79 lines
2.6 KiB
Python
79 lines
2.6 KiB
Python
#!/usr/bin/env python3
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import torch
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import torch.nn.functional as F
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def test_multi_scale_features():
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"""测试多尺度特征生成"""
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# 模拟输入
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batch_size = 1
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# 模拟bev_180_features: camera和lidar的180x180特征
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bev_180_features = [
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torch.randn(batch_size, 80, 180, 180), # camera特征
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torch.randn(batch_size, 256, 180, 180) # lidar特征 (假设有256通道)
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]
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# 模拟decoder输出: 360x360特征
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x = torch.randn(batch_size, 512, 360, 360) # 512通道
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print("输入尺寸:")
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print(f"bev_180_features[0] (camera): {bev_180_features[0].shape}")
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print(f"bev_180_features[1] (lidar): {bev_180_features[1].shape}")
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print(f"x (decoder output): {x.shape}")
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# 模拟_prepare_multi_scale_features逻辑
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# S3: 180x180 - 从fuser前的特征获取，采用重采样技术
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if bev_180_features is not None and len(bev_180_features) >= 1:
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s3 = bev_180_features[0] # (B, C, 180, 180) - 原始fuser前特征
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# 重采样技术：保持原始通道数，通过上采样到360x360来匹配其他尺度
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s3 = F.interpolate(s3, size=(360, 360), mode='bilinear', align_corners=False)
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else:
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# 回退：从360x360创建180x180再上采样
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s3_small = F.adaptive_avg_pool2d(x, (180, 180))
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s3 = F.interpolate(s3_small, size=(360, 360), mode='bilinear', align_corners=False)
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# S4: 360x360 - decoder输出，直接使用
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s4 = x # (B, C, 360, 360) - 原始decoder输出
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# S5: 600x600 - 从360x360上采样到600x600
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s5 = F.interpolate(x, size=(600, 600), mode='bilinear', align_corners=False)
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print("\n多尺度特征输出:")
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print(f"s3: {s3.shape}")
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print(f"s4: {s4.shape}")
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print(f"s5: {s5.shape}")
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# 模拟adaptive_proj处理
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C = 64 # Transformer内部维度
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# 模拟adaptive_proj
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proj_s3 = torch.nn.Conv2d(80, C, kernel_size=1)
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proj_s4 = torch.nn.Conv2d(512, C, kernel_size=1)
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proj_s5 = torch.nn.Conv2d(512, C, kernel_size=1)
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S3_ = proj_s3(s3)
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S4_ = proj_s4(s4)
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S5_ = proj_s5(s5)
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print("\n投影后特征:")
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print(f"S3_: {S3_.shape}")
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print(f"S4_: {S4_.shape}")
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print(f"S5_: {S5_.shape}")
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# 测试stack
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target_size = S4_.shape[2:] # (360, 360)
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S5_resized = F.interpolate(S5_, size=target_size, mode='bilinear', align_corners=False)
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print(f"\n调整后S5: {S5_resized.shape}")
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try:
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multi_scale_features = torch.stack([S3_, S4_, S5_resized], dim=1)
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print(f"Stack成功: {multi_scale_features.shape}")
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except Exception as e:
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print(f"Stack失败: {e}")
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if __name__ == "__main__":
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test_multi_scale_features()
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