3D ResNet系列网络由日本国家工业科学技术研究院的Kensho Hara等人提出。接下来，我将对3D ResNet系列网络做出详细的网络结构解释，欢迎大家补充与提问。文章来源：https://www.toymoban.com/news/detail-523745.html
我的github链接主页为XuecWu (Conna) · GitHub文章来源地址https://www.toymoban.com/news/detail-523745.html
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from functools import partial
def conv3x3x3(in_planes, out_planes, stride=1):
# 3x3x3 convolution with padding
return nn.Conv3d(in_channels=in_planes, out_channels=out_planes, kernel_size=3, stride=stride, padding=1,bias=False)
#------------------------------------#
# 此为对于ResNet-101中的Bottleneck的定义
#------------------------------------#
class Bottleneck(nn.Module):
expansion = 4
def __init__(self, in_planes, planes, stride=1, downsample=None):
super(Bottleneck, self).__init__()
self.stride = stride
self.conv1 = nn.Conv3d(in_planes, planes, kernel_size=1, bias=False)
self.bn1 = nn.BatchNorm3d(planes)
self.conv2 = nn.Conv3d(planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
self.bn2 = nn.BatchNorm3d(planes)
self.conv3 = nn.Conv3d(planes, planes * self.expansion, kernel_size=1, bias=False)
self.bn3 = nn.BatchNorm3d(planes * self.expansion)
self.relu = nn.ReLU(inplace=True)
self.downsample = downsample
def forward(self, x):
residual = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
out = self.relu(out)
out = self.conv3(out)
out = self.bn3(out)
if self.downsample is not None:
residual = self.downsample(x)
out = out + residual
out = self.relu(out)
return out
#------------------------------#
# 此为对于ResNet的定义
# 这里需要注意,如果更换了数据集,那么
# 我们就要更换相应的num_classes值!!!
#------------------------------#
class ResNet(nn.Module):
def __init__(self, block, layers, sample_size, sample_duration, shortcut_type='B', num_classes=8):
super(ResNet, self).__init__()
self.in_planes = 64
self.conv1 = nn.Conv3d(3, 64, kernel_size=7, stride=(1, 2, 2), padding=(3, 3, 3), bias=False)
self.bn1 = nn.BatchNorm3d(64)
self.relu = nn.ReLU(inplace=True)
self.maxpool = nn.MaxPool3d(kernel_size=(3, 3, 3), stride=2, padding=1)
self.layer1 = self._make_layer(block, 64, layers[0], shortcut_type)
self.layer2 = self._make_layer(block, 128, layers[1], shortcut_type, stride=2)
self.layer3 = self._make_layer(block, 256, layers[2], shortcut_type, stride=2)
self.layer4 = self._make_layer(block, 512, layers[3], shortcut_type, stride=2)
#-------------------------------------#
# math.ceil()方法的含义为向上取整
# 之后又加了一个int限制,以充分确保该数为整数
#-------------------------------------#
last_duration = int(math.ceil(sample_duration / 16))
last_size = int(math.ceil(sample_size / 32))
self.avgpool = nn.AvgPool3d((last_duration, last_size, last_size), stride=1)
self.fc = nn.Linear(512 * block.expansion, num_classes)
for m in self.modules():
if isinstance(m, nn.Conv3d):
nn.init.kaiming_normal_(m.weight, mode='fan_out') #对于3D卷积所采用的权重初始化方法
elif isinstance(m, nn.BatchNorm3d):
m.weight.data.fill_(1)
m.bias.data.zero_()
def _make_layer(self, block, planes, blocks, shortcut_type, stride=1):
downsample = None
if stride != 1 or self.in_planes != planes * block.expansion:
if shortcut_type == 'A':
assert True, 'Not implemented!!!'
else:
downsample = nn.Sequential(
nn.Conv3d(self.in_planes, planes * block.expansion, kernel_size=1, stride=stride, bias=False),
nn.BatchNorm3d(planes * block.expansion),)
layers = []
layers.append(block(self.in_planes, planes, stride, downsample))
self.in_planes = planes * block.expansion
for i in range(1, blocks):
layers.append(block(self.in_planes, planes))
return nn.Sequential(*layers)
def forward(self, x):
x = self.conv1(x)
x = self.bn1(x)
x = self.relu(x)
x = self.maxpool(x)
x = self.layer1(x)
x = self.layer2(x)
x = self.layer3(x)
x = self.layer4(x)
x = self.avgpool(x)
x = x.view(x.size(0), -1)
x = self.fc(x)
return x到了这里，关于【CVPR2018 3D ResNet】3D ResNet网络结构详解的文章就介绍完了。如果您还想了解更多内容，请在右上角搜索TOY模板网以前的文章或继续浏览下面的相关文章，希望大家以后多多支持TOY模板网！
