Some doubts about restarting training

[T-wow]

def fuse_conv(conv, norm): fused_conv = torch.nn.Conv2d(conv.in_channels, conv.out_channels, kernel_size=conv.kernel_size, stride=conv.stride, padding=conv.padding, groups=conv.groups, bias=True).requiresgrad(False).to(conv.weight.device)

w_conv = conv.weight.clone().view(conv.out_channels, -1)
w_norm = torch.diag(norm.weight.div(torch.sqrt(norm.eps + norm.running_var)))
fused_conv.weight.copy_(torch.mm(w_norm, w_conv).view(fused_conv.weight.size()))

b_conv = torch.zeros(conv.weight.size(0), device=conv.weight.device) if conv.bias is None else conv.bias
b_norm = norm.bias - norm.weight.mul(norm.running_mean).div(torch.sqrt(norm.running_var + norm.eps))
fused_conv.bias.copy_(torch.mm(w_norm, b_conv.reshape(-1, 1)).reshape(-1) + b_norm)

return fused_conv

class Conv(torch.nn.Module):
def __init__(self, in_ch, out_ch, k=1, s=1, p=None, d=1, g=1):
    super().__init__()
    self.conv = torch.nn.Conv2d(in_ch, out_ch, k, s, pad(k, p, d), d, g, False)
    self.norm = torch.nn.BatchNorm2d(out_ch, 0.001, 0.03)
    self.relu = torch.nn.SiLU(inplace=True)

def forward(self, x):
    return self.relu(self.norm(self.conv(x)))

def fuse_forward(self, x):
    return self.relu(self.conv(x))

  class YOLO(torch.nn.Module):
def __init__(self, width, depth, pretrain_path):
    super().__init__()
    self.net = DarkNet(width, depth)
    self.fpn = DarkFPN(width, depth)

    self.pretrain_path = pretrain_path

def forward(self, x):
    x = self.net(x)
    return self.fpn(x)

def fuse(self):
    for m in self.modules():
        if type(m) is Conv and hasattr(m, 'norm'):
            m.conv = fuse_conv(m.conv, m.norm)
            m.forward = m.fuse_forward
            delattr(m, 'norm')
    return self

    Due to the mentioned def fuse_conv() and def fuse(self), if I restart training, since the Conv class in the file will be used, fusion operations as well as freezing of weights will take place. Wouldn't this mean that during the process of restarting training, the network would not get trained effectively?"
    Thanks

[Hope1337]

@T-wow Fusion batchnorm only occurs in inference stage which was removed (by some reason). You can recover the fusion process to speed up inference, it will not affects anything in training stage.

[T-wow]

@Hope1337 Thank you for your response. I have another question as well. In your program, is there a setting for freezing weights during training?

[Hope1337]

@T-wow yes, I added it few day ago. Look at new config file to see