import torchvision
model = torchvision.models.resnet50(pretrained=True)

model = torchvision.models.resnet50(pretrained=False)

model = torchvision.models.densenet169(pretrained=False)

model = torchvision.models.densenet169()

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-b121ed2d.pth',
}

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 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 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

class ResNet(nn.Module):

 def __init__(self, block, layers, num_classes=1000):
 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=2)
 self.avgpool = nn.AvgPool2d(7, stride=1)
 self.fc = nn.Linear(512 * block.expansion, num_classes)

 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):
 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))
 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

class Bottleneck(nn.Module):
 expansion = 4

 def __init__(self, inplanes, planes, stride=1, downsample=None):
 super(Bottleneck, self).__init__()
 self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
 self.bn1 = nn.BatchNorm2d(planes)
 self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride,
    padding=1, bias=False)
 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

def conv3x3(in_planes, out_planes, stride=1):
 """3x3 convolution with padding"""
 return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
   padding=1, 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.BatchNorm2d(planes)
 self.relu = nn.ReLU(inplace=True)
 self.conv2 = conv3x3(planes, planes)
 self.bn2 = nn.BatchNorm2d(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

def load_url(url, model_dir=None, map_location=None, progress=True):
 r"""Loads the Torch serialized object at the given URL.

 If the object is already present in `model_dir`, it's deserialized and
 returned. The filename part of the URL should follow the naming convention
 ``filename-<sha256>.ext`` where ``<sha256>`` is the first eight or more
 digits of the SHA256 hash of the contents of the file. The hash is used to
 ensure unique names and to verify the contents of the file.

 The default value of `model_dir` is ``$TORCH_HOME/models`` where
 ``$TORCH_HOME`` defaults to ``~/.torch``. The default directory can be
 overriden with the ``$TORCH_MODEL_ZOO`` environment variable.

 Args:
 url (string): URL of the object to download
 model_dir (string, optional): directory in which to save the object
 map_location (optional): a function or a dict specifying how to remap storage locations (see torch.load)
 progress (bool, optional): whether or not to display a progress bar to stderr

 Example:
 >>> state_dict = torch.utils.model_zoo.load_url('https://s3.amazonaws.com/pytorch/models/resnet18-5c106cde.pth')

 """
 if model_dir is None:
 torch_home = os.path.expanduser(os.getenv('TORCH_HOME', '~/.torch'))
 model_dir = os.getenv('TORCH_MODEL_ZOO', os.path.join(torch_home, 'models'))
 if not os.path.exists(model_dir):
 os.makedirs(model_dir)
 parts = urlparse(url)
 filename = os.path.basename(parts.path)
 cached_file = os.path.join(model_dir, filename)
 if not os.path.exists(cached_file):
 sys.stderr.write('Downloading: "{}" to {}\n'.format(url, cached_file))
 hash_prefix = HASH_REGEX.search(filename).group(1)
 _download_url_to_file(url, cached_file, hash_prefix, progress=progress)
 return torch.load(cached_file, map_location=map_location)