pytorch expansion = 1啥意思
时间: 2023-10-03 15:03:14 浏览: 51
在PyTorch中,`expansion`参数用于指定张量的扩展倍数。当我们想要复制一个张量多次来扩展它的维度时,可以使用`torch.expansion()`函数。
具体而言,`expansion`参数接受一个整数值,表示要扩展的倍数。例如,如果有一个形状为`(3, 1)`的张量,我们可以使用`torch.expansion(input, size)`来将其扩展为`(3, 4)`,其中`size`为`(3, 4)`。
下面是一个简单的示例代码:
```python
import torch
x = torch.tensor([[1], [2], [3]])
expanded_x = torch.expansion(x, (3, 4))
print(expanded_x.shape) # 输出 (3, 4)
print(expanded_x)
```
上述代码中,`x`是一个形状为`(3, 1)`的张量,通过`torch.expansion(x, (3, 4))`将其扩展为`(3, 4)`的张量`expanded_x`。
相关问题
class BasicBlock(nn.Module): expansion = 1 def __init__(self, in_channels, out_channels, stride=1, downsample=None): super(BasicBlock, self).__init__() self.conv1 = nn.Conv2d(in_channels=in_channels, out_channels=out_channels , kernel_size=3, stride=stride, padding=1, bias=False) self.bn1 = nn.BatchNorm2d(out_channels) self.relu = nn.ReLU() self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1, bias=False) self.bn2 = nn.BatchNorm2d(out_channels) self.downsample = downsample
这是一个PyTorch中定义ResNet的BasicBlock的代码。BasicBlock是ResNet中的基本残差块,包含两个卷积层和一个跳跃连接。参数in_channels和out_channels分别表示输入通道数和输出通道数。stride表示卷积核的步长,downsample表示是否需要对输入进行下采样。
在BasicBlock的构造函数中,首先调用父类的构造函数,然后定义了两个卷积层。其中,第一个卷积层使用3×3的卷积核,stride为stride,padding为1,不使用偏置项;第二个卷积层与第一个卷积层类似,但stride为1。在卷积层之间,使用BatchNorm2d和ReLU激活函数。最后,如果需要进行下采样,则使用downsample对输入进行下采样。
此外,expansion=1表示残差块中第二个卷积层的输出通道数与输入通道数相同。在ResNet中还有Bottleneck残差块,它的输出通道数是输入通道数的4倍,expansion=4。
希望这可以帮助回答你的问题。
pytorch fpn实现
以下是使用PyTorch实现FPN的示例代码:
```python
import torch.nn as nn
import torch.nn.functional as F
class FPN(nn.Module):
def __init__(self, block, layers):
super(FPN, self).__init__()
self.inplanes = 64
self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3,
bias=False)
self.bn1 = nn.BatchNorm2d(64)
self.layer1 = self._make_layer(block, 64, layers[0])
self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
self.toplayer = nn.Conv2d(2048, 256, kernel_size=1, stride=1, padding=0)
self.smooth1 = nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1)
self.smooth2 = nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1)
self.smooth3 = nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1)
self.latlayer1 = nn.Conv2d(1024, 256, kernel_size=1, stride=1, padding=0)
self.latlayer2 = nn.Conv2d(512, 256, kernel_size=1, stride=1, padding=0)
self.latlayer3 = nn.Conv2d(256, 256, kernel_size=1, stride=1, padding=0)
def _make_layer(self, block, planes, blocks, stride=1):
downsample = None
if stride != 1 or self.inplanes != planes * block.expansion:
downsample = nn.Sequential(
nn.Conv2d(self.inplanes, planes * block.expansion,
kernel_size=1, stride=stride, bias=False),
nn.BatchNorm2d(planes * block.expansion),
)
layers = []
layers.append(block(self.inplanes, planes, stride, downsample))
self.inplanes = planes * block.expansion
for i in range(1, blocks):
layers.append(block(self.inplanes, planes))
return nn.Sequential(*layers)
def _upsample_add(self, x, y):
_, _, H, W = y.size()
return F.upsample(x, size=(H, W), mode='bilinear') + y
def forward(self, x):
c1 = F.relu(self.bn1(self.conv1(x)))
c1 = F.max_pool2d(c1, kernel_size=3, stride=2, padding=1)
c2 = self.layer1(c1)
c3 = self.layer2(c2)
c4 = self.layer3(c3)
c5 = self.layer4(c4)
p5 = self.toplayer(c5)
p4 = self._upsample_add(p5, self.latlayer1(c4))
p3 = self._upsample_add(p4, self.latlayer2(c3))
p2 = self._upsample_add(p3, self.latlayer3(c2))
p4 = self.smooth1(p4)
p3 = self.smooth2(p3)
p2 = self.smooth3(p2)
return p2, p3, p4, p5
```
其中,block参数指的是ResNet中的基本块类型,例如BasicBlock或Bottleneck;layers参数指的是ResNet中每个stage中基本块的数量,例如[3, 4, 6, 3]表示ResNet50。在FPN中,我们首先通过ResNet的前四个stage得到c2、c3、c4和c5特征层,然后通过自下而上的方式将这些特征层融合起来,得到p2、p3、p4和p5特征层。最后,我们对这些特征层进行平滑处理,以便更好地进行目标检测。