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ResNet.py
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ResNet.py
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import torch.nn as nn
import torch.utils.model_zoo as model_zoo
import torch
class SELayer(nn.Module):
def __init__(self, channel, reduction=16):
super(SELayer, self).__init__()
self.avg_pool = nn.AdaptiveAvgPool1d(1)
self.fc = nn.Sequential(
nn.Linear(channel, channel // reduction, bias=False),
nn.ReLU(inplace=True),
nn.Linear(channel // reduction, channel, bias=False),
nn.Sigmoid()
)
def forward(self, x):
b, c, _ = x.size()
y = self.avg_pool(x).view(b, c)
y = self.fc(y).view(b, c, 1)
return x * y.expand_as(x)
def conv3x1(in_planes, out_planes, stride=1):
"""3x3 convolution with padding"""
return nn.Conv1d(in_planes, out_planes, kernel_size=7, stride=stride,
padding=3, bias=False)
def conv1x1(in_planes, out_planes, stride=1):
"""1x1 convolution"""
return nn.Conv1d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False)
class BasicBlock(nn.Module):
expansion = 1
def __init__(self, inplanes, planes, stride=1, downsample=None):
super(BasicBlock, self).__init__()
self.conv1 = conv3x1(inplanes, planes, stride)
self.bn1 = nn.BatchNorm1d(planes)
self.relu = nn.ReLU(inplace=True)
self.conv2 = conv3x1(planes, planes)
self.bn2 = nn.BatchNorm1d(planes)
self.se = SELayer(planes)
self.downsample = downsample
self.stride = stride
self.dropout = nn.Dropout(.2)
def forward(self, x):
identity = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.dropout(out)
out = self.conv2(out)
out = self.bn2(out)
out = self.se(out)
if self.downsample is not None:
identity = self.downsample(x)
out += identity
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 = conv1x1(inplanes, planes)
self.bn1 = nn.BatchNorm1d(planes)
self.conv2 = conv3x1(planes, planes, stride)
self.bn2 = nn.BatchNorm1d(planes)
self.conv3 = conv1x1(planes, planes * self.expansion)
self.bn3 = nn.BatchNorm1d(planes * self.expansion)
self.se = SELayer(self.expansion * planes)
self.relu = nn.ReLU(inplace=True)
self.downsample = downsample
self.stride = stride
self.dropout = nn.Dropout(.2)
def forward(self, x):
identity = 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.dropout(out)
out = self.conv3(out)
out = self.bn3(out)
out = self.se(out)
if self.downsample is not None:
identity = self.downsample(x)
out += identity
out = self.relu(out)
return out
class ResNet(nn.Module):
def __init__(self, block, layers, in_channel=1, out_channel=10, zero_init_residual=False):
super(ResNet, self).__init__()
self.inplanes = 64
self.conv1 = nn.Conv1d(in_channel, 64, kernel_size=15, stride=2, padding=7,
bias=False)
self.bn1 = nn.BatchNorm1d(64) # expects an input in [batch_size, n_features, n_timesteps]. Arg should be n_timesteps
self.relu = nn.ReLU(inplace=True)
self.maxpool = nn.MaxPool1d(kernel_size=3, stride=2, padding=1)
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.avgpool = nn.AdaptiveAvgPool1d(1)
self.fc1 = nn.Linear(6, 12) #num_features, num_features*2
self.fc = nn.Linear(512 * block.expansion + 12, out_channel) #512 * block.expansion + num_features*2
for m in self.modules():
if isinstance(m, nn.Conv1d):
nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
elif isinstance(m, nn.BatchNorm1d):
nn.init.constant_(m.weight, 1)
nn.init.constant_(m.bias, 0)
# Zero-initialize the last BN in each residual branch,
# so that the residual branch starts with zeros, and each residual block behaves like an identity.
# This improves the model by 0.2~0.3% according to https://arxiv.org/abs/1706.02677
if zero_init_residual:
for m in self.modules():
if isinstance(m, Bottleneck):
nn.init.constant_(m.bn3.weight, 0)
elif isinstance(m, BasicBlock):
nn.init.constant_(m.bn2.weight, 0)
def _make_layer(self, block, planes, blocks, stride=1):
downsample = None
if stride != 1 or self.inplanes != planes * block.expansion:
downsample = nn.Sequential(
conv1x1(self.inplanes, planes * block.expansion, stride),
nn.BatchNorm1d(planes * block.expansion),
)
layers = []
layers.append(block(self.inplanes, planes, stride, downsample))
self.inplanes = planes * block.expansion
for _ in range(1, blocks):
layers.append(block(self.inplanes, planes))
return nn.Sequential(*layers)
def forward(self, x, ag):
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)
ag = self.fc1(ag)
x = torch.cat((ag, x), dim=1)
x = self.fc(x)
return x # change to torch.sigmoid(x) if using only BCELoss
def resnet18(pretrained=False, **kwargs):
"""Constructs a ResNet-18 model.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
model = ResNet(BasicBlock, [2, 2, 2, 2], **kwargs)
if pretrained:
model.load_state_dict(model_zoo.load_url('https://download.pytorch.org/models/resnet18-5c106cde.pth'))
return model