#!/usr/bin/env python3 """ 论文用简化图表 - 2张图讲清楚结论 图1: Total Loss 对比 (DeCLIP vs Integrated) 图2: 梯度冲突分析 (1×2: 分布 + 逐层趋势) """ import json import os import numpy as np import matplotlib.pyplot as plt import matplotlib.patches as mpatches from collections import defaultdict # 配置 BASE_DIR = "/mnt/bn/strategy-mllm-train/user/wangjunjie/code/xiaomoguhzz/DeCLIP_private" DECLIP_LOG = f"{BASE_DIR}/logs/DeCLIP_EVA-B_DINOv2-B_560/out.log" INTEGRATED_2LOSS_LOG = f"{BASE_DIR}/logs/Integrated_EVA-B_DINOv2-B_560_2loss/out.log" GRADIENT_2LOSS_PATH = f"{BASE_DIR}/logs/Integrated_EVA-B_DINOv2-B_560_grad_analysis_2loss/gradient_analysis.jsonl" OUTPUT_DIR = f"{BASE_DIR}/decoupling_analysis/2loss/results/paper_figures" SMOOTHING_WINDOW = 20 # 颜色 COLOR_DECLIP = '#2E86AB' # 蓝色 COLOR_INTEGRATED = '#E94F37' # 红色 COLOR_CONFLICT = '#E94F37' # 红色 COLOR_ALIGNED = '#2E86AB' # 蓝色 def setup_plot_style(): """设置论文级别绘图风格""" plt.rcParams.update({ 'font.family': 'serif', 'font.serif': ['Times New Roman', 'DejaVu Serif'], 'font.size': 12, 'axes.labelsize': 14, 'axes.titlesize': 15, 'axes.titleweight': 'bold', 'xtick.labelsize': 12, 'ytick.labelsize': 12, 'legend.fontsize': 12, 'figure.dpi': 150, 'savefig.dpi': 300, 'savefig.bbox': 'tight', 'savefig.pad_inches': 0.1, 'axes.linewidth': 1.5, 'axes.spines.top': False, 'axes.spines.right': False, 'lines.linewidth': 2.5, }) def parse_training_log(log_path: str) -> list: """解析训练日志""" import re records = [] with open(log_path, 'r', encoding='utf-8') as f: for line in f: if "Train Epoch:" not in line: continue try: total_match = re.search(r'Loss:\s*([\d.e+-]+)\s*\(', line) if total_match: records.append(float(total_match.group(1))) except: continue return records def load_gradient_data(filepath: str) -> dict: """加载梯度分析数据""" layer_data = defaultdict(list) with open(filepath, 'r', encoding='utf-8') as f: for line in f: record = json.loads(line.strip()) for layer_name, cos_sim in record.get('layer_cos_sims', {}).items(): layer_data[layer_name].append(cos_sim) return dict(layer_data) def smooth_data(data: np.ndarray, window: int) -> np.ndarray: """滑动平均""" if window <= 1 or len(data) < window: 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_loss_comparison(declip_loss, integrated_loss, output_path): """ 图1: Total Loss 对比 简洁版：只画 Total Loss，突出差距 """ min_len = min(len(declip_loss), len(integrated_loss)) declip_loss = np.array(declip_loss[:min_len]) integrated_loss = np.array(integrated_loss[:min_len]) iters = np.arange(min_len) fig, ax = plt.subplots(figsize=(8, 5)) # 绘制曲线 ax.plot(iters, declip_loss, color=COLOR_DECLIP, alpha=0.15, linewidth=1.0) ax.plot(iters, smooth_data(declip_loss, SMOOTHING_WINDOW), color=COLOR_DECLIP, linewidth=3, label='DeCLIP (Decoupled)') ax.plot(iters, integrated_loss, color=COLOR_INTEGRATED, alpha=0.15, linewidth=1.0) ax.plot(iters, smooth_data(integrated_loss, SMOOTHING_WINDOW), color=COLOR_INTEGRATED, linewidth=3, label='Integrated') # 标注最终值 final_declip = declip_loss[-1] final_integrated = integrated_loss[-1] # 添加水平虚线和数值标注 ax.axhline(y=final_declip, color=COLOR_DECLIP, linestyle='--', alpha=0.5, linewidth=1.5) ax.axhline(y=final_integrated, color=COLOR_INTEGRATED, linestyle='--', alpha=0.5, linewidth=1.5) ax.text(min_len * 1.02, final_declip, f'{final_declip:.2f}', color=COLOR_DECLIP, fontsize=14, fontweight='bold', va='center') ax.text(min_len * 1.02, final_integrated, f'{final_integrated:.2f}', color=COLOR_INTEGRATED, fontsize=14, fontweight='bold', va='center') # 添加差距标注 ratio = final_integrated / final_declip mid_y = (final_declip + final_integrated) / 2 ax.annotate('', xy=(min_len * 0.95, final_declip), xytext=(min_len * 0.95, final_integrated), arrowprops=dict(arrowstyle='<->', color='gray', lw=2)) ax.text(min_len * 0.92, mid_y, f'{ratio:.1f}×', fontsize=13, color='gray', fontweight='bold', ha='right', va='center') ax.set_xlabel('Iteration') ax.set_ylabel('Total Loss') ax.set_title('Decoupled Distillation Converges Better') ax.legend(loc='upper right', framealpha=0.95) ax.set_xlim(0, min_len * 1.1) ax.set_ylim(0, max(integrated_loss) * 1.1) ax.grid(True, alpha=0.3, linestyle='-', linewidth=0.5) plt.tight_layout() plt.savefig(output_path.replace('.png', '.png')) plt.savefig(output_path.replace('.png', '.pdf')) plt.close() print(f"已保存: {output_path}") def plot_gradient_analysis(layer_data, output_path): """ 图2: 梯度冲突分析 (1×2) 左: 分布直方图 右: 逐层趋势 """ # 收集所有数据 all_values = [] for cos_sims in layer_data.values(): all_values.extend(cos_sims) all_array = np.array(all_values) # 计算统计 conflict_ratio = np.mean(all_array < 0) * 100 mean_cos = np.mean(all_array) # 逐层统计 layer_names = sorted(layer_data.keys(), key=lambda x: int(x.split('_')[1])) layer_means = [np.mean(layer_data[name]) for name in layer_names] layer_stds = [np.std(layer_data[name]) for name in layer_names] layer_indices = [int(name.split('_')[1]) for name in layer_names] fig, axes = plt.subplots(1, 2, figsize=(12, 5)) # ===== 左图: 分布直方图 ===== ax1 = axes[0] bins = np.linspace(-0.5, 0.5, 31) n, bins_edges, patches = ax1.hist(all_array, bins=bins, edgecolor='white', linewidth=0.8, alpha=0.9) # 根据值着色 for i, (patch, left_edge) in enumerate(zip(patches, bins_edges[:-1])): right_edge = bins_edges[i + 1] center = (left_edge + right_edge) / 2 if center < 0: patch.set_facecolor(COLOR_CONFLICT) else: patch.set_facecolor(COLOR_ALIGNED) # 添加零线 ax1.axvline(x=0, color='black', linestyle='-', linewidth=2) # 添加统计标注框 textstr = f'Conflict: {conflict_ratio:.0f}%\nMean: {mean_cos:.3f}' props = dict(boxstyle='round,pad=0.5', facecolor='white', alpha=0.95, edgecolor='gray') ax1.text(0.05, 0.95, textstr, transform=ax1.transAxes, fontsize=13, verticalalignment='top', bbox=props, fontweight='bold') # 图例 legend_elements = [ mpatches.Patch(facecolor=COLOR_CONFLICT, label='Conflict (cos < 0)'), mpatches.Patch(facecolor=COLOR_ALIGNED, label='Aligned (cos > 0)') ] ax1.legend(handles=legend_elements, loc='upper right', framealpha=0.95) ax1.set_xlabel('Gradient Cosine Similarity') ax1.set_ylabel('Frequency') ax1.set_title('(a) Distribution of Gradient Similarity') ax1.set_xlim(-0.55, 0.55) # ===== 右图: 逐层趋势 ===== ax2 = axes[1] # 绘制柱状图 colors = [COLOR_CONFLICT if m < 0 else COLOR_ALIGNED for m in layer_means] bars = ax2.bar(layer_indices, layer_means, color=colors, alpha=0.85, edgecolor='white', linewidth=1) ax2.errorbar(layer_indices, layer_means, yerr=layer_stds, fmt='none', color='black', capsize=4, capthick=1.5, elinewidth=1.5) # 零线 ax2.axhline(y=0, color='black', linestyle='-', linewidth=1.5) # 添加趋势箭头和标注 ax2.annotate('', xy=(11, layer_means[-1] - 0.05), xytext=(0, layer_means[0] + 0.05), arrowprops=dict(arrowstyle='->', color='gray', lw=2, connectionstyle='arc3,rad=-0.2')) ax2.text(5.5, -0.38, 'Deeper = More Conflict', fontsize=11, color='gray', ha='center', style='italic') ax2.set_xlabel('Layer Index') ax2.set_ylabel('Mean Cosine Similarity') ax2.set_title('(b) Conflict Increases in Deeper Layers') ax2.set_xticks(layer_indices) ax2.set_ylim(-0.45, 0.15) ax2.grid(True, alpha=0.3, axis='y', linestyle='-', linewidth=0.5) plt.tight_layout() plt.savefig(output_path) plt.savefig(output_path.replace('.png', '.pdf')) plt.close() print(f"已保存: {output_path}") def main(): setup_plot_style() os.makedirs(OUTPUT_DIR, exist_ok=True) print("=" * 60) print("生成论文用图表") print("=" * 60) # 加载数据 print("\n加载数据...") declip_loss = parse_training_log(DECLIP_LOG) integrated_loss = parse_training_log(INTEGRATED_2LOSS_LOG) layer_data = load_gradient_data(GRADIENT_2LOSS_PATH) print(f" DeCLIP: {len(declip_loss)} 条记录") print(f" Integrated: {len(integrated_loss)} 条记录") print(f" 梯度数据: {len(layer_data)} 层") # 生成图表 print("\n生成图表...") # 图1: Loss 对比 plot_loss_comparison( declip_loss, integrated_loss, os.path.join(OUTPUT_DIR, 'fig1_loss_comparison.png') ) # 图2: 梯度冲突分析 plot_gradient_analysis( layer_data, os.path.join(OUTPUT_DIR, 'fig2_gradient_analysis.png') ) print("\n" + "=" * 60) print("完成！图表保存在:") print(f" {OUTPUT_DIR}/") print("=" * 60) if __name__ == "__main__": main()