text: # -*- coding: utf-8 -*-
  '''
  @time: 2019/9/8 20:14
  直接修改torch的resnet
  @ author: javis
  '''
  
  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',
  }
  
  
  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, block, layers, num_classes=18):
          self.inplanes = 64
          super(ResNet, self).__init__()
          self.conv1 = nn.Conv1d(13, 64, kernel_size=15, stride=2, padding=7,
                                 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.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.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)
          x = self.avgpool(x)
          x = x.view(x.size(0), -1)
          x = self.fc(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
  
  
  
  
  
  if __name__ == '__main__':
      import torch
  
      x = torch.randn(1, 13, 2048)
      m = resnet34()
      m(x)
      from torchvision.models import resnet
  