Hi ,
I am really new to pytorch and deep-learning. I am exploring ResNet.
However , I am not able to import resnet in pytorch.
import torchvision.models as models
resnet152 = models.resnet152(pretrained=False)

is giving me errors 
TypeError                                 Traceback (most recent call last)
<ipython-input-4-c95471609672> in <module>()
----> 1 resnet152 = models.resnet152(pretrained=False)

~/pytorch-segmentation-detection/vision/torchvision/models/resnet.py in resnet152(pretrained, **kwargs)
    272         pretrained (bool): If True, returns a model pre-trained on ImageNet
    273     """
--> 274     model = ResNet(Bottleneck, [3, 8, 36, 3], **kwargs)
    275     if pretrained:
    276         model.load_state_dict(model_zoo.load_url(model_urls['resnet152']))

~/pytorch-segmentation-detection/vision/torchvision/models/resnet.py in __init__(self, block, layers, num_classes, fully_conv, remove_avg_pool_layer, output_stride)
    141         self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
    142 
--> 143         self.layer1 = self._make_layer(block, 64, layers[0])
    144         self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
    145         self.layer3 = self._make_layer(block, 256, layers[2], stride=2)

~/pytorch-segmentation-detection/vision/torchvision/models/resnet.py in _make_layer(self, block, planes, blocks, stride, dilation)
    189 
    190         layers = []
--> 191         layers.append(block(self.inplanes, planes, stride, downsample, dilation=self.current_dilation))
    192         self.inplanes = planes * block.expansion
    193         for i in range(1, blocks):

~/pytorch-segmentation-detection/vision/torchvision/models/resnet.py in __init__(self, inplanes, planes, stride, downsample, dilation)
     78         self.bn1 = nn.BatchNorm2d(planes)
     79 
---> 80         self.conv2 = conv3x3(planes, planes, stride=stride, dilation=dilation)
     81 
     82         #self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride,

~/pytorch-segmentation-detection/vision/torchvision/models/resnet.py in conv3x3(in_planes, out_planes, stride, dilation)
     35 
     36     return nn.Conv2d(in_planes, out_planes, kernel_size=kernel_size, stride=stride,
---> 37                      padding=full_padding, dilation=dilation, bias=False)
     38 
     39 

/opt/anaconda/lib/python3.6/site-packages/torch/nn/modules/conv.py in __init__(self, in_channels, out_channels, kernel_size, stride, padding, dilation, groups, bias)
    271         super(Conv2d, self).__init__(
    272             in_channels, out_channels, kernel_size, stride, padding, dilation,
--> 273             False, _pair(0), groups, bias)
    274 
    275     def forward(self, input):

/opt/anaconda/lib/python3.6/site-packages/torch/nn/modules/conv.py in __init__(self, in_channels, out_channels, kernel_size, stride, padding, dilation, transposed, output_padding, groups, bias)
     31         else:
     32             self.weight = Parameter(torch.Tensor(
---> 33                 out_channels, in_channels // groups, *kernel_size))
     34         if bias:
     35             self.bias = Parameter(torch.Tensor(out_channels))

TypeError: torch.FloatTensor constructor received an invalid combination of arguments - got (int, int, numpy.int64, numpy.int64), but expected one of:
 * no arguments
 * (int ...)
      didn't match because some of the arguments have invalid types: (int, int, numpy.int64, numpy.int64)
 * (torch.FloatTensor viewed_tensor)
 * (torch.Size size)
 * (torch.FloatStorage data)
 * (Sequence data)

Can anyone please help ?