x = x.mean(dim = 1) if self.pool == 'mean' else x[:, 0]
时间: 2023-08-12 22:04:06 浏览: 77
这是一个Python中的三目运算符,用于简化条件语句的书写。在这个语句中,如果`self.pool`等于字符串`'mean'`,则`x`调用`mean`方法进行计算并返回沿着第二个维度求平均值后的结果,即`x.mean(dim=1)`;否则,取`x`的第一列数据,即`x[:, 0]`。最终返回的结果将赋值给变量`x`。
等价于下面的代码:
```
if self.pool == 'mean':
x = x.mean(dim=1)
else:
x = x[:, 0]
```
相关问题
class ChannelAttention(nn.Module): def __init__(self, in_planes, ratio=4): super(ChannelAttention, self).__init__() self.avg_pool = nn.AdaptiveAvgPool2d(1) self.max_pool = nn.AdaptiveMaxPool2d(1) # 利用1x1卷积代替全连接 self.fc1 = nn.Conv2d(in_planes, in_planes // ratio, 1, bias=False) self.relu1 = nn.ReLU() self.fc2 = nn.Conv2d(in_planes // ratio, in_planes, 1, bias=False) self.sigmoid = nn.Sigmoid() def forward(self, x): avg_out = self.fc2(self.relu1(self.fc1(self.avg_pool(x)))) max_out = self.fc2(self.relu1(self.fc1(self.max_pool(x)))) out = avg_out + max_out return self.sigmoid(out) class SpatialAttention(nn.Module): def __init__(self, kernel_size=7): super(SpatialAttention, self).__init__() assert kernel_size in (3, 7), 'kernel size must be 3 or 7' padding = 3 if kernel_size == 7 else 1 self.conv1 = nn.Conv2d(2, 1, kernel_size, padding=padding, bias=False) self.sigmoid = nn.Sigmoid() def forward(self, x): avg_out = torch.mean(x, dim=1, keepdim=True) max_out, _ = torch.max(x, dim=1, keepdim=True) x = torch.cat([avg_out, max_out], dim=1) x = self.conv1(x) return self.sigmoid(x)
这段代码定义了两个 PyTorch 模块:通道注意力模块(ChannelAttention)和空间注意力模块(SpatialAttention)。这两个模块都是用来增强卷积神经网络的表达能力的。
通道注意力模块的输入是一个四维张量 x,其形状为 [batch_size, in_planes, height, width],其中 in_planes 是输入特征图的通道数。在该模块中,首先对输入特征图进行了平均池化和最大池化操作,然后将两个池化结果分别经过一个 1x1 卷积层和激活函数,并将它们相加得到最终的特征图。最后,通过一个 sigmoid 激活函数将特征图的像素值归一化到 [0,1] 范围内。
空间注意力模块的输入也是一个四维张量 x,其形状与通道注意力模块相同。在该模块中,通过对输入特征图进行平均池化和最大池化操作,并将两个池化结果拼接在一起,得到了一个二通道的特征图。然后,将该特征图经过一个卷积层和 sigmoid 激活函数,得到一个 [0,1] 范围内的权重图。最后,将输入特征图与权重图相乘,得到了加强了空间细节的特征图。
这两个模块可以组合起来使用,以增强卷积神经网络的表达能力。
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我想将以上模块输出的张量的shape在模块尾部打印出来方便检查,你可以提供详细的代码吗?
当一个模块被调用时,可以使用`print()`函数将张量的shape打印出来。在这个例子中,我们可以在`forward()`函数中添加一个打印语句来打印输出的张量的shape。
以下是修改后的代码:
```python
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
def forward(self, xyz, features=None):
"""
:param xyz: (B, N, 3) xyz coordinates of the features
:param features: (B, N, C) descriptors of the features
:return: (B, npoint, \sum_k(mlps[k][-1])) tensor of the global features for the set
"""
# Set Abstraction
xyz_flipped = xyz.transpose(1, 2).contiguous()
new_xyz = None
if self.npoint is not None:
fps_idx = pointnet2_utils.furthest_point_sample(xyz, self.npoint) # (B, npoint)
new_xyz = pointnet2_utils.gather_operation(xyz_flipped, fps_idx).transpose(1, 2).contiguous() if xyz is not None else None # (B, 3, npoint)
else:
new_xyz = None
# Grouping
new_features_list = []
for i in range(len(self.groupers)):
grouper = self.groupers[i]
new_features = grouper(new_xyz, xyz, features) # (B, C, npoint, nsample)
new_features = self.mlps[i](new_features) # (B, mlp[-1], npoint)
if self.pool_method == 'max_pool':
new_features = new_features.max(dim=-1)[0] # (B, mlp[-1])
elif self.pool_method == 'avg_pool':
new_features = new_features.mean(dim=-1) # (B, mlp[-1])
new_features_list.append(new_features)
return torch.cat(new_features_list, dim=1)
```
现在,当你调用这个模块并传入输入张量时,你将在控制台中看到shape的输出。
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