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resnet3d.py
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resnet3d.py
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import torch.nn as nn
import torch.utils.model_zoo as model_zoo
import torch.nn.functional as F
#__all__ = ['ResNet', 'resnet18', 'resnet34', 'resnet50', 'resnet101', 'resnet152']
def conv3x3(in_planes, out_planes, stride=1):
return nn.Conv3d(in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=False)
class BasicBlock(nn.Module):
expansion = 1
def __init__(self, inplanes, planes, stride=1, downsample=None):
super(BasicBlock, self).__init__()
self.conv1 = conv3x3(inplanes, planes, stride)
self.bn1 = nn.BatchNorm3d(planes)
self.relu = nn.ReLU(inplace=True)
self.conv2 = conv3x3(planes, planes)
self.bn2 = nn.BatchNorm3d(planes)
self.downsample = downsample
self.stride = stride
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)
if self.downsample is not None:
residual = self.downsample(x)
out += residual
out = self.relu(out)
return out
class Bottleneck(nn.Module):
expansion = 4
def __init__(self, inplanes, planes, stride=1, downsample=None):
super(Bottleneck, self).__init__()
self.conv1 = nn.Conv3d(inplanes, 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.stride = stride
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 += residual
out = self.relu(out)
return out
# class ResNet(nn.Module):
# def __init__(self, block, layers, num_classes=1000, channel_size=[64,64,128,256,512],dropout=False):
# c = channel_size
# self.inplanes = c[0]
# super(ResNet, self).__init__()
# net = nn.Sequential()
# net.add_module('conv1', nn.Conv3d(1, c[0],kernel_size=7, stride=2, padding=0, bias=False))
# net.add_module('bn1', nn.BatchNorm3d(c[0]))
# net.add_module('relu', nn.ReLU(inplace=True))
# net.add_module('maxpool',nn.MaxPool3d(kernel_size=3, stride=2, padding=1))
# net.add_module('layer1', self._make_layer(block, c[1], layers[0]))
# net.add_module('layer2', self._make_layer(block, c[2], layers[1], stride=2))
# net.add_module('layer3', self._make_layer(block, c[3], layers[2], stride=2))
# net.add_module('layer4', self._make_layer(block, c[4], layers[3], stride=2))
# net.add_module('avgpool', nn.AvgPool3d([5,6,5], stride=1))
# if dropout is True:
# net.add_module('dropout', nn.Dropout(0.5))
# self.feature_extractor = net
# self.classifier = nn.Linear(c[4] * block.expansion, num_classes)
# for m in self.modules():
# if isinstance(m, nn.Conv3d):
# nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
# elif isinstance(m, nn.BatchNorm3d):
# nn.init.constant_(m.weight,1)
# nn.init.constant_(m.bias, 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.Conv3d(self.inplanes, planes*block.expansion,kernel_size=1, stride=stride, bias=False),
# nn.BatchNorm3d(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 forward(self, x):
# x = self.feature_extractor(x)
# x = x.view(x.size(0),-1)
# x = self.classifier(x)
# x = F.log_softmax(x)
# return x
# def resnet18(**kwargs):
# model = ResNet(BasicBlock, [2,2,2,2], **kwargs)
# return model
# def resnet34(**kwargs):
# model = ResNet(BasicBlock, [3,4,6,3], **kwargs)
# return model
# def resnet50(**kwargs):
# model = ResNet(BasicBlock, [3,4,6,3], **kwargs)
# return model