl"
# failures_baseline_ocrbench = find_failure_cases_textvqa(baseline_file_ocrbench, ours_file_ocrbench, 'ocrbench_accuracy')
# failure_ids_baseline_ocrbench = [case['doc_id'] for case in failures_baseline_ocrbench]
# failures_ours_ocrbench = find_failure_cases_textvqa(ours_file_ocrbench, baseline_file_ocrbench, 'ocrbench_accuracy')
# failure_ids_ours_ocrbench = [case['doc_id'] for case in failures_ours_ocrbench]
#
pass
# if failure_ids_ours:
# print("\n--- Failure Cases ---")
# for i, case in enumerate(failure_ids_ours):
# print(f"\nCase {i + 1} (doc_id: {case['doc_id']})")
# print(f" Question: {case['question']}")
# print(f" Ground Truth: {case['ground_truth']}")
# print(f" Our (Wrong) Prediction: {case['our_prediction']}")
# print(f" Baseline (Correct) Prediction: {case['baseline_prediction']}")
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