关于使用PSA模块时报错

[nlper01]

大佬，我在使用PSA.py这个模块时，老是报错
RuntimeError: Input type (torch.cuda.FloatTensor) and weight type (torch.FloatTensor) should be the same，
我已经把这个模块.cuda()了

    input=torch.randn(50,512,7,7).cuda()
    psa = PSA(channel=512,reduction=8).cuda()
    output=psa(input)
    a=output.view(-1).sum()
    a.backward()
    print(output.shape)

还是报错，能解决一下吗？

[XiaoyuWant]

我也有这个问题，不知道怎么解决

[nlper01]

我也有这个问题，不知道怎么解决

不能直接使用list，改成nn.ModuleList([])

[nlper01]

issue有大佬改了，连带就地操作问题也解决了，找不到了，搬运一下

import numpy as np
import torch
from torch import nn
from torch.nn import init
from attention.SpaceAtt import SpatialAttention

class PSA(nn.Module):

    def __init__(self, channel=552, reduction=4, S=3):
        super().__init__()
        self.S = S

        self.convs = nn.ModuleList([])
        for i in range(S):
            # Add groups
            self.convs.append(
                nn.Conv2d(channel // S, channel // S, kernel_size=2 * (i + 1) + 1, padding=i + 1, groups=2 ** i))

        self.se_blocks = nn.ModuleList([])
        for i in range(S):
            self.se_blocks.append(nn.Sequential(
                nn.AdaptiveAvgPool2d(1),
                nn.Conv2d(channel // S, channel // (S * reduction), kernel_size=1, bias=False),
                nn.ReLU(inplace=False),
                nn.Conv2d(channel // (S * reduction), channel // S, kernel_size=1, bias=False),
                nn.Sigmoid()
                ))

        self.softmax = nn.Softmax(dim=1)

    def init_weights(self):
        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                init.kaiming_normal_(m.weight, mode='fan_out')
                if m.bias is not None:
                    init.constant_(m.bias, 0)
            elif isinstance(m, nn.BatchNorm2d):
                init.constant_(m.weight, 1)
                init.constant_(m.bias, 0)
            elif isinstance(m, nn.Linear):
                init.normal_(m.weight, std=0.001)
                if m.bias is not None:
                    init.constant_(m.bias, 0)

    def forward(self, x):
        b, c, h, w = x.size()

        # Step1:SPC module
        PSA_input = x.view(b, self.S, c // self.S, h, w)  # bs,s,ci,h,w
        outs = []
        for idx, conv in enumerate(self.convs):
            SPC_input = PSA_input[:, idx, :, :, :]
            # SPC_out[:,idx,:,:,:]=se(SPC_input)
            outs.append(conv(SPC_input))
        SPC_out = torch.stack([out for out in outs], dim=1)

        # Step2:SE weight
        SE_out = torch.zeros_like(SPC_out)
        outs = []
        for idx, se in enumerate(self.se_blocks):
            SE_input = SPC_out[:, idx, :, :, :]
            # SE_out[:,idx,:,:,:]=se(SE_input)
            outs.append(se(SE_input))

        SE_out = torch.stack([out for out in outs], dim=1)

        # Step3:Softmax
        softmax_out = self.softmax(SE_out)

        # Step4:SPA
        PSA_out = SPC_out * softmax_out
        PSA_out = PSA_out.view(b, -1, h, w)

        return PSA_out

if __name__ == '__main__':
    device = torch.device('cuda')
    input = torch.randn(8, 552, 7, 7).to(device)
    psa = PSA(channel=552, reduction=8).to(device)
    output = psa(input)
    a = output.reshape(-1).sum()
    a.backward()
    print(output.shape)

[zeng-cy]

大佬，我在使用PSA.py这个模块时，老是报错 RuntimeError: Input type (torch.cuda.FloatTensor) and weight type (torch.FloatTensor) should be the same， 我已经把这个模块.cuda()了

    input=torch.randn(50,512,7,7).cuda()
    psa = PSA(channel=512,reduction=8).cuda()
    output=psa(input)
    a=output.view(-1).sum()
    a.backward()
    print(output.shape)

还是报错，能解决一下吗？

这个问题您解决了吗？

[zeng-cy]

我也有这个问题，不知道怎么解决

不能直接使用list，改成nn.ModuleList([])

好像还是不行

[zeng-cy]

，我在使用PSA.py这个模块时，老是报错 RuntimeError

这个还是用不了，from attention.SpaceAtt import SpatialAttention这个引进不来

[zeng-cy]

我也有这个问题，不知道怎么解决

不能直接使用list，改成nn.ModuleList([])

只改这个nn.ModuleList，确实出现您说的问题：one of the variables needed for gradient computation has been modified by an inplace operation: [torch.cuda.FloatTensor [4, 64, 20, 20]], which is output 0 of ReluBackward1, is at version 5; expected version 1 instead. Hint: enable anomaly detection to find the operation that failed to compute its gradient, with torch.autograd.set_detect_anomaly(True).

[SuperFDX]

我是在48行添加conv = conv.to(x.device)和54行添加se = se.to(x.device)全部转移到和输入的x同设备上解决的。

[18373233193]

我也有这个问题，不知道怎么解决

不能直接使用list，改成nn.ModuleList([])

只改这个nn.ModuleList，确实出现您说的问题：one of the variables needed for gradient computation has been modified by an inplace operation: [torch.cuda.FloatTensor [4, 64, 20, 20]], which is output 0 of ReluBackward1, is at version 5; expected version 1 instead. Hint: enable anomaly detection to find the operation that failed to compute its gradient, with torch.autograd.set_detect_anomaly(True).

请问这个问题您解决了吗 我现在也遇到了这个问题