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 ?