qq_39032270 2023-09-24 15:55 采纳率: 0%
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将resnet50中的卷积替换微ODConv动态卷积

问题1:如何将以下resnet50中的卷积替换微ODConv动态卷积
问题2:如果将修改后的网络训练得到的model.pth用于预测,是否会因为模型结构的变化而导致尺寸不匹配的问题。怎么解决?
例如:

size mismatch for layer1.0.conv2.weight: copying a param with shape torch.Size([64, 64, 3, 3]) from checkpoint, the shape in current model is torch.Size([1, 64, 64, 3, 3]).

具体来说,错误消息中提到了一个名为layer1.0.conv2.weight的参数,在加载预训练权重时,其形状为torch.Size([64, 64, 3, 3]),而当前模型中该参数的形状为torch.Size([1, 64, 64, 3, 3])

import torch.nn as nn

__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-b121ed2d.pth',
}


class Bottleneck(nn.Module):
  expansion = 4

  def __init__(self, inplanes, planes, stride=1, downsample=None, dilation=1):
    super(Bottleneck, self).__init__()
    self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
    self.bn1 = nn.BatchNorm2d(planes)
    # original padding is 1; original dilation is 1
    self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, padding=dilation, bias=False, dilation=dilation)
    self.bn2 = nn.BatchNorm2d(planes)
    self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)
    self.bn3 = nn.BatchNorm2d(planes * 4)
    self.relu = nn.ReLU(inplace=True)
    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)
    out = self.relu(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, last_conv_stride=2, last_conv_dilation=1):

    self.inplanes = 64
    super(ResNet, self).__init__()
    self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3,
                           bias=False)
    self.bn1 = nn.BatchNorm2d(64)
    self.relu = nn.ReLU(inplace=True)
    self.maxpool = nn.MaxPool2d(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=last_conv_stride, dilation=last_conv_dilation)

    for m in self.modules():
      if isinstance(m, nn.Conv2d):
        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.BatchNorm2d):
        m.weight.data.fill_(1)
        m.bias.data.zero_()

  def _make_layer(self, block, planes, blocks, stride=1, dilation=1):
    downsample = None
#     if stride != 1 or self.inplanes != planes * block.expansion:
    downsample = nn.Sequential(
        nn.Conv2d(self.inplanes, planes * block.expansion,
                  kernel_size=1, stride=stride, bias=False),
        nn.BatchNorm2d(planes * block.expansion),
    )

    layers = []
    layers.append(block(self.inplanes, planes, stride, downsample, dilation))
    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)

    return x


def remove_fc(state_dict):
    """Remove the fc layer parameters from state_dict."""
    key_collect = []
    for key, value in state_dict.items():
        if key.startswith('fc.'):
            key_collect.append(key)
#             del state_dict[key]
    for key in key_collect:
        del state_dict[key]
    return state_dict


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(remove_fc(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(remove_fc(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(remove_fc(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(
      remove_fc(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(
      remove_fc(model_zoo.load_url(model_urls['resnet152'])))
  return model


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  • 专家-赤兔[在线] 全栈领域优质创作者 2023-09-24 16:00
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    引用 皆我百晓生 小程序回复内容作答:
    问题1:如何将resnet50中的卷积替换为微ODConv动态卷积?

    要将resnet50中的卷积替换为微ODConv动态卷积,可以按照以下步骤进行操作:

    1. 导入需要的库和模块:

      import torch.nn as nn
      
    2. 定义一个新的微ODConv动态卷积层:

      class ODConv(nn.Module):
          def __init__(self, inplanes, planes, stride=1, dilation=1):
              super(ODConv, self).__init__()
              self.conv = nn.Conv2d(inplanes, planes, kernel_size=3, stride=stride, padding=dilation, bias=False, dilation=dilation)
              self.bn = nn.BatchNorm2d(planes)
              self.relu = nn.ReLU(inplace=True)
          
          def forward(self, x):
              out = self.conv(x)
              out = self.bn(out)
              out = self.relu(out)
              return out
      
    3. 更新ResNet中的Bottleneck类,将conv2替换为ODConv:

      class Bottleneck(nn.Module):
          expansion = 4
      
          def __init__(self, inplanes, planes, stride=1, downsample=None, dilation=1):
              super(Bottleneck, self).__init__()
              self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
              self.bn1 = nn.BatchNorm2d(planes)
              self.conv2 = ODConv(planes, planes, stride=stride, dilation=dilation)
              self.bn2 = nn.BatchNorm2d(planes)
              self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)
              self.bn3 = nn.BatchNorm2d(planes * 4)
              self.relu = nn.ReLU(inplace=True)
              self.downsample = downsample
              self.stride = stride
              
          ...
      
    4. 更新ResNet中的forward函数,确保新的ODConv层被正确调用:

      def forward(self, x):
          ...
          out = self.conv2(out)
          out = self.bn2(out)
          out = self.relu(out)
          ...
      

    这样就成功将resnet50中的卷积替换为微ODConv动态卷积。

    问题2:如果将修改后的网络训练得到的model.pth用于预测,是否会因为模型结构的变化而导致尺寸不匹配的问题。怎么解决?

    是的,由于模型结构的变化,训练得到的model.pth在预测时可能会导致尺寸不匹配的问题。解决这个问题的方法是加载预训练权重时,只复制可用的参数,并将其他参数自定义初始化。

    具体来说,可以按照以下步骤进行操作:

    1. 在加载预训练权重之前,为新添加的ODConv层定义自定义初始化方法:

      def init_odconv_weights(module):
          if isinstance(module, nn.Conv2d):
              nn.init.kaiming_normal_(module.weight, mode='fan_out', nonlinearity='relu')
          elif isinstance(module, nn.BatchNorm2d):
              nn.init.constant_(module.weight, 1)
              nn.init.constant_(module.bias, 0)
      
    2. 在加载预训练权重时,使用自定义的初始化方法,只复制可用参数:

      def load_pretrained_weights(model):
          pretrained_dict = model_zoo.load_url(model_urls['resnet50'])
          model_dict = model.state_dict()
          pretrained_dict = {k: v for k, v in pretrained_dict.items() if k in model_dict and v.size() == model_dict[k].size()}
          model_dict.update(pretrained_dict)
          model.load_state_dict(model_dict)
      
          model.apply(init_odconv_weights)
      

    通过这种方法,即只复制可用的参数,并为新添加的ODConv层自定义初始化,可以解决训练得到的model.pth尺寸不匹配的问题,使模型能够成功预测。

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问题事件

  • 系统已结题 10月2日
  • 修改了问题 9月24日
  • 创建了问题 9月24日

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