scale = torch.sigmoid(channel_att_sum).unsqueeze(2).unsqueeze(3).unsqueeze(4).expand_as(x) IndexError: Dimension out of range (expected to be in range of [-2, 1], but got 2)。有什么解决方法吗
时间: 2023-06-09 13:08:48 浏览: 63
这个错误通常是由于从某个维度开始,unsqueeze()的参数数量超出了输入 tensor 的维度数所造成的。在你的代码中,从 3 个维度开始使用了 unsqueeze() 函数,因此需要确认输入张量的维度是多少。要解决这个错误,建议使用 .shape 属性来检查 tensor 的维度,并且使用正确的 unsqueeze 大小。或者,您也可以使用 PyTorch 的 view() 函数来改变 tensor 的形状,在这种情况下,您可能只需要用unsqueeze()来添加一些维度,并且在调用 expand_as()函数之前使用 view() 来重新调整形状。
相关问题
注意力机制self.space_attention = nn.Sequential( nn.Conv2d(512, 512, kernel_size=3, padding=1, bias=True), nn.BatchNorm2d(512), nn.ReLU(inplace=True) ) self.channel_attention = nn.Sequential( nn.Conv2d(512, 512, kernel_size=3, padding=1, bias=True), nn.BatchNorm2d(512), nn.ReLU(inplace=True) ) out_c1 = nn.AdaptiveMaxPool2d((1, 1))(x4_0) out_c1 = self.sigmoid(out_c1) channel_feature = x4_0 * out_c1 channel_att = self.channel_attention(channel_feature) x = x4_0 + channel_att out_s1 = torch.max(x, 1)[0].unsqueeze(1) out_s1 = self.sigmoid(out_s1) space_feature = x * out_s1 space_att = self.space_attention(space_feature) x = x + space_att 想在网络中换成self.attention = BiLevelRoutingAttention(dim=512, n_win=7, num_heads=8, qk_dim=None, qk_scale=None, kv_per_win=4, kv_downsample_ratio=4, kv_downsample_kernel=None, kv_downsample_mode='identity', topk=4, param_attention="qkvo", param_routing=False, diff_routing=False, soft_routing=False, side_dwconv=3, auto_pad=True)之后代码怎么写啊
如果您想在网络中使用`BiLevelRoutingAttention`,可以将原来的`self.space_attention`和`self.channel_attention`替换为`self.attention`,然后在`__init__`函数中初始化`self.attention`,并将其输入和输出的维度设置为512(因为原来的代码中维度为512)。代码示例如下:
```
from attention import BiLevelRoutingAttention
class YourNetwork(nn.Module):
def __init__(self):
super(YourNetwork, self).__init__()
self.attention = BiLevelRoutingAttention(dim=512, n_win=7, num_heads=8, qk_dim=None, qk_scale=None,
kv_per_win=4, kv_downsample_ratio=4, kv_downsample_kernel=None,
kv_downsample_mode='identity', topk=4, param_attention="qkvo",
param_routing=False, diff_routing=False, soft_routing=False,
side_dwconv=3, auto_pad=True)
# 其他层的初始化
...
def forward(self, x):
# 其他层的前向传播
...
out_c1 = nn.AdaptiveMaxPool2d((1, 1))(x4_0)
out_c1 = self.sigmoid(out_c1)
channel_feature = x4_0 * out_c1
channel_att = self.attention(channel_feature) # 使用BiLevelRoutingAttention
x = x4_0 + channel_att
out_s1 = torch.max(x, 1)[0].unsqueeze(1)
out_s1 = self.sigmoid(out_s1)
space_feature = x * out_s1
space_att = self.attention(space_feature) # 使用BiLevelRoutingAttention
x = x + space_att
# 其他层的后续处理
...
return x
```
注意,`BiLevelRoutingAttention`的输入和输出需要满足一定的维度要求,具体可参考`attention.py`文件中的实现。因此,如果您的输入和输出维度不符合要求,可能需要对其进行调整。
def forward(self, x1, x2): x1 = x1.to(torch.float32) x2 = x2.to(torch.float32) channel1_conv1 = self.channel1_conv1(x1).squeeze(dim=2) channel1_conv1 = torch.max(channel1_conv1, dim=1)[0].unsqueeze(dim=1) channel1_conv2 = self.channel1_conv2(x1).squeeze(dim=2) channel1_conv2 = torch.max(channel1_conv2, dim=1)[0].unsqueeze(dim=1) channel1_conv3 = self.channel1_conv3(x1).squeeze(dim=2) channel1_conv3 = torch.max(channel1_conv3, dim=1)[0].unsqueeze(dim=1) channel1_conv4 = self.channel1_conv4(x1).squeeze(dim=2) channel1_conv4 = torch.max(channel1_conv4, dim=1)[0].unsqueeze(dim=1) X1 = torch.cat([channel1_conv1, channel1_conv2, channel1_conv3, channel1_conv4], dim=1) channel2_conv1 = self.channel2_conv1(x2).squeeze(dim=2) channel2_conv1 = torch.max(channel2_conv1, dim=1)[0].unsqueeze(dim=1) channel2_conv2 = self.channel2_conv2(x2).squeeze(dim=2) channel2_conv2 = torch.max(channel2_conv2, dim=1)[0].unsqueeze(dim=1) channel2_conv3 = self.channel2_conv3(x2).squeeze(dim=2) channel2_conv3 = torch.max(channel2_conv3, dim=1)[0].unsqueeze(dim=1) channel2_conv4 = self.channel2_conv4(x2).squeeze(dim=2) channel2_conv4 = torch.max(channel2_conv4, dim=1)[0].unsqueeze(dim=1) X2 = torch.cat([channel2_conv1, channel2_conv2, channel2_conv3, channel2_conv4], dim=1) X2 = torch.cat([X1, X2], dim=1) output = self.layer3(X2)
这段代码是一个双通道的卷积神经网络的前向传播函数,输入是两个张量 x1 和 x2,经过一些卷积、池化和拼接操作后,最终输出一个张量 output。这个神经网络的具体结构是由两个通道的卷积层、池化层和全连接层构成的,其中 channel1_conv1-4 和 channel2_conv1-4 是卷积层,layer3 是全连接层。在前向传播过程中,先对两个通道的数据进行卷积和池化操作,然后将两个通道的结果拼接在一起,最后通过全连接层生成输出。需要注意的是,这个神经网络对输入数据进行了类型转换,将输入张量的数据类型转换为 float32。