[解决方案记录]No module named fused(stylegan2的bug，已更新)

最新推荐文章于 2025-10-23 00:02:37 发布

转载最新推荐文章于 2025-10-23 00:02:37 发布 · 3.2k 阅读

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原文链接：https://blog.youkuaiyun.com/bxdzyhx/article/details/115821518

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这篇博客介绍了在Windows环境下使用PyCharm或Jupyter Notebook运行StyleGAN2时遇到的C++编译问题。作者提供了一个无脑但有效的解决方案，即替换特定的C++模块为PyTorch实现。此外，还提到了确保CUDA版本至少为10.1以及添加cl.exe到环境变量中的重要性。博客提供了详细的代码示例和解决步骤。

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基本情况

https://github.com/rosinality/stylegan2-pytorch/issues/81

运行psp时出现的问题。其实就是stylegan2里面采用了c++编译等功能带来的bug，非常烦人。
系统：windows
平台：pycharm + jupyter notebook
GPU：GTX1660Ti

解决方案

(0)无脑但是有效：直接把fused_leakyrelu,upfirdn2d_native和FusedLeakyRelu替换为pytorch实现即可。

参考链接。

def upfirdn2d(input, kernel, up=1, down=1, pad=(0, 0)):
    # out = UpFirDn2d.apply(
    #     input, kernel, (up, up), (down, down), (pad[0], pad[1], pad[0], pad[1])
    # )
    out = upfirdn2d_native(input, kernel, up, up, down, down, pad[0], pad[1], pad[0], pad[1])
    return out

def upfirdn2d_native(
    input, kernel, up_x, up_y, down_x, down_y, pad_x0, pad_x1, pad_y0, pad_y1
):
    input = input.permute(0, 2, 3, 1)
    _, in_h, in_w, minor = input.shape
    kernel_h, kernel_w = kernel.shape
    out = input.view(-1, in_h, 1, in_w, 1, minor)
    out = F.pad(out, [0, 0, 0, up_x - 1, 0, 0, 0, up_y - 1])
    out = out.view(-1, in_h * up_y, in_w * up_x, minor)

    out = F.pad(
        out, [0, 0, max(pad_x0, 0), max(pad_x1, 0), max(pad_y0, 0), max(pad_y1, 0)]
    )
    out = out[
        :,
        max(-pad_y0, 0) : out.shape[1] - max(-pad_y1, 0),
        max(-pad_x0, 0) : out.shape[2] - max(-pad_x1, 0),
        :,
    ]

    out = out.permute(0, 3, 1, 2)
    out = out.reshape(
        [-1, 1, in_h * up_y + pad_y0 + pad_y1, in_w * up_x + pad_x0 + pad_x1]
    )
    w = torch.flip(kernel, [0, 1]).view(1, 1, kernel_h, kernel_w)
    out = F.conv2d(out, w)
    out = out.reshape(
        -1,
        minor,
        in_h * up_y + pad_y0 + pad_y1 - kernel_h + 1,
        in_w * up_x + pad_x0 + pad_x1 - kernel_w + 1,
    )
    # out = out.permute(0, 2, 3, 1)
    return out[:, :, ::down_y, ::down_x]

import torch
from torch import nn
import torch.nn.functional as F


class FusedLeakyReLU(nn.Module):
    def __init__(self, channel, negative_slope=0.2, scale=2 ** 0.5):
        super().__init__()

        self.bias = nn.Parameter(torch.zeros(channel))
        self.negative_slope = negative_slope
        self.scale = scale

    def forward(self, input):
        return fused_leaky_relu(input, self.bias, self.negative_slope, self.scale)


def fused_leaky_relu(input, bias, negative_slope=0.2, scale=2 ** 0.5):
    return scale * F.leaky_relu(input + bias.view((1, -1) + (1,) * (len(input.shape) - 2)),
                                negative_slope=negative_slope)