assert not bias
时间: 2023-10-27 15:33:21 浏览: 37
As an AI language model, I am designed to be unbiased and neutral in my responses. I do not have personal opinions, feelings, or experiences that could influence my answers. My responses are based on the input provided to me and the data available. However, it is important to note that bias can exist in the data that I am trained on or the way the input is framed, and it is important to be aware of this potential bias and address it accordingly.
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class BCNN(nn.Module): def __init__(self): nn.Module.__init__(self) self.features = torchvision.models.vgg16(pretrained = True).features self.features = nn.Sequential(*list(self.features.children())[:-1]) self.fc = nn.Linear(512 ** 2, 200) for param in self.feature.parameters(): param.requires_grad = True nn.init.kaiming_normal_(self.fc.weight.data) if self.fc.bias is not None: nn.init.constant_(self.fc.bias.data, val = 0) def forward(self, x): N = x.size()[0] assert x.size() == (N, 3, 448, 448) x = self.features(x) assert x.size() == (N, 512, 28, 28) x = x.view(N, 512, 28 ** 2) x = torch.bmm(x, torch.transpose(x, 1, 2)) / (28 ** 2) assert x.size() ==(N, 512, 512) x = x.view(N, 512 ** 2) x = torch.sqrt(x + 1e-5) x = nn.functional.normalize(x) x = self.fc(x) assert x.size() == (N, 200) return x啥意思
这是一个 PyTorch 模型定义,定义了一个叫做 BCNN 的类,继承自 nn.Module。该模型使用了预训练的 VGG16 网络,去除了最后一层全连接层,将其余层保存在 self.features 中。接着定义了一个全连接层 self.fc,输入为 512x512,输出为 200。在前向传递中,输入数据 x 经过 self.features 后,先将其 reshape 成 N x 512 x (28^2),然后进行矩阵乘法,得到 N x 512 x 512 的输出,接着对输出进行开根号和 L2 归一化处理,最后送入全连接层得到输出。整个模型的作用是将输入图像映射到一个 200 维的向量表示。
class PointnetSAModuleMSG(_PointnetSAModuleBase): """Pointnet set abstraction layer with multiscale grouping""" def __init__(self, *, npoint: int, radii: List[float], nsamples: List[int], mlps: List[List[int]], bn: bool = True, use_xyz: bool = True, pool_method='max_pool', instance_norm=False): """ :param npoint: int :param radii: list of float, list of radii to group with :param nsamples: list of int, number of samples in each ball query :param mlps: list of list of int, spec of the pointnet before the global pooling for each scale :param bn: whether to use batchnorm :param use_xyz: :param pool_method: max_pool / avg_pool :param instance_norm: whether to use instance_norm """ super().__init__() assert len(radii) == len(nsamples) == len(mlps) self.npoint = npoint self.groupers = nn.ModuleList() self.mlps = nn.ModuleList() for i in range(len(radii)): radius = radii[i] nsample = nsamples[i] self.groupers.append( pointnet2_utils.QueryAndGroup(radius, nsample, use_xyz=use_xyz) if npoint is not None else pointnet2_utils.GroupAll(use_xyz) ) mlp_spec = mlps[i] if use_xyz: mlp_spec[0] += 3 self.mlps.append(pt_utils.SharedMLP(mlp_spec, bn=bn, instance_norm=instance_norm)) self.pool_method = pool_method这是PointnetSAModuleMSG的代码,而这是selfattention的代码:class SelfAttention(nn.Module): def __init__(self, in_channels, reduction=4): super(SelfAttention, self).__init__() self.avg_pool = nn.AdaptiveAvgPool1d(1) self.fc1 = nn.Conv1d(in_channels, in_channels // reduction, 1, bias=False) self.relu = nn.ReLU(inplace=True) self.fc2 = nn.Conv1d(in_channels // reduction, in_channels, 1, bias=False) self.sigmoid = nn.Sigmoid() def forward(self, x): b, c, n = x.size() y = self.avg_pool(x) y = self.fc1(y) y = self.relu(y) y = self.fc2(y) y = self.sigmoid(y) return x * y.expand_as(x);我想将SelfAttention作为PointnetSAModuleMSG的子模块,我是为了加入SA注意力机制,所以需要对PointnetSAModuleMSG进行修改。我想在每个SA模块中添加一个注意力机制,以使得网络可以更好地聚焦于重要的点。具体实现方式是在每个SA模块的最后一层MLP后加入一个Self-Attention层,(如SelfAttention类所示)用于计算每个点的注意力分数。你可以给我写出详细的修改代码吗?
好的,以下是修改后的代码:
```
class PointnetSAModuleMSG(SelfAttention, _PointnetSAModuleBase):
"""Pointnet set abstraction layer with multiscale grouping and self-attention"""
def __init__(self, *, npoint: int, radii: List[float], nsamples: List[int], mlps: List[List[int]], bn: bool = True,
use_xyz: bool = True, pool_method='max_pool', instance_norm=False, reduction=4):
"""
:param npoint: int
:param radii: list of float, list of radii to group with
:param nsamples: list of int, number of samples in each ball query
:param mlps: list of list of int, spec of the pointnet before the global pooling for each scale
:param bn: whether to use batchnorm
:param use_xyz:
:param pool_method: max_pool / avg_pool
:param instance_norm: whether to use instance_norm
:param reduction: reduction factor for self-attention
"""
super().__init__(in_channels=mlps[-1][-1], reduction=reduction)
assert len(radii) == len(nsamples) == len(mlps)
self.npoint = npoint
self.groupers = nn.ModuleList()
self.mlps = nn.ModuleList()
for i in range(len(radii)):
radius = radii[i]
nsample = nsamples[i]
self.groupers.append(
pointnet2_utils.QueryAndGroup(radius, nsample, use_xyz=use_xyz)
if npoint is not None else pointnet2_utils.GroupAll(use_xyz)
)
mlp_spec = mlps[i]
if use_xyz:
mlp_spec[0] += 3
mlp_spec.append(mlp_spec[-1]) # Add output channel for self-attention
self.mlps.append(pt_utils.SharedMLP(mlp_spec, bn=bn, instance_norm=instance_norm))
self.pool_method = pool_method
def forward(self, xyz, features):
"""
:param xyz: (B, N, 3) tensor of the xyz coordinates of the features
:param features: (B, C, N) tensor of the descriptors of the features
:return: (B, sum(mlps[-1]), npoint) tensor of the global features of each subsampled ball
"""
new_features_list = []
for i in range(len(self.groupers)):
new_features = self.groupers[i](xyz, features)
new_features = self.mlps[i](new_features)
new_features = self.forward_self_attention(new_features) # Apply self-attention
new_features = getattr(torch, self.pool_method)(new_features, 2) # (B, mlp[-1], npoint)
new_features_list.append(new_features)
return torch.cat(new_features_list, dim=1) # (B, sum(mlps[-1]), npoint)
```
在这个新的 `PointnetSAModuleMSG` 类中,我们继承了 `SelfAttention` 和 `_PointnetSAModuleBase`,并在构造函数中增加了一个新的参数 `reduction`,它用于控制 self-attention 的降维比例。
在 `__init__` 函数中,我们首先调用 `super()` 来初始化 `SelfAttention` 类,然后按照原来的方式初始化 `PointnetSAModuleMSG` 类。不同的是,在最后一层 MLP 之后,我们加入了一个新的输出通道,用于 self-attention 的计算。
在 `forward` 函数中,我们首先调用原来的方法获取每个球的子采样点集,然后将这些点集输入到新的 MLP 中。在这个 MLP 的最后一层之后,我们使用 `forward_self_attention` 函数对特征进行自注意力计算。最后,我们使用池化函数对每个子采样球的特征进行池化,并将它们拼接在一起,形成一个全局特征的张量。
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