From e6bd5af6a8e306a1cdef63402a77a980a04ad6e1 Mon Sep 17 00:00:00 2001
From: Grafting Rayman <156515434+GraftingRayman@users.noreply.github.com>
Date: Fri, 17 Jan 2025 11:06:44 +0000
Subject: Add files via upload

---
 .../detection/yolov5face/utils/torch_utils.py      | 40 ++++++++++++++++++++++
 1 file changed, 40 insertions(+)
 create mode 100644 r_facelib/detection/yolov5face/utils/torch_utils.py

(limited to 'r_facelib/detection/yolov5face/utils/torch_utils.py')

diff --git a/r_facelib/detection/yolov5face/utils/torch_utils.py b/r_facelib/detection/yolov5face/utils/torch_utils.py
new file mode 100644
index 0000000..f702962
--- /dev/null
+++ b/r_facelib/detection/yolov5face/utils/torch_utils.py
@@ -0,0 +1,40 @@
+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)
-- 
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