analysis/decoupling_analysis/2loss/plot_loss_curves.py

#!/usr/bin/env python3
"""
绘制 2loss 实验的 Loss 曲线对比图。

对比: DeCLIP vs Integrated_2loss
"""

import json
import os
import numpy as np
import matplotlib.pyplot as plt
from pathlib import Path


# 配置
DATA_DIR = "/mnt/bn/strategy-mllm-train/user/wangjunjie/code/xiaomoguhzz/DeCLIP_private/decoupling_analysis/2loss/results/data"
OUTPUT_DIR = "/mnt/bn/strategy-mllm-train/user/wangjunjie/code/xiaomoguhzz/DeCLIP_private/decoupling_analysis/2loss/results"

# 绘图配置
SMOOTHING_WINDOW = 15
COLORS = {
    'DeCLIP': '#2E86AB',           # 蓝色
    'Integrated_2loss': '#E94F37', # 红色
}


def setup_plot_style():
    """设置顶会论文级别的绘图风格。"""
    plt.style.use('seaborn-v0_8-whitegrid')
    plt.rcParams.update({
        'font.family': 'serif',
        'font.serif': ['Times New Roman', 'DejaVu Serif'],
        'font.size': 11,
        'axes.labelsize': 13,
        'axes.titlesize': 14,
        'axes.titleweight': 'bold',
        'xtick.labelsize': 11,
        'ytick.labelsize': 11,
        'legend.fontsize': 11,
        'figure.dpi': 150,
        'savefig.dpi': 300,
        'savefig.bbox': 'tight',
        'savefig.pad_inches': 0.1,
        'axes.linewidth': 1.2,
        'grid.linewidth': 0.8,
        'grid.alpha': 0.3,
        'lines.linewidth': 2.0,
    })


def load_jsonl(filepath: str) -> list:
    """加载JSONL文件。"""
    data = []
    with open(filepath, 'r', encoding='utf-8') as f:
        for line in f:
            data.append(json.loads(line.strip()))
    return data


def smooth_data(data: np.ndarray, window: int) -> np.ndarray:
    """使用滑动平均平滑数据。"""
    if window <= 1:
        return data
    kernel = np.ones(window) / window
    smoothed = np.convolve(data, kernel, mode='same')
    for i in range(window // 2):
        smoothed[i] = np.mean(data[:i + window // 2 + 1])
        smoothed[-(i + 1)] = np.mean(data[-(i + window // 2 + 1):])
    return smoothed


def plot_paper_figure(
    iters: np.ndarray,
    declip_data: dict,
    integrated_data: dict,
    save_path_png: str,
    save_path_pdf: str,
    smoothing_window: int = SMOOTHING_WINDOW
):
    """绘制1x3论文用图。"""
    fig, axes = plt.subplots(1, 3, figsize=(15, 4.5))
    
    plot_configs = [
        (axes[0], 'loss_total', '(a) Total Loss'),
        (axes[1], 'loss_content', '(b) Content Loss'),
        (axes[2], 'loss_context', '(c) Context Loss'),
    ]
    
    for ax, key, title in plot_configs:
        for model_name, data, color in [
            ('DeCLIP', declip_data, COLORS['DeCLIP']),
            ('Integrated (2loss)', integrated_data, COLORS['Integrated_2loss'])
        ]:
            y_data = np.array(data[key])
            ax.plot(iters, y_data, color=color, alpha=0.2, linewidth=1.0)
            y_smooth = smooth_data(y_data, smoothing_window)
            ax.plot(iters, y_smooth, color=color, linewidth=2.5, label=model_name)
        
        ax.set_xlabel('Iteration')
        ax.set_ylabel('Loss')
        ax.set_title(title, fontweight='bold')
        ax.legend(loc='upper right', framealpha=0.9)
        ax.grid(True, alpha=0.3)
        
        for spine in ax.spines.values():
            spine.set_linewidth(1.2)
    
    plt.tight_layout()
    plt.savefig(save_path_png)
    plt.savefig(save_path_pdf)
    plt.close()
    print(f"已保存: {save_path_png}")
    print(f"已保存: {save_path_pdf}")


def compute_statistics(declip_data: dict, integrated_data: dict) -> dict:
    """计算统计数据。"""
    stats = {}
    
    for model_name, data in [('DeCLIP', declip_data), ('Integrated_2loss', integrated_data)]:
        stats[model_name] = {}
        for key in ['loss_total', 'loss_content', 'loss_context']:
            values = np.array(data[key])
            stats[model_name][key] = {
                'mean': float(np.mean(values)),
                'std': float(np.std(values)),
                'min': float(np.min(values)),
                'max': float(np.max(values)),
                'final': float(values[-1]),
                'first': float(values[0]),
            }
    
    return stats


def main():
    setup_plot_style()
    os.makedirs(OUTPUT_DIR, exist_ok=True)
    
    print("=" * 60)
    print("Loss曲线绘图工具 (2loss 版本)")
    print("=" * 60)
    
    # 加载数据
    declip_path = os.path.join(DATA_DIR, "declip_training.jsonl")
    integrated_path = os.path.join(DATA_DIR, "integrated_2loss_training.jsonl")
    
    if not os.path.exists(declip_path) or not os.path.exists(integrated_path):
        print("错误: 数据文件不存在，请先运行 extract_training_data.py")
        return
    
    print("加载数据...")
    declip_records = load_jsonl(declip_path)
    integrated_records = load_jsonl(integrated_path)
    
    print(f"  DeCLIP: {len(declip_records)} 条记录")
    print(f"  Integrated_2loss: {len(integrated_records)} 条记录")
    
    # 截断到相同长度
    min_len = min(len(declip_records), len(integrated_records))
    declip_records = declip_records[:min_len]
    integrated_records = integrated_records[:min_len]
    print(f"  截断后: {min_len} 条记录")
    
    # 转换为numpy数组格式
    iters = np.arange(min_len)
    
    declip_data = {
        'loss_total': [r['loss_total'] for r in declip_records],
        'loss_content': [r['loss_content'] for r in declip_records],
        'loss_context': [r['loss_context'] for r in declip_records],
    }
    
    integrated_data = {
        'loss_total': [r['loss_total'] for r in integrated_records],
        'loss_content': [r['loss_content'] for r in integrated_records],
        'loss_context': [r['loss_context'] for r in integrated_records],
    }
    
    print()
    print("=" * 60)
    print("生成图表")
    print("=" * 60)
    
    # 论文用图 (1x3)
    plot_paper_figure(
        iters, declip_data, integrated_data,
        os.path.join(OUTPUT_DIR, 'loss_comparison_2loss_paper.png'),
        os.path.join(OUTPUT_DIR, 'loss_comparison_2loss_paper.pdf')
    )
    
    # 保存统计数据
    stats = compute_statistics(declip_data, integrated_data)
    stats_path = os.path.join(OUTPUT_DIR, 'statistics.json')
    with open(stats_path, 'w', encoding='utf-8') as f:
        json.dump(stats, f, indent=2, ensure_ascii=False)
    print(f"已保存: {stats_path}")
    
    print()
    print("=" * 60)
    print("统计摘要")
    print("=" * 60)
    
    for model in ['DeCLIP', 'Integrated_2loss']:
        print(f"\n【{model}】")
        for loss_type in ['loss_total', 'loss_content', 'loss_context']:
            s = stats[model][loss_type]
            print(f"  {loss_type}: first={s['first']:.4f}, final={s['final']:.4f}, "
                  f"mean={s['mean']:.4f}, min={s['min']:.4f}")
    
    print()
    print("=" * 60)
    print("绘图完成!")
    print("=" * 60)


if __name__ == "__main__":
    main()