pytorch中怎么调用resnet8×4

在 PyTorch 中，可以通过以下代码调用 ResNet-8×4：
import torch
import torch.nn as nn

class BasicBlock(nn.Module):
def __init__(self, in_planes, out_planes, stride):
super(BasicBlock, self).__init__()
self.conv1 = nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=False)
self.bn1 = nn.BatchNorm2d(out_planes)
self.conv2 = nn.Conv2d(out_planes, out_planes, kernel_size=3, stride=1, padding=1, bias=False)
self.bn2 = nn.BatchNorm2d(out_planes)

self.shortcut = nn.Sequential()
if stride != 1 or in_planes != out_planes:
self.shortcut = nn.Sequential(
nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False),
nn.BatchNorm2d(out_planes)
)

def forward(self, x):
out = nn.ReLU()(self.bn1(self.conv1(x)))
out = self.bn2(self.conv2(out))
out += self.shortcut(x)
out = nn.ReLU()(out)
return out

class ResNet(nn.Module):
def __init__(self, num_blocks, num_classes=10):
super(ResNet, self).__init__()
self.in_planes = 16

self.conv1 = nn.Conv2d(3, 16, kernel_size=3, stride=1, padding=1, bias=False)
self.bn1 = nn.BatchNorm2d(16)
self.layer1 = self._make_layer(num_blocks[0], 16, 1)
self.layer2 = self._make_layer(num_blocks[1], 32, 2)
self.layer3 = self._make_layer(num_blocks[2], 64, 2)
self.linear = nn.Linear(64, num_classes)

def _make_layer(self, num_blocks, out_planes, stride):
strides = [stride] + [1]*(num_blocks-1)
layers = []
for stride in strides:
layers.append(BasicBlock(self.in_planes, out_planes, stride))
self.in_planes = out_planes
return nn.Sequential(*layers)

def forward(self, x):
out = nn.ReLU()(self.bn1(self.conv1(x)))
out = self.layer1(out)
out = self.layer2(out)
out = self.layer3(out)
out = nn.AvgPool2d(8)(out)
out = out.view(out.size(0), -1)
out = self.linear(out)
return out

def ResNet8x4():
return ResNet([1, 1, 1, 1])
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这里定义了一个名为 ResNet8x4 的函数，它返回一个 ResNet 模型，其中包含 4 个残差块，每个块包含 8 个卷积层。调用该函数即可创建 ResNet-8×4 模型的实例。