When trying to load a model I trained back in pytorch 1.0 in pytorch 1.1, I get the following warning :
/home/user/miniconda3/envs/crystal_clear/lib/python3.6/site-packages/torch/serialization.py:454: SourceChangeWarning: source code of class 'torch.nn.modules.conv.Conv2d' has changed. you can retrieve the original source code by accessing the object's source attribute or set `torch.nn.Module.dump_patches = True` and use the patch tool to revert the changes.
  warnings.warn(msg, SourceChangeWarning)
/home/user/miniconda3/envs/crystal_clear/lib/python3.6/site-packages/torch/serialization.py:454: SourceChangeWarning: source code of class 'torch.nn.modules.batchnorm.BatchNorm2d' has changed. you can retrieve the original source code by accessing the object's source attribute or set `torch.nn.Module.dump_patches = True` and use the patch tool to revert the changes.
  warnings.warn(msg, SourceChangeWarning)
/home/user/miniconda3/envs/crystal_clear/lib/python3.6/site-packages/torch/serialization.py:454: SourceChangeWarning: source code of class 'torch.nn.modules.activation.ReLU' has changed. you can retrieve the original source code by accessing the object's source attribute or set `torch.nn.Module.dump_patches = True` and use the patch tool to revert the changes.
  warnings.warn(msg, SourceChangeWarning)
/home/user/miniconda3/envs/crystal_clear/lib/python3.6/site-packages/torch/serialization.py:454: SourceChangeWarning: source code of class 'torch.nn.modules.container.ModuleList' has changed. you can retrieve the original source code by accessing the object's source attribute or set `torch.nn.Module.dump_patches = True` and use the patch tool to revert the changes.
  warnings.warn(msg, SourceChangeWarning)
/home/user/miniconda3/envs/crystal_clear/lib/python3.6/site-packages/torch/serialization.py:454: SourceChangeWarning: source code of class 'torchvision.models.resnet.BasicBlock' has changed. you can retrieve the original source code by accessing the object's source attribute or set `torch.nn.Module.dump_patches = True` and use the patch tool to revert the changes.
  warnings.warn(msg, SourceChangeWarning)
/home/user/miniconda3/envs/crystal_clear/lib/python3.6/site-packages/torch/serialization.py:454: SourceChangeWarning: source code of class 'torch.nn.modules.pixelshuffle.PixelShuffle' has changed. you can retrieve the original source code by accessing the object's source attribute or set `torch.nn.Module.dump_patches = True` and use the patch tool to revert the changes.
  warnings.warn(msg, SourceChangeWarning)
/home/user/miniconda3/envs/crystal_clear/lib/python3.6/site-packages/torch/serialization.py:454: SourceChangeWarning: source code of class 'torch.nn.modules.padding.ReplicationPad2d' has changed. you can retrieve the original source code by accessing the object's source attribute or set `torch.nn.Module.dump_patches = True` and use the patch tool to revert the changes.
  warnings.warn(msg, SourceChangeWarning)

That warning translate into an error when trying to use the model :
~/miniconda3/envs/crystal_clear/lib/python3.6/site-packages/torch/nn/modules/module.py in __call__(self, *input, **kwargs)
    491             result = self._slow_forward(*input, **kwargs)
    492         else:
--> 493             result = self.forward(*input, **kwargs)
    494         for hook in self._forward_hooks.values():
    495             hook_result = hook(self, input, result)

~/Documents/fastai/fastai/layers.py in forward(self, x)
    153         for l in self.layers:
    154             res.orig = x
--> 155             nres = l(res)
    156             # We have to remove res.orig to avoid hanging refs and therefore memory leaks
    157             res.orig = None

~/miniconda3/envs/crystal_clear/lib/python3.6/site-packages/torch/nn/modules/module.py in __call__(self, *input, **kwargs)
    491             result = self._slow_forward(*input, **kwargs)
    492         else:
--> 493             result = self.forward(*input, **kwargs)
    494         for hook in self._forward_hooks.values():
    495             hook_result = hook(self, input, result)

~/miniconda3/envs/crystal_clear/lib/python3.6/site-packages/torch/nn/modules/container.py in forward(self, input)
     90     def forward(self, input):
     91         for module in self._modules.values():
---> 92             input = module(input)
     93         return input
     94 

~/miniconda3/envs/crystal_clear/lib/python3.6/site-packages/torch/nn/modules/module.py in __call__(self, *input, **kwargs)
    491             result = self._slow_forward(*input, **kwargs)
    492         else:
--> 493             result = self.forward(*input, **kwargs)
    494         for hook in self._forward_hooks.values():
    495             hook_result = hook(self, input, result)

~/miniconda3/envs/crystal_clear/lib/python3.6/site-packages/torch/nn/modules/conv.py in forward(self, input)
    329     @weak_script_method
    330     def forward(self, input):
--> 331         if self.padding_mode == 'circular':
    332             expanded_padding = ((self.padding[1] + 1) // 2, self.padding[1] // 2,
    333                                 (self.padding[0] + 1) // 2, self.padding[0] // 2)

~/miniconda3/envs/crystal_clear/lib/python3.6/site-packages/torch/nn/modules/module.py in __getattr__(self, name)
    537                 return modules[name]
    538         raise AttributeError("'{}' object has no attribute '{}'".format(
--> 539             type(self).__name__, name))
    540 
    541     def __setattr__(self, name, value):

AttributeError: 'Conv2d' object has no attribute 'padding_mode'

From what I understand, I can revert the source with the patch tool to be able to use my old model. Is there a way to convert the model to pytorch 1.1?