223 lines
7.9 KiB
Python
223 lines
7.9 KiB
Python
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#!/usr/bin/env python3
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"""
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RT-DETR架构分析工具
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分析rtdetr-l.yaml和rtdetr-resnet50.yaml的网络结构和特征维度
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"""
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def analyze_rtdetr_l():
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"""分析RT-DETR-l (HGBlock) 架构"""
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print("=" * 80)
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print("🚀 RT-DETR-l (HGBlock架构) 网络分析")
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print("=" * 80)
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# 假设输入尺寸为640×640（典型RT-DETR输入）
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input_size = 640
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print(f"📊 输入尺寸: {input_size}×{input_size}×3")
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print()
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print("🏗️ Backbone架构分析:")
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print("-" * 50)
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# 特征尺寸跟踪
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current_size = input_size
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layers = [
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# [layer_idx, module, args, description]
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[0, "HGStem", "[32, 48]", "Stem层 - P2/4"],
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[1, "HGBlock×6", "[48, 128, 3]", "Stage 1 - 6个HGBlock"],
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[2, "DWConv", "[128, 3, 2, 1, False]", "下采样 - P3/8"],
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[3, "HGBlock×6", "[96, 512, 3]", "Stage 2 - 6个HGBlock"],
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[4, "DWConv", "[512, 3, 2, 1, False]", "下采样 - P4/16"],
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[5, "HGBlock×6", "[192, 1024, 5, True, False]", "Stage 3 - HGBlock 1"],
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[6, "HGBlock×6", "[192, 1024, 5, True, True]", "Stage 3 - HGBlock 2"],
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[7, "HGBlock×6", "[192, 1024, 5, True, True]", "Stage 3 - HGBlock 3"],
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[8, "DWConv", "[1024, 3, 2, 1, False]", "下采样 - P5/32"],
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[9, "HGBlock×6", "[384, 2048, 5, True, False]", "Stage 4 - 6个HGBlock"],
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]
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feature_maps = {}
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for i, (idx, module, args, desc) in enumerate(layers):
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print(f" [{idx}] {module}: {desc}")
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if "HGStem" in module:
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# Stem层：输入 -> 32 -> 48, 尺寸/4
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current_size = current_size // 4
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channels = 48
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print(f" ├── 输入: {input_size}×{input_size}×3")
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print(f" ├── 输出: {current_size}×{current_size}×{channels}")
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feature_maps[f"P2"] = (current_size, channels)
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elif "HGBlock" in module:
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# HGBlock保持尺寸和通道数
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args_list = args.replace("[", "").replace("]", "").split(", ")
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if len(args_list) >= 2:
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channels = int(args_list[1])
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print(f" ├── 尺寸: {current_size}×{current_size}×{channels}")
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elif "DWConv" in module:
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# 下采样：stride=2, 尺寸/2
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current_size = current_size // 2
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args_list = args.replace("[", "").replace("]", "").split(", ")
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channels = int(args_list[0])
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level = f"P{5 - (current_size // (input_size // 32)) + 1}" # 计算P级别
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if current_size == input_size // 8: level = "P3"
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elif current_size == input_size // 16: level = "P4"
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elif current_size == input_size // 32: level = "P5"
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print(f" ├── 输出: {current_size}×{current_size}×{channels}")
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feature_maps[level] = (current_size, channels)
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print()
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print("🎯 Backbone输出特征图:")
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for level, (size, ch) in feature_maps.items():
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print(f" {level}: {size}×{size}×{ch} = {size*size*ch:,} 参数")
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print()
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# Head分析
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print("🎯 Head架构分析 (FPN + Decoder):")
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print("-" * 50)
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# FPN特征流
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fpn_features = {
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"P3": feature_maps.get("P3", (80, 256)),
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"P4": feature_maps.get("P4", (40, 512)),
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"P5": feature_maps.get("P5", (20, 1024))
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}
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print("FPN特征流:")
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for level, (size, ch) in fpn_features.items():
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print(f" {level}: {size}×{size}×{ch}")
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print()
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print("最终检测输出:")
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print(f" RTDETRDecoder输入: P3({fpn_features['P3'][0]}×{fpn_features['P3'][0]}×{fpn_features['P3'][1]}) + " +
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f"P4({fpn_features['P4'][0]}×{fpn_features['P4'][0]}×{fpn_features['P4'][1]}) + " +
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f"P5({fpn_features['P5'][0]}×{fpn_features['P5'][0]}×{fpn_features['P5'][1]})")
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print(f" 输出: {80}×{80}×(4+nc) + {40}×{40}×(4+nc) + {20}×{20}×(4+nc) # 4=bbox, nc=80类")
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print()
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def analyze_rtdetr_resnet50():
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"""分析RT-DETR-ResNet50架构"""
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print("=" * 80)
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print("🏗️ RT-DETR-ResNet50 (传统架构) 网络分析")
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print("=" * 80)
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input_size = 640
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print(f"📊 输入尺寸: {input_size}×{input_size}×3")
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print()
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print("🏗️ Backbone架构分析:")
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print("-" * 50)
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current_size = input_size
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layers = [
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[0, "ResNetLayer", "[3, 64, 1, True, 1]", "Stem - C2"],
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[1, "ResNetLayer", "[64, 64, 1, False, 3]", "Stage 1 - C3"],
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[2, "ResNetLayer", "[256, 128, 2, False, 4]", "Stage 2 - C4"],
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[3, "ResNetLayer", "[512, 256, 2, False, 6]", "Stage 3 - C5"],
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[4, "ResNetLayer", "[1024, 512, 2, False, 3]", "Stage 4 - C6"],
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]
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feature_maps = {}
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channels = [64, 64, 128, 256, 512]
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for i, (idx, module, args, desc) in enumerate(layers):
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print(f" [{idx}] {module}: {desc}")
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args_list = args.replace("[", "").replace("]", "").split(", ")
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in_ch = int(args_list[0])
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out_ch = int(args_list[1])
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stride = int(args_list[2])
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if stride == 2:
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current_size = current_size // 2
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print(f" ├── 输入通道: {in_ch}, 输出通道: {out_ch}")
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print(f" ├── 步长: {stride}, 当前尺寸: {current_size}×{current_size}")
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# 记录输出特征图
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level = f"C{i+2}" if i < 5 else f"C{i+2}"
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feature_maps[level] = (current_size, out_ch)
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print()
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print("🎯 Backbone输出特征图:")
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for level, (size, ch) in feature_maps.items():
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print(f" {level}: {size}×{size}×{ch} = {size*size*ch:,} 参数")
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print()
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# Head分析
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print("🎯 Head架构分析 (FPN + Decoder):")
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print("-" * 50)
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# 从C3-C6映射到P3-P6，然后FPN生成P3-P5
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fpn_features = {
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"P3": (80, 256), # 从C3上采样得到
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"P4": (40, 256), # 从C4得到
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"P5": (20, 256) # 从C5下采样得到
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}
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print("FPN特征流:")
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for level, (size, ch) in fpn_features.items():
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print(f" {level}: {size}×{size}×{ch}")
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print()
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print("最终检测输出:")
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print(f" RTDETRDecoder输入: P3({fpn_features['P3'][0]}×{fpn_features['P3'][0]}×{fpn_features['P3'][1]}) + " +
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f"P4({fpn_features['P4'][0]}×{fpn_features['P4'][0]}×{fpn_features['P4'][1]}) + " +
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f"P5({fpn_features['P5'][0]}×{fpn_features['P5'][0]}×{fpn_features['P5'][1]})")
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print(f" 输出: {80}×{80}×(4+nc) + {40}×{40}×(4+nc) + {20}×{20}×(4+nc) # 4=bbox, nc=80类")
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print()
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def compare_architectures():
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"""对比两种架构"""
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print("=" * 80)
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print("🔄 架构对比分析")
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print("=" * 80)
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comparison = {
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"RT-DETR-l (HGBlock)": {
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"骨干网络": "HGBlock (Hybrid Guided)",
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"参数量级": "大 (多层HGBlock)",
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"创新点": "轻量级混合引导块",
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"优势": "参数效率高，性能好",
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"P3特征": "80×80×256",
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"P4特征": "40×40×512",
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"P5特征": "20×20×1024"
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},
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"RT-DETR-ResNet50": {
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"骨干网络": "ResNetLayer (传统)",
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"参数量级": "中等",
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"创新点": "标准ResNet架构",
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"优势": "稳定，易于训练",
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"P3特征": "80×80×256",
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"P4特征": "40×40×256",
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"P5特征": "20×20×256"
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}
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}
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print("📊 架构对比:")
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print("-" * 60)
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for model, specs in comparison.items():
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print(f"🏗️ {model}:")
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for key, value in specs.items():
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print(f" {key}: {value}")
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print()
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print("🎯 关键差异:")
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print("1. HGBlock架构使用渐进式通道增长: 48→128→512→1024→2048")
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print("2. ResNet50使用标准通道: 64→256→512→1024→2048")
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print("3. HGBlock在深层使用更大的卷积核 (k=5) 和残差连接")
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print("4. 两种架构的Head部分都是统一的FPN + RTDETRDecoder")
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print()
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if __name__ == "__main__":
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analyze_rtdetr_l()
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analyze_rtdetr_resnet50()
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compare_architectures()
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