131 lines
4.3 KiB
Python
131 lines
4.3 KiB
Python
#!/usr/bin/env python3
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"""
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简化测试TransformerSegmentationDecoder修复
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不依赖完整的mmdet3d框架
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"""
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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class TransformerSegmentationDecoder(nn.Module):
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"""
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简化的TransformerSegmentationDecoder用于测试
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"""
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def __init__(self, hidden_dim, nc=6, C=64, nhead=8, num_layers=2):
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super(TransformerSegmentationDecoder, self).__init__()
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self.C = C
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self.nc = nc
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# 自适应权重学习 - 修复后的单尺度权重
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self.task_weights = nn.Parameter(torch.ones(nc, 1)) # (类别数, 1个尺度)
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# 最终细化头
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self.refine = nn.Sequential(
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nn.Conv2d(C, C, kernel_size=3, padding=1),
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nn.BatchNorm2d(C),
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nn.ReLU(inplace=True),
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nn.Conv2d(C, 1, kernel_size=1)
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)
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def forward(self, x_single, imgs):
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# 投影到内部维度
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if x_single.shape[1] != self.C:
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single_proj = nn.Conv2d(x_single.shape[1], self.C, kernel_size=1).to(x_single.device)
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x_proj = single_proj(x_single)
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else:
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x_proj = x_single
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# Phase 4B: 修复后的单尺度处理
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task_features = x_proj.unsqueeze(1).expand(-1, self.nc, -1, -1, -1) # (B, nc, C, H, W)
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# 自适应权重计算
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task_weights = F.softmax(self.task_weights, dim=1).view(1, self.nc, 1, 1, 1)
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# 应用类别特定的权重
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task_features = task_features * task_weights # (B, nc, C, H, W)
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# 重塑为标准卷积格式并处理
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B, T, C, H, W = task_features.shape
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task_features = task_features.view(B * T, C, H, W)
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# 细化处理
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seg_logits = self.refine(task_features) # (B*nc, 1, H, W)
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# 调整到目标尺寸
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if H != imgs or W != imgs:
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seg_logits = F.interpolate(seg_logits, size=(imgs, imgs), mode='nearest')
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# 重塑回 (B, nc, H, W)
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seg_masks = seg_logits.view(B, T, 1, imgs, imgs).squeeze(2)
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return seg_masks, []
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def test_transformer_fix():
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"""测试修复后的Transformer解码器"""
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# 测试参数
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batch_size = 1
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num_classes = 6 # BEVFusion分割类别数
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C_in = 512 # 输入通道数
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C = 64 # 内部通道数
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H, W = 360, 360 # BEV尺寸
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target_size = 360
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print("🧪 测试修复后的TransformerSegmentationDecoder")
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print(f"输入尺寸: ({batch_size}, {C_in}, {H}, {W})")
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print(f"类别数: {num_classes}")
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print(f"目标尺寸: {target_size}")
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# 创建解码器
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decoder = TransformerSegmentationDecoder(
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hidden_dim=256,
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nc=num_classes,
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C=C
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)
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print(f"✅ 解码器创建成功")
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print(f" - nc: {decoder.nc} (应该为6)")
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print(f" - C: {decoder.C}")
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print(f" - task_weights.shape: {decoder.task_weights.shape} (应该为{num_classes}x1)")
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# 创建测试输入
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x_single = torch.randn(batch_size, C_in, H, W)
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print(f"输入特征: {x_single.shape}")
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# 前向传播
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try:
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seg_masks, aux_list = decoder(x_single, target_size)
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print(f"✅ 前向传播成功")
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print(f" - seg_masks.shape: {seg_masks.shape} (应该为{batch_size}x{num_classes}x{target_size}x{target_size})")
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# 验证输出范围
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print(f" - 输出范围: [{seg_masks.min():.3f}, {seg_masks.max():.3f}]")
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# 验证类别数
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assert seg_masks.shape[1] == num_classes, f"类别数错误: {seg_masks.shape[1]} != {num_classes}"
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assert decoder.task_weights.shape[0] == num_classes, f"权重维度错误: {decoder.task_weights.shape[0]} != {num_classes}"
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print("🎉 所有测试通过！")
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print("修复内容:")
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print(" ✅ 类别数从2改为6")
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print(" ✅ 移除假的多尺度特征")
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print(" ✅ 使用正确的单尺度权重")
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return True
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except Exception as e:
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print(f"❌ 前向传播失败: {e}")
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import traceback
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traceback.print_exc()
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return False
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if __name__ == "__main__":
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success = test_transformer_fix()
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if success:
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print("\n✅ Phase 4B Transformer解码器配置修复验证成功！")
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print("现在可以重新启动训练，应该能看到正确的分割性能。")
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else:
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print("\n❌ 测试失败，需要进一步调试")
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