| 81 | |
| 82 | |
| 83 | class LoRAConv(nn.Module): |
| 84 | def __init__( |
| 85 | self, |
| 86 | in_channels: int, |
| 87 | out_channels: int, |
| 88 | kernel_size: Union[int, Tuple[int, int]], |
| 89 | stride: Union[int, Tuple[int, int]], |
| 90 | padding: Union[int, Tuple[int, int]], |
| 91 | rank: int = None, |
| 92 | lora_scale: float = 1.0, |
| 93 | ): |
| 94 | super().__init__() |
| 95 | |
| 96 | # self.lora_scale = alpha / rank |
| 97 | self.lora_scale = lora_scale |
| 98 | |
| 99 | self.W = nn.Conv2d(in_channels, out_channels, kernel_size, stride, padding) |
| 100 | for p in self.W.parameters(): |
| 101 | p.requires_grad_(False) |
| 102 | |
| 103 | self.A = nn.Conv2d(in_channels, rank, kernel_size, stride, padding, bias=False) |
| 104 | self.B = nn.Conv2d(rank, out_channels, kernel_size=1, stride=1, padding=0, bias=False) |
| 105 | |
| 106 | nn.init.zeros_(self.B.weight) |
| 107 | nn.init.kaiming_normal_(self.A.weight, a=1) |
| 108 | |
| 109 | def forward(self, x): |
| 110 | """ |
| 111 | Args: |
| 112 | x (torch.Tensor): In shape of (B, C, H, W) |
| 113 | """ |
| 114 | w_out = self.W(x) |
| 115 | a_out = self.A(x) |
| 116 | b_out = self.B(a_out) |
| 117 | |
| 118 | return w_out + b_out * self.lora_scale |
| 119 | |
| 120 | |
| 121 | class LoRAAdapterConv(nn.Module): |