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"""
从训练日志中提取loss数据，保存为JSONL格式。
2loss 实验说明：
- 去掉了 region loss
- context loss 权重调整为 0.25（和 DeCLIP 一致）
- 因此不需要进行归一化，直接对比
"""
import re
import json
import os
from pathlib import Path
# 配置
DECLIP_LOG = "/mnt/bn/strategy-mllm-train/user/wangjunjie/code/xiaomoguhzz/DeCLIP_private/logs/DeCLIP_EVA-B_DINOv2-B_560/out.log"
INTEGRATED_2LOSS_LOG = "/mnt/bn/strategy-mllm-train/user/wangjunjie/code/xiaomoguhzz/DeCLIP_private/logs/Integrated_EVA-B_DINOv2-B_560_2loss/out.log"
OUTPUT_DIR = "/mnt/bn/strategy-mllm-train/user/wangjunjie/code/xiaomoguhzz/DeCLIP_private/decoupling_analysis/2loss/results/data"
def parse_training_line(line: str) -> dict | None:
"""
解析训练日志行，提取loss数据。
格式示例:
2026-01-22,15:50:21 | INFO | Train Epoch: 0 [ 16/118287 (0%)] ... Loss_context: 2.2152 (2.2152) Loss_content: 0.55079 (0.55079) Loss: 2.7660 (2.7660)
"""
if "Train Epoch:" not in line:
return None
try:
# 提取时间戳
timestamp_match = re.match(r'(\d{4}-\d{2}-\d{2},\d{2}:\d{2}:\d{2})', line)
timestamp = timestamp_match.group(1) if timestamp_match else None
# 提取epoch
epoch_match = re.search(r'Train Epoch:\s*(\d+)', line)
epoch = int(epoch_match.group(1)) if epoch_match else None
# 提取当前step和总step
step_match = re.search(r'\[\s*(\d+)/(\d+)', line)
current_step = int(step_match.group(1)) if step_match else None
total_steps = int(step_match.group(2)) if step_match else None
# 提取LR
lr_match = re.search(r'LR:\s*([\d.e+-]+)', line)
lr = float(lr_match.group(1)) if lr_match else None
# 提取Loss_context (当前值)
context_match = re.search(r'Loss_context:\s*([\d.e+-]+)', line)
loss_context = float(context_match.group(1)) if context_match else None
# 提取Loss_content (当前值)
content_match = re.search(r'Loss_content:\s*([\d.e+-]+)', line)
loss_content = float(content_match.group(1)) if content_match else None
# 提取总Loss (当前值) - 注意匹配 "Loss:" 但不匹配 "Loss_xxx:"
total_match = re.search(r'Loss:\s*([\d.e+-]+)\s*\(', line)
loss_total = float(total_match.group(1)) if total_match else None
if all(v is not None for v in [epoch, loss_context, loss_content, loss_total]):
return {
"timestamp": timestamp,
"epoch": epoch,
"step": current_step,
"total_steps": total_steps,
"lr": lr,
"loss_context": loss_context,
"loss_content": loss_content,
"loss_total": loss_total
}
except Exception as e:
print(f"解析行失败: {line[:100]}... 错误: {e}")
return None
def parse_validation_line(line: str) -> dict | None:
"""
解析验证指标行。
"""
if "rois.thing.macc1" not in line:
return None
try:
# 提取时间戳
timestamp_match = re.match(r'(\d{4}-\d{2}-\d{2},\d{2}:\d{2}:\d{2})', line)
timestamp = timestamp_match.group(1) if timestamp_match else None
# 提取字典部分
dict_match = re.search(r'\{[^}]+\}', line)
if dict_match:
dict_str = dict_match.group(0).replace("'", '"')
metrics = json.loads(dict_str)
return {
"timestamp": timestamp,
"metrics": metrics
}
except Exception as e:
print(f"解析验证行失败: {line[:100]}... 错误: {e}")
return None
def extract_from_log(log_path: str, model_name: str) -> tuple[list, list]:
"""
从日志文件中提取训练loss和验证指标。
"""
training_data = []
validation_data = []
print(f"正在解析日志: {log_path}")
with open(log_path, 'r', encoding='utf-8') as f:
for line in f:
# 尝试解析训练行
train_record = parse_training_line(line)
if train_record:
train_record["model"] = model_name
training_data.append(train_record)
continue
# 尝试解析验证行
val_record = parse_validation_line(line)
if val_record:
val_record["model"] = model_name
validation_data.append(val_record)
print(f" - 提取到 {len(training_data)} 条训练记录")
print(f" - 提取到 {len(validation_data)} 条验证记录")
return training_data, validation_data
def save_jsonl(data: list, filepath: str):
"""保存数据为JSONL格式。"""
os.makedirs(os.path.dirname(filepath), exist_ok=True)
with open(filepath, 'w', encoding='utf-8') as f:
for record in data:
f.write(json.dumps(record, ensure_ascii=False) + '\n')
print(f"已保存: {filepath} ({len(data)} 条记录)")
def main():
print("=" * 60)
print("训练数据提取工具 (2loss 版本)")
print("=" * 60)
print("说明: 2loss 实验已将 context loss 权重调整为 0.25，无需归一化")
print()
# 提取DeCLIP数据
declip_train, declip_val = extract_from_log(DECLIP_LOG, model_name="DeCLIP")
# 提取Integrated_2loss数据
integrated_train, integrated_val = extract_from_log(
INTEGRATED_2LOSS_LOG,
model_name="Integrated_2loss"
)
print()
print("=" * 60)
print("保存数据")
print("=" * 60)
# 保存训练数据
save_jsonl(declip_train, os.path.join(OUTPUT_DIR, "declip_training.jsonl"))
save_jsonl(integrated_train, os.path.join(OUTPUT_DIR, "integrated_2loss_training.jsonl"))
# 保存验证数据
all_validation = declip_val + integrated_val
save_jsonl(all_validation, os.path.join(OUTPUT_DIR, "validation_metrics.jsonl"))
# 打印统计信息
print()
print("=" * 60)
print("数据统计")
print("=" * 60)
print("\n【DeCLIP训练数据】")
if declip_train:
epochs = set(r["epoch"] for r in declip_train)
print(f" Epoch范围: {min(epochs)} - {max(epochs)}")
print(f" 总记录数: {len(declip_train)}")
print(f" 最终Loss: context={declip_train[-1]['loss_context']:.4f}, "
f"content={declip_train[-1]['loss_content']:.4f}, "
f"total={declip_train[-1]['loss_total']:.4f}")
print("\n【Integrated_2loss训练数据】")
if integrated_train:
epochs = set(r["epoch"] for r in integrated_train)
print(f" Epoch范围: {min(epochs)} - {max(epochs)}")
print(f" 总记录数: {len(integrated_train)}")
print(f" 最终Loss: context={integrated_train[-1]['loss_context']:.4f}, "
f"content={integrated_train[-1]['loss_content']:.4f}, "
f"total={integrated_train[-1]['loss_total']:.4f}")
print("\n【验证指标】")
for val in all_validation:
print(f" {val['model']} @ {val['timestamp']}:")
m = val['metrics']
print(f" rois.thing.macc1: {m['rois.thing.macc1']:.4f}")
print(f" maskpool.thing.macc1: {m['maskpool.thing.macc1']:.4f}")
print()
print("=" * 60)
print("提取完成!")
print("=" * 60)
if __name__ == "__main__":
main()
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