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diff --git a/r_facelib/detection/yolov5face/utils/torch_utils.py b/r_facelib/detection/yolov5face/utils/torch_utils.py
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index 0000000..f702962
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+++ b/r_facelib/detection/yolov5face/utils/torch_utils.py
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+import torch

+from torch import nn

+

+

+def fuse_conv_and_bn(conv, bn):

+    # Fuse convolution and batchnorm layers https://tehnokv.com/posts/fusing-batchnorm-and-conv/

+    fusedconv = (

+        nn.Conv2d(

+            conv.in_channels,

+            conv.out_channels,

+            kernel_size=conv.kernel_size,

+            stride=conv.stride,

+            padding=conv.padding,

+            groups=conv.groups,

+            bias=True,

+        )

+        .requires_grad_(False)

+        .to(conv.weight.device)

+    )

+

+    # prepare filters

+    w_conv = conv.weight.clone().view(conv.out_channels, -1)

+    w_bn = torch.diag(bn.weight.div(torch.sqrt(bn.eps + bn.running_var)))

+    fusedconv.weight.copy_(torch.mm(w_bn, w_conv).view(fusedconv.weight.size()))

+

+    # prepare spatial bias

+    b_conv = torch.zeros(conv.weight.size(0), device=conv.weight.device) if conv.bias is None else conv.bias

+    b_bn = bn.bias - bn.weight.mul(bn.running_mean).div(torch.sqrt(bn.running_var + bn.eps))

+    fusedconv.bias.copy_(torch.mm(w_bn, b_conv.reshape(-1, 1)).reshape(-1) + b_bn)

+

+    return fusedconv

+

+

+def copy_attr(a, b, include=(), exclude=()):

+    # Copy attributes from b to a, options to only include [...] and to exclude [...]

+    for k, v in b.__dict__.items():

+        if (include and k not in include) or k.startswith("_") or k in exclude:

+            continue

+

+        setattr(a, k, v)