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net.py
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net.py
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import torch
import torch.nn as nn
import math
import torch.utils.model_zoo as model_zoo
__all__ = ['ResNet', 'resnet18', 'resnet34', 'resnet50', 'resnet101',
'resnet152']
model_urls = {
'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth',
'resnet34': 'https://download.pytorch.org/models/resnet34-333f7ec4.pth',
'resnet50': 'https://download.pytorch.org/models/resnet50-19c8e357.pth',
'resnet101': 'https://download.pytorch.org/models/resnet101-5d3b4d8f.pth',
'resnet152': 'https://download.pytorch.org/models/resnet152-b121ed1d.pth',
}
dp_rate = 0
def conv3x3(in_planes, out_planes, stride=1):
"""3x3 convolution with padding"""
return nn.Conv1d(in_planes, out_planes, kernel_size=33, stride=stride,
padding=16, bias=False)
class BasicBlock(nn.Module):
expansion = 2
def __init__(self, inplanes, planes, stride=1, downsample=None):
super(BasicBlock, self).__init__()
self.bn0 = nn.BatchNorm1d(inplanes)
self.relu = nn.ReLU(inplace=True)
self.conv1 = conv3x3(inplanes, planes, stride)
self.bn1 = nn.BatchNorm1d(planes)
self.conv2 = conv3x3(planes, planes*2)
self.downsample = downsample
self.stride = stride
self.dropout = nn.Dropout(dp_rate)
def forward(self, x):
residual = x
out = self.bn0(x)
out = self.relu(out)
# out = self.dropout(out)
out = self.conv1(out)
out = self.bn1(out)
out = self.relu(out)
out = self.dropout(out)
out = self.conv2(out)
if self.downsample is not None:
residual = self.downsample(x)
# residual = torch.cat((residual,residual),1)
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.bn0 = nn.BatchNorm1d(inplanes)
self.conv1 = nn.Conv1d(inplanes, planes, kernel_size=33, bias=False, padding=16)
self.bn1 = nn.BatchNorm1d(planes)
self.conv2 = nn.Conv1d(planes, planes, kernel_size=65, stride=stride,
padding=32, bias=False)
self.bn2 = nn.BatchNorm1d(planes)
self.conv3 = nn.Conv1d(planes, planes * 4, kernel_size=1, bias=False, padding=0)
self.bn3 = nn.BatchNorm1d(planes * 4)
self.relu = nn.ReLU(inplace=True)
self.downsample = downsample
self.stride = stride
self.dropout = nn.Dropout(dp_rate)
def forward(self, x):
residual = x
out = self.bn0(x)
out = self.relu(out)
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)
if self.downsample is not None:
residual = self.downsample(x)
# residual = torch.cat((residual, residual), 1)
out += residual
out = self.relu(out)
return out
class ResNet(nn.Module):
def __init__(self, block, layers, classification, num_classes=5):
self.inplanes = 12
self.classification = classification
super(ResNet, self).__init__()
self.conv1 = nn.Conv1d(1, self.inplanes, kernel_size=33, stride=1, padding=16,
bias=False)
self.bn1 = nn.BatchNorm1d(self.inplanes)
self.relu = nn.ReLU(inplace=True)
self.maxpool = nn.MaxPool1d(kernel_size=3, stride=2, padding=1)
self.conv2 = nn.Conv1d(self.inplanes, self.inplanes, kernel_size=33, stride=2, padding=16,
bias=False)
self.bn2 = nn.BatchNorm1d(self.inplanes)
self.downsample = nn.MaxPool1d(kernel_size=2, stride=2)
self.conv3 = nn.Conv1d(self.inplanes, self.inplanes, kernel_size=33, stride=1, padding=16,
bias=False)
self.dropout = nn.Dropout(dp_rate)
self.layer1 = self._make_layer(block, 12, layers[0], stride=2)
self.layer2 = self._make_layer(block, 24, layers[1], stride=2)
self.layer3 = self._make_layer(block, 48, layers[2], stride=2)
self.layer4 = self._make_layer(block, 96, layers[3], stride=2)
# self.layer5 = self._make_layer(block, self.inplanes, layers[4], stride=2)
self.bn_final = nn.BatchNorm1d(96*2)
self.avgpool = nn.AdaptiveAvgPool1d(2)
self.fc1 = nn.Linear(96*4, 384)
self.bn3 = nn.BatchNorm1d(384)
self.fc2 = nn.Linear(384, 192)
self.bn4 = nn.BatchNorm1d(192)
self.fc3 = nn.Linear(192, 5)
self.softmax = nn.Softmax(1)
for m in self.modules():
if isinstance(m, nn.Conv1d):
nn.init.kaiming_normal_(m.weight.data, mode='fan_in', nonlinearity='relu')
elif isinstance(m, nn.BatchNorm1d):
m.weight.data.fill_(1)
m.bias.data.zero_()
elif isinstance(m, nn.Linear):
m.weight.data.normal_(0, 0.01)
m.bias.data.zero_()
def _make_layer(self, block, planes, blocks, stride=1):
downsample = None
if stride != 1:
downsample = nn.Sequential(
nn.Conv1d(self.inplanes, planes * block.expansion,
kernel_size=1, stride=stride, bias=False),
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):
x = self.conv1(x)
x = self.bn1(x)
x = self.relu(x)
# x = self.maxpool(x)
out = self.conv2(x)
out = self.bn2(out)
out = self.relu(out)
out = self.dropout(out)
out = self.conv3(out)
residual = self.downsample(x)
out += residual
x = self.relu(out)
x = self.layer1(x)
x = self.layer2(x)
x = self.layer3(x)
x = self.layer4(x)
# x = self.layer5(x)
x = self.bn_final(x)
x = self.avgpool(x)
x = x.view(x.size(0), -1)
if self.classification:
x = self.fc1(x)
x = self.bn3(x)
x = self.relu(x)
x = self.dropout(x)
x = self.fc2(x)
x = self.bn4(x)
x = self.relu(x)
x = self.dropout(x)
x = self.fc3(x)
# x = self.softmax(x)
return x
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(model_urls['resnet18']))
return model
def resnet34(pretrained=False, **kwargs):
"""Constructs a ResNet-34 model.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
model = ResNet(BasicBlock, [3, 4, 6, 3], **kwargs)
if pretrained:
model.load_state_dict(model_zoo.load_url(model_urls['resnet34']))
return model
def resnet50(pretrained=False, **kwargs):
"""Constructs a ResNet-50 model.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
model = ResNet(Bottleneck, [3, 4, 6, 3], **kwargs)
if pretrained:
model.load_state_dict(model_zoo.load_url(model_urls['resnet50']))
return model
def resnet101(pretrained=False, **kwargs):
"""Constructs a ResNet-101 model.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
model = ResNet(Bottleneck, [3, 4, 23, 3], **kwargs)
if pretrained:
model.load_state_dict(model_zoo.load_url(model_urls['resnet101']))
return model
def resnet152(pretrained=False, **kwargs):
"""Constructs a ResNet-152 model.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
model = ResNet(Bottleneck, [3, 8, 36, 3], **kwargs)
if pretrained:
model.load_state_dict(model_zoo.load_url(model_urls['resnet152']))
return model