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MulitScale1DResNet.py
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MulitScale1DResNet.py
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import torch.nn as nn
import math
import torch.utils.model_zoo as model_zoo
import torch.nn.functional as F
import torch
def conv3x3(in_planes, out_planes, stride=1):
"""3x3 convolution with padding"""
return nn.Conv1d(in_planes, out_planes, kernel_size=3, stride=stride,
padding=1, bias=False)
def conv5x5(in_planes, out_planes, stride=1):
return nn.Conv1d(in_planes, out_planes, kernel_size=5, stride=stride,
padding=1, bias=False)
def conv7x7(in_planes, out_planes, stride=1):
return nn.Conv1d(in_planes, out_planes, kernel_size=7, stride=stride,
padding=1, bias=False)
class BasicBlock3x3(nn.Module):
expansion = 1
def __init__(self, inplanes3, planes, stride=1, downsample=None):
super(BasicBlock3x3, self).__init__()
self.conv1 = conv3x3(inplanes3, planes, stride)
self.bn1 = nn.BatchNorm1d(planes)
self.relu = nn.ReLU(inplace=True)
self.conv2 = conv3x3(planes, planes)
self.bn2 = nn.BatchNorm1d(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 BasicBlock5x5(nn.Module):
expansion = 1
def __init__(self, inplanes5, planes, stride=1, downsample=None):
super(BasicBlock5x5, self).__init__()
self.conv1 = conv5x5(inplanes5, planes, stride)
self.bn1 = nn.BatchNorm1d(planes)
self.relu = nn.ReLU(inplace=True)
self.conv2 = conv5x5(planes, planes)
self.bn2 = nn.BatchNorm1d(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)
d = residual.shape[2] - out.shape[2]
out1 = residual[:,:,0:-d] + out
out1 = self.relu(out1)
# out += residual
return out1
class BasicBlock7x7(nn.Module):
expansion = 1
def __init__(self, inplanes7, planes, stride=1, downsample=None):
super(BasicBlock7x7, self).__init__()
self.conv1 = conv7x7(inplanes7, planes, stride)
self.bn1 = nn.BatchNorm1d(planes)
self.relu = nn.ReLU(inplace=True)
self.conv2 = conv7x7(planes, planes)
self.bn2 = nn.BatchNorm1d(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)
d = residual.shape[2] - out.shape[2]
out1 = residual[:, :, 0:-d] + out
out1 = self.relu(out1)
# out += residual
return out1
class MSResNet(nn.Module):
def __init__(self, input_channel, device, layers=[1, 1, 1, 1], num_classes=10):
self.device = device
self.inplanes3 = 64
self.inplanes5 = 64
self.inplanes7 = 64
super(MSResNet, self).__init__()
self.conv1 = nn.Conv1d(input_channel, 64, kernel_size=7, stride=2, padding=3,
bias=False)
self.bn1 = nn.BatchNorm1d(64)
self.relu = nn.ReLU(inplace=True)
self.maxpool = nn.AvgPool1d(kernel_size=3)#, stride=2, padding=1)
self.layer3x3_1 = self._make_layer3(BasicBlock3x3, 32, layers[0], stride=2)
self.layer3x3_2 = self._make_layer3(BasicBlock3x3, 64, layers[1], stride=2)
self.layer3x3_3 = self._make_layer3(BasicBlock3x3, 128, layers[2], stride=2)
self.layer3x3_4 = self._make_layer3(BasicBlock3x3, 256, layers[3], stride=2)
#maxplooing kernel size: 16, 11, 6
self.maxpool3 = nn.AvgPool1d(kernel_size=25, stride=1, padding=0)
self.layer5x5_1 = self._make_layer5(BasicBlock5x5, 32, layers[0], stride=2)
self.layer5x5_2 = self._make_layer5(BasicBlock5x5, 64, layers[1], stride=2)
self.layer5x5_3 = self._make_layer5(BasicBlock5x5, 128, layers[2], stride=2)
self.layer5x5_4 = self._make_layer5(BasicBlock5x5, 256, layers[3], stride=2)
self.maxpool5 = nn.AvgPool1d(kernel_size=21, stride=1, padding=0)
self.layer7x7_1 = self._make_layer7(BasicBlock7x7, 32, layers[0], stride=2)
self.layer7x7_2 = self._make_layer7(BasicBlock7x7, 64, layers[1], stride=2)
self.layer7x7_3 = self._make_layer7(BasicBlock7x7, 128, layers[2], stride=2)
self.layer7x7_4 = self._make_layer7(BasicBlock7x7, 256, layers[3], stride=2)
self.maxpool7 = nn.AvgPool1d(kernel_size=18, stride=1, padding=0)
# self.drop = nn.Dropout(p=0.2)
self.fc = nn.Linear(3*256, num_classes)
# todo: modify the initialization
# for m in self.modules():
# if isinstance(m, nn.Conv1d):
# n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
# m.weight.data.normal_(0, math.sqrt(2. / n))
# elif isinstance(m, nn.BatchNorm1d):
# m.weight.data.fill_(1)
# m.bias.data.zero_()
def _make_layer3(self, block, planes, blocks, stride=2):
downsample = None
if stride != 1 or self.inplanes3 != planes * block.expansion:
downsample = nn.Sequential(
nn.Conv1d(self.inplanes3, planes * block.expansion,
kernel_size=1, stride=stride, bias=False),
nn.BatchNorm1d(planes * block.expansion),
)
layers = []
layers.append(block(self.inplanes3, planes, stride, downsample))
self.inplanes3 = planes * block.expansion
for i in range(1, blocks):
layers.append(block(self.inplanes3, planes))
return nn.Sequential(*layers)
def _make_layer5(self, block, planes, blocks, stride=2):
downsample = None
if stride != 1 or self.inplanes5 != planes * block.expansion:
downsample = nn.Sequential(
nn.Conv1d(self.inplanes5, planes * block.expansion,
kernel_size=1, stride=stride, bias=False),
nn.BatchNorm1d(planes * block.expansion),
)
layers = []
layers.append(block(self.inplanes5, planes, stride, downsample))
self.inplanes5 = planes * block.expansion
for i in range(1, blocks):
layers.append(block(self.inplanes5, planes))
return nn.Sequential(*layers)
def _make_layer7(self, block, planes, blocks, stride=2):
downsample = None
if stride != 1 or self.inplanes7 != planes * block.expansion:
downsample = nn.Sequential(
nn.Conv1d(self.inplanes7, planes * block.expansion,
kernel_size=1, stride=stride, bias=False),
nn.BatchNorm1d(planes * block.expansion),
)
layers = []
layers.append(block(self.inplanes7, planes, stride, downsample))
self.inplanes7 = planes * block.expansion
for i in range(1, blocks):
layers.append(block(self.inplanes7, planes))
return nn.Sequential(*layers)
def forward(self, x0):
x0 = x0.view(x0.size()[0],1,-1).to(self.device).float()
x0 = self.conv1(x0)
x0 = self.bn1(x0)
x0 = self.relu(x0)
x0 = self.maxpool(x0)
#print(x0.size())
x = self.layer3x3_1(x0)
x = self.maxpool(x)
x = self.layer3x3_2(x)
x = self.maxpool(x)
x = self.layer3x3_3(x)
x = self.maxpool(x)
x = self.layer3x3_4(x)
#print(x.size())
x = self.maxpool3(x)
y = self.layer5x5_1(x0)
y = self.maxpool(y)
y = self.layer5x5_2(y)
y = self.maxpool(y)
y = self.layer5x5_3(y)
y = self.maxpool(y)
y = self.layer5x5_4(y)
#print(y.size())
y = self.maxpool5(y)
z = self.layer7x7_1(x0)
z = self.maxpool(z)
z = self.layer7x7_2(z)
z = self.maxpool(z)
z = self.layer7x7_3(z)
z = self.maxpool(z)
z = self.layer7x7_4(z)
#print(z.size())
z = self.maxpool7(z)
out = torch.cat([x.view(x.size()[0], -1), y.view(y.size()[0], -1), z.view(z.size()[0], -1)], dim=1)
out = out.squeeze()
# out = self.drop(out)
out1 = self.fc(out)
return F.log_softmax(out1, dim=1)