import torch.nn as nn import math import torch.utils.model_zoo as model_zoo 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, layers=[1, 1, 1, 1], num_classes=10): 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.MaxPool1d(kernel_size=3, stride=2, padding=1) self.layer3x3_1 = self._make_layer3(BasicBlock3x3, 64, layers[0], stride=2) self.layer3x3_2 = self._make_layer3(BasicBlock3x3, 128, layers[1], stride=2) self.layer3x3_3 = self._make_layer3(BasicBlock3x3, 256, layers[2], stride=2) # self.layer3x3_4 = self._make_layer3(BasicBlock3x3, 512, layers[3], stride=2) # maxplooing kernel size: 16, 11, 6 self.maxpool3 = nn.AvgPool1d(kernel_size=16, stride=1, padding=0) self.layer5x5_1 = self._make_layer5(BasicBlock5x5, 64, layers[0], stride=2) self.layer5x5_2 = self._make_layer5(BasicBlock5x5, 128, layers[1], stride=2) self.layer5x5_3 = self._make_layer5(BasicBlock5x5, 256, layers[2], stride=2) # self.layer5x5_4 = self._make_layer5(BasicBlock5x5, 512, layers[3], stride=2) self.maxpool5 = nn.AvgPool1d(kernel_size=11, stride=1, padding=0) self.layer7x7_1 = self._make_layer7(BasicBlock7x7, 64, layers[0], stride=2) self.layer7x7_2 = self._make_layer7(BasicBlock7x7, 128, layers[1], stride=2) self.layer7x7_3 = self._make_layer7(BasicBlock7x7, 256, layers[2], stride=2) # self.layer7x7_4 = self._make_layer7(BasicBlock7x7, 512, layers[3], stride=2) self.maxpool7 = nn.AvgPool1d(kernel_size=6, stride=1, padding=0) # self.drop = nn.Dropout(p=0.2) self.fc = nn.Linear(256*3, 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 = self.conv1(x0) x0 = self.bn1(x0) x0 = self.relu(x0) x0 = self.maxpool(x0) x = self.layer3x3_1(x0) x = self.layer3x3_2(x) x = self.layer3x3_3(x) # x = self.layer3x3_4(x) x = self.maxpool3(x) y = self.layer5x5_1(x0) y = self.layer5x5_2(y) y = self.layer5x5_3(y) # y = self.layer5x5_4(y) y = self.maxpool5(y) z = self.layer7x7_1(x0) z = self.layer7x7_2(z) z = self.layer7x7_3(z) # z = self.layer7x7_4(z) z = self.maxpool7(z) out = torch.cat([x, y, z], dim=1) out = out.squeeze() # out = self.drop(out) out1 = self.fc(out) return out1, out