code: # -*- coding: utf-8 -*-
  '''
  update: 2021/5/18    lym
  '''
  import torch
  import torch.nn.functional as F
  import torch.nn as nn
  import math
  import torch.utils.model_zoo as model_zoo
  from torchsummary import summary
  
  __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',
  }
  
  #Mish - "Mish: A Self Regularized Non-Monotonic Neural Activation Function"
  #https://arxiv.org/abs/1908.08681v1
  #implemented for PyTorch / FastAI by lessw2020 
  #github: https://github.com/lessw2020/mish
  
  class Mish(nn.Module):
      def __init__(self):
          super().__init__()
  
      def forward(self, x):
          #inlining this saves 1 second per epoch (V100 GPU) vs having a temp x and then returning x(!)
          return x *( torch.tanh(F.softplus(x)))
  
  # or: ELU+init (a=0.54; gain=1.55)
  act_fn = Mish()#nn.ReLU(inplace=True)
  
  class Attention(nn.Module):
      def __init__(self, feature_dim, step_dim, bias=True, **kwargs):
          super(Attention, self).__init__(**kwargs)
          
          self.supports_masking = True
  
          self.bias = bias
          self.feature_dim = feature_dim
          self.step_dim = step_dim
          self.features_dim = 0
          
          weight = torch.zeros(feature_dim, 1)
          nn.init.kaiming_uniform_(weight)
          self.weight = nn.Parameter(weight)
          
          if bias:
              self.b = nn.Parameter(torch.zeros(step_dim))
          
      def forward(self, x, mask=None):
          feature_dim = self.feature_dim 
          step_dim = self.step_dim
  
          eij = torch.mm(
              x.contiguous().view(-1, feature_dim), 
              self.weight
          ).view(-1, step_dim)
          
          if self.bias:
              eij = eij + self.b
              
          eij = torch.tanh(eij)
          a = torch.exp(eij)
          
          if mask is not None:
              a = a * mask
  
          a = a / (torch.sum(a, 1, keepdim=True) + 1e-10)
  
          weighted_input = x * torch.unsqueeze(a, -1)
          return torch.sum(weighted_input, 1)
  
  def conv3x3(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)
  
  
  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.BatchNorm1d(planes)
          self.relu = nn.ReLU(inplace=True)
          self.conv2 = conv3x3(planes, planes)
          self.bn2 = nn.BatchNorm1d(planes)
          self.downsample = downsample
          self.stride = stride
          self.dropout = nn.Dropout(.2)
  
      def forward(self, x):
          residual = 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)
  
          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.Conv1d(inplanes, planes, kernel_size=7, bias=False, padding=3)
          self.bn1 = nn.BatchNorm1d(planes)
          self.conv2 = nn.Conv1d(planes, planes, kernel_size=11, stride=stride,
                                 padding=5, bias=False)
          self.bn2 = nn.BatchNorm1d(planes)
          self.conv3 = nn.Conv1d(planes, planes * 4, kernel_size=7, bias=False, padding=3)
          self.bn3 = nn.BatchNorm1d(planes * 4)
          self.relu = nn.ReLU(inplace=True)
          self.downsample = downsample
          self.stride = stride
          self.dropout = nn.Dropout(.2)
  
      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.dropout(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, in_channels, block, layers, num_classes=27):
          self.inplanes = 64
          super(ResNet, self).__init__()
          self.conv1 = nn.Conv1d(in_channels, 64, kernel_size=15, stride=2, padding=7, bias=False)  #12   6dao lian
          self.bn1 = nn.BatchNorm1d(64)
          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.lstm = nn.LSTM(80, 512, bidirectional=True, batch_first=True)
          self.attention_layer = Attention(1024, 512)
  
          self.fc = nn.Linear(512 * block.expansion, num_classes)
  
          for m in self.modules():
              if isinstance(m, nn.Conv1d):
                  n = m.kernel_size[0] * m.kernel_size[0] * 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_layer(self, block, planes, blocks, stride=1):
          downsample = None
          if stride != 1 or self.inplanes != planes * block.expansion:
              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 i 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)
  
          x = self.layer1(x)
          x = self.layer2(x)
          x = self.layer3(x)
          x = self.layer4(x)
          # print(x.shape)
          # max_pooled = F.adaptive_max_pool1d(out, 1)
          # avg_pooled = F.adaptive_avg_pool1d(out, 1)
          # out = torch.cat([max_pooled, avg_pooled], dim=1)
          x = F.adaptive_max_pool1d(x, 1)#self.avgpool(x)
          # x,_ = self.lstm(x)
          # x = self.attention_layer(x)
  
          # print(x.shape)
          x = x.view(x.size(0), -1)
          # print(x.shape)
          x = self.fc(x)
          # print('x.shape:', x.shape)
          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:
  #         print('----------------downloading----------------')
  #         model.load_state_dict(model_zoo.load_url(model_urls['resnet50']))
  #     return model
  
  def resnet50(in_channels, **kwargs):
      """Constructs a ResNet-50 model.
  
      Args:
          pretrained (bool): If True, returns a model pre-trained on ImageNet
      """
      model = ResNet(in_channels,Bottleneck, [3, 4, 6, 3], **kwargs)
      # if pretrained:
      #     print('----------------downloading----------------')
      #     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
  
  
  if __name__ == '__main__':
  
      in_channels = 6
      x = torch.randn(2, in_channels, 72000)
      # x = torch.randn(1, 2560*6, 12)
      # net = resnet18()
      # net = resnet34()
      net = resnet50(in_channels)
      # net = resnet101()
      # net = resnet152()
      y = net(x)
  
      summary(net, (in_channels, 72000))
  
      # print(net)
      print(x.shape)
      print(y.shape)
  
apis: torch.nn.functional.softplus torch.nn.AdaptiveAvgPool1d torch.nn.init.kaiming_uniform_ torch.nn.Dropout torch.nn.BatchNorm1d torch.randn torch.tanh torch.unsqueeze torch.nn.MaxPool1d torch.nn.LSTM torch.nn.Conv1d torch.sum torch.nn.Linear torch.nn.Parameter torch.exp torch.nn.functional.adaptive_max_pool1d torch.nn.Sequential torch.zeros torch.nn.ReLU