150 lines
5.7 KiB
Python
150 lines
5.7 KiB
Python
#!/usr/bin/env python
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"""
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分析Transformer解码器debug信息：为什么会出现998×998尺寸？
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"""
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def analyze_transformer_debug():
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print("="*80)
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print("🔍 Transformer解码器Debug信息深度分析")
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print("="*80)
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# 1. 原始配置参数
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print("\n📊 1. 配置参数回顾")
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print("-" * 50)
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config_params = {
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'bev_input_size': 360, # BEVFusion输出BEV尺寸
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'multi_scales': [180, 360, 600], # _prepare_multi_scale_features生成的三尺度
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'target_image_size': 598, # _get_target_image_size返回的值
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'actual_debug_size': 998, # debug信息显示的实际尺寸
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'num_classes': 6
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}
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print("关键配置参数:")
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for key, value in config_params.items():
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print(f"├── {key}: {value}")
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# 2. 多尺度特征生成过程
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print("\n🔄 2. 多尺度特征生成过程")
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print("-" * 50)
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bev_input = config_params['bev_input_size']
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scales = config_params['multi_scales']
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print("基于360×360输入生成三尺度:")
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print(f"├── 尺度0 (180×180): {bev_input} × 0.5 = {int(bev_input * 0.5)}")
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print(f"├── 尺度1 (360×360): {bev_input} × 1.0 = {bev_input}")
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print(f"└── 尺度2 (600×600): {bev_input} × (600/360) = {int(bev_input * (600/360))}")
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print("\n实际生成的三尺度特征:")
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for i, scale in enumerate(scales):
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print(f"├── 尺度{i}: {scale}×{scale}×64ch (投影后)")
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# 3. Transformer解码器插值逻辑
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print("\n🎯 3. Transformer解码器插值逻辑")
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print("-" * 50)
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target_size = config_params['target_image_size'] # 598
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debug_size = config_params['actual_debug_size'] # 998
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print("插值目标尺寸分析:")
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print(f"├── _get_target_image_size() 返回: {target_size}")
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print(f"├── debug信息显示实际尺寸: {debug_size}")
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print(f"├── 差异: {debug_size - target_size} 像素")
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print("\n每个尺度的插值计算:")
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for i, scale in enumerate(scales):
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ratio = debug_size / scale
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print(f"├── 尺度{i} ({scale}×{scale}) → {debug_size}×{debug_size}")
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print(f"│ 插值倍数: {ratio:.3f}x")
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print(f"│ 原因: 将{scale}插值到{debug_size}以匹配最大尺度")
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# 4. 问题根源分析
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print("\n🔍 4. 问题根源分析")
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print("-" * 50)
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issues = [
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("代码逻辑错误", "Transformer解码器使用最大尺度作为目标尺寸，而不是_get_target_image_size()"),
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("多尺度融合策略", "所有尺度都被插值到最大尺度(600×600→998×998)，然后统一处理"),
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("插值计算偏差", "600 × (998÷600) = 600 × 1.663 = 998，出现了计算偏差"),
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("配置不一致", "_get_target_image_size返回598，但实际使用998")
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]
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print("发现的问题:")
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for i, (title, description) in enumerate(issues, 1):
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print(f"{i}. {title}:")
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print(f" {description}")
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# 5. 修正建议
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print("\n💡 5. 修正建议")
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print("-" * 50)
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fixes = [
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("统一目标尺寸", "确保Transformer解码器使用_get_target_image_size()返回的598作为目标尺寸"),
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("修复插值逻辑", "所有尺度都插值到598×598，而不是998×998"),
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("多尺度融合优化", "使用更合理的多尺度融合策略，避免过度插值"),
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("代码一致性", "保证_prepare_multi_scale_features和Transformer解码器的尺寸计算一致")
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]
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print("建议的修复方案:")
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for i, (title, description) in enumerate(fixes, 1):
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print(f"{i}. {title}:")
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print(f" {description}")
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# 6. 当前影响评估
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print("\n⚠️ 6. 当前影响评估")
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print("-" * 50)
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impacts = [
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("功能影响", "分割结果尺寸从998×998被裁剪/插值到598×598，造成信息损失"),
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("性能影响", "额外的插值计算增加推理时间"),
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("内存影响", "998×998的中间特征占用更多GPU内存"),
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("精度影响", "多尺度融合在错误尺寸下进行，可能影响分割精度")
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]
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print("当前实现的影响:")
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for i, (title, description) in enumerate(impacts, 1):
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print(f"{i}. {title}:")
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print(f" {description}")
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# 7. 正确的多尺度流程
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print("\n✅ 7. 正确的多尺度流程应该是")
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print("-" * 50)
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correct_flow = [
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("尺度生成", "[180×180, 360×360, 600×600] 三尺度特征"),
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("统一插值", "所有尺度插值到目标尺寸 598×598"),
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("多尺度融合", "在正确尺寸下进行类别特定的尺度融合"),
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("最终输出", "598×598×6 的分割结果")
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]
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print("正确的处理流程:")
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for i, (step, description) in enumerate(correct_flow, 1):
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print(f"{i}. {step}: {description}")
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print("\n对比当前实现:")
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print("├── 当前: 尺度2插值到998×998 (600×1.663)")
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print("├── 正确: 所有尺度插值到598×598")
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print(f"└── 差异: 998 - 598 = 400像素 的错误插值")
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# 8. 总结
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print("\n🎉 8. 总结")
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print("-" * 50)
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summary = [
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"debug信息显示998×998是因为Transformer解码器将尺度2(600×600)作为基准尺寸",
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"正确的目标尺寸应该是598×598，但代码中存在插值计算偏差",
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"这导致多尺度融合在错误尺寸下进行，影响最终分割精度",
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"需要修复Transformer解码器的插值逻辑，确保所有尺度都插值到598×598"
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]
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for item in summary:
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print(f"├── {item}")
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print("\n" + "="*80)
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print("🏁 Debug信息分析完成：998×998是插值计算偏差导致的！")
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print("="*80)
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if __name__ == '__main__':
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analyze_transformer_debug()
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