text: import torch.nn as nn
  import torch.nn.functional as Functional
  from models.ClassificationModel import ClassificationModel
  import torch
  import os
  
  """
  from
  https://github.com/geekfeiw/Multi-Scale-1D-ResNet/blob/master/model/multi_scale_ori.py
  """
  
  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(ClassificationModel):
      def __init__(self, input_channel, layers=[1, 1, 1, 1], num_classes=10, hidden_dims=64):
  
          self.d_model = hidden_dims
          self.inplanes3 = hidden_dims
          self.inplanes5 = hidden_dims
          self.inplanes7 = hidden_dims
          stride=2
  
          super(MSResNet, self).__init__()
  
          self.conv1 = nn.Conv1d(input_channel, hidden_dims, kernel_size=7, stride=2, padding=3, bias=False)
          self.bn1 = nn.BatchNorm1d(hidden_dims)
          self.relu = nn.ReLU(inplace=True)
          self.maxpool = nn.MaxPool1d(kernel_size=3, stride=2, padding=1)
  
          self.layer3x3_1 = self._make_layer3(BasicBlock3x3, hidden_dims, layers[0], stride=stride)
          self.layer3x3_2 = self._make_layer3(BasicBlock3x3, 2*hidden_dims, layers[1], stride=stride)
          self.layer3x3_3 = self._make_layer3(BasicBlock3x3, 4*hidden_dims, layers[2], stride=stride)
          # 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, hidden_dims, layers[0], stride=stride)
          self.layer5x5_2 = self._make_layer5(BasicBlock5x5, 2*hidden_dims, layers[1], stride=stride)
          self.layer5x5_3 = self._make_layer5(BasicBlock5x5, 4*hidden_dims, layers[2], stride=stride)
          # 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, hidden_dims, layers[0], stride=2)
          self.layer7x7_2 = self._make_layer7(BasicBlock7x7, 2*hidden_dims, layers[1], stride=2)
          self.layer7x7_3 = self._make_layer7(BasicBlock7x7, 4*hidden_dims, 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(4*hidden_dims*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 _logits(self, x0):
          x0 = torch.nn.functional.interpolate(x0, size=512)
  
          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
  
      def forward(self, x0):
          logits, _ = self._logits(x0)
  
          logprobabilities = Functional.log_softmax(logits, dim=-1)
  
          return logprobabilities, None, None, None
  
      def save(self, path="model.pth", **kwargs):
          print("\nsaving model to "+path)
          model_state = self.state_dict()
          os.makedirs(os.path.dirname(path), exist_ok=True)
          torch.save(dict(model_state=model_state,**kwargs),path)
  
      def load(self, path):
          print("loading model from "+path)
          snapshot = torch.load(path, map_location="cpu")
          model_state = snapshot.pop('model_state', snapshot)
          self.load_state_dict(model_state)
          return snapshot
  
  
  
  
  
  
  
  