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import os |
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import sys |
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sys.path.append(os.path.dirname(os.path.abspath(__file__))) |
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from dvc_eval import eval_dvc, eval_soda |
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import json |
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import argparse |
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import re |
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import difflib |
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import psutil |
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def set_cpu_affinity(start_idx=0,end_idx=128): |
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p = psutil.Process() |
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p.cpu_affinity(list(range(start_idx,end_idx))) |
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def print_metrics(metrics): |
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for k, v in metrics.items(): |
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print(f"{k}: {v:.2f}") |
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def save_metrics(metrics, path, num_logs): |
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if not os.path.exists(os.path.dirname(path)): |
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os.makedirs(os.path.dirname(path)) |
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with open(path, 'w') as f: |
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for k, v in metrics.items(): |
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f.write(f"{k}: {v:.2f}\n") |
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f.write(f"Num samples: {num_logs}\n") |
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def merge_similar_sentences(data): |
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if not data: return data |
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merged_data = [] |
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current_sentence = data[0]["sentence"] |
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current_timestamp = data[0]["timestamp"] |
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for i in range(1, len(data)): |
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next_sentence = data[i]["sentence"] |
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next_timestamp = data[i]["timestamp"] |
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if difflib.SequenceMatcher(None, current_sentence, next_sentence).ratio() > 0.98 and -1 <= next_timestamp[0] - current_timestamp[1] <= 1: |
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current_timestamp = [current_timestamp[0], next_timestamp[1]] |
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else: |
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merged_data.append({"sentence": current_sentence, "timestamp": current_timestamp}) |
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current_sentence = next_sentence |
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current_timestamp = next_timestamp |
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merged_data.append({"sentence": current_sentence, "timestamp": current_timestamp}) |
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return merged_data |
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def captioning_metrics(all_logs, data_path, print_matrix, args): |
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logs = [x for x in all_logs if x['task'] == 'captioning'] |
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pred = {} |
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num_duplicates = 0 |
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for log in logs: |
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id = log['video_id'] |
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answer = log['answer'] |
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pred[id] = [] |
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if args.reproduced: |
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pattern = r'from (\d+) to (\d+)' |
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try: |
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items = answer.split(".")[:-1] |
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all_items = [] |
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for i in items: |
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all_items.extend(i.split(', ')) |
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pattern = r'(\d+) to (\d+)' |
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items = all_items |
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sentences = [ i for i in items if re.search(pattern, i, re.IGNORECASE) is None] |
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timestamps = [ i for i in items if re.search(pattern, i, re.IGNORECASE) is not None ] |
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for sen, time in zip(sentences, timestamps): |
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sen = sen.strip()[:] |
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time = time.strip()[:] |
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matches = re.search(pattern, time, re.IGNORECASE) |
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pred[id].append({ |
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'timestamp': [int(matches.group(1)), int(matches.group(2))], |
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'sentence': sen, |
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}) |
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except Exception as e: |
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print("Error", e, answer) |
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else: |
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try: |
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items = answer.split(".")[:-1] |
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for num in range(0, len(items) // 2, 2): |
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sen = items[num].strip()[4:] |
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time = items[len(items) // 2 + num ].strip()[4:] |
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pred[id].append({ |
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'timestamp': [int(time[5:7]), int(time[-2:])], |
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'sentence': sen, |
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}) |
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except Exception as e: |
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print("Error", e, answer) |
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refined_pred = [] |
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for num_pred, curr_pred in enumerate(pred[id]): |
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duplicate = False |
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for curr_pred2 in pred[id][num_pred + 1:]: |
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if curr_pred2 == curr_pred: |
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num_duplicates+=1 |
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duplicate=True |
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if not duplicate: |
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refined_pred.append(curr_pred) |
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pred[id] = refined_pred |
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print(f"{num_duplicates} have been removed") |
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print(len(pred)) |
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gt_js = json.load(open(data_path)) |
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gt_js = {k: v for k, v in gt_js.items() if k in pred.keys()} |
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for id, items in list(pred.items()): |
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items = merge_similar_sentences(items) |
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duration = gt_js[id]['duration'] |
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for item in items: |
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item['timestamp'][0] = item['timestamp'][0] * duration / 100 |
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item['timestamp'][1] = (item['timestamp'][1] + 1) * duration / 100 |
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pred[id] = items |
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pred_result = {'results': pred} |
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metrics = eval_soda(pred_result, [gt_js], print_matrix=print_matrix) |
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metrics.update(eval_dvc(pred_result, [gt_js], |
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tious=[0.3, 0.5, 0.7, 0.9], |
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distances=[], |
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max_proposals_per_video=1000, |
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verbose=False, |
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no_lang_eval=False)) |
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print(f"Found {len(pred)} logs") |
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metrics = {k: v * 100 for k, v in metrics.items() if k in ['soda_c', 'METEOR', 'CIDEr']} |
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return metrics |
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def grounding_metrics(all_logs): |
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ious = [x['info']['iou'] for x in all_logs if x['task'] == 'grounding'] |
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l = len(ious) |
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print(f"Found {l} logs") |
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if l == 0: return |
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metrics = { |
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"mIoU": sum(ious) / l * 100 |
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} |
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for m in [0.3, 0.5, 0.7]: |
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metrics[f"R1@{m}"] = sum(iou >= m for iou in ious) / l * 100 |
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return metrics |
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if __name__ == "__main__": |
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set_cpu_affinity(start_idx=0,end_idx=128) |
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parser = argparse.ArgumentParser() |
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parser.add_argument("--log_path", type=str, default='vtimellm/eval/log/example_log.txt') |
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parser.add_argument("--task", type=str, default='all', choices=['all', 'grounding', 'captioning']) |
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parser.add_argument("--data_path", type=str, default='vtimellm/eval/data_example.json') |
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parser.add_argument('--result_path', type=str, default='vtimellm/eval/result/result.txt') |
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parser.add_argument("--reproduced", action='store_true') |
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parser.add_argument("--print_matrix", action='store_true') |
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args = parser.parse_args() |
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logs = [] |
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with open(args.log_path) as f: |
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for line in f: |
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try: |
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json_data = json.loads(line) |
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logs.append(json_data) |
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except Exception as e: |
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print(e, line) |
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if args.task in ['captioning', 'all']: |
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print("====================== Captioning =====================") |
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cap_metrics = captioning_metrics(logs, args.data_path, print_matrix=args.print_matrix, args=args) |
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print_metrics(cap_metrics) |
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save_metrics(cap_metrics, args.result_path, num_logs=len(logs)) |
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if args.task in ['grounding', 'all']: |
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print("====================== Grounding ======================") |
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grnd_metrics = grounding_metrics(logs) |
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print_metrics(grnd_metrics) |
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save_metrics(grnd_metrics, args.result_path, num_logs=len(logs)) |
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