PyTorch: "ValueError: can't optimize a non-leaf Tensor" after changing pretrained model from 3 RGB Channels to 4 Channels

Question

I have been trying to change the pretrained PyTorch Densenet's first conv layer from 3 channels to 4 channels while maintaining its original RGB channel's pretrained weights. I have done the following codes, but the optimizer part throws me this error: "ValueError: can't optimize a non-leaf Tensor" .

import torchvision.models as models
import torch.nn as nn
backbone = models.__dict__['densenet169'](pretrained=True)


weight1 = backbone.features.conv0.weight.data.clone()
new_first_layer  = nn.Conv2d(4, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)
with torch.no_grad():
    new_first_layer.weight[:,:3] = weight1

backbone.features.conv0 = new_first_layer
optimizer = torch.optim.SGD(backbone.parameters(), 0.001,
                                 weight_decay=0.1)  # Changing this optimizer from SGD to ADAM

I have also tried to remove the argument with torch.no_grad(): but this issue still remains:

  ValueError                                Traceback (most recent call last)
<ipython-input-343-5fc87352da04> in <module>()
     11 backbone.features.conv0 = new_first_layer
     12 optimizer = torch.optim.SGD(res.parameters(), 0.001,
---> 13                                  weight_decay=0.1)  # Changing this optimizer from SGD to ADAM

~/anaconda3/envs/detectron2/lib/python3.6/site-packages/torch/optim/sgd.py in __init__(self, params, lr, momentum, dampening, weight_decay, nesterov)
     66         if nesterov and (momentum <= 0 or dampening != 0):
     67             raise ValueError("Nesterov momentum requires a momentum and zero dampening")
---> 68         super(SGD, self).__init__(params, defaults)
     69 
     70     def __setstate__(self, state):

~/anaconda3/envs/detectron2/lib/python3.6/site-packages/torch/optim/optimizer.py in __init__(self, params, defaults)
     50 
     51         for param_group in param_groups:
---> 52             self.add_param_group(param_group)
     53 
     54     def __getstate__(self):

~/anaconda3/envs/detectron2/lib/python3.6/site-packages/torch/optim/optimizer.py in add_param_group(self, param_group)
    231                                 "but one of the params is " + torch.typename(param))
    232             if not param.is_leaf:
--> 233                 raise ValueError("can't optimize a non-leaf Tensor")
    234 
    235         for name, default in self.defaults.items():

ValueError: can't optimize a non-leaf Tensor

My PyTorch version is: 1.7.0.

Could you guys please help? Thanks alot!

Regards.

Answer 1

I think I have resolved this problem!:

import torchvision.models as models
import torch.nn as nn
from torch.autograd import Variable
backbone = models.__dict__['densenet169'](pretrained=True)
weight1 = backbone.features.conv0.weight.clone()
new_first_layer  = nn.Conv2d(4, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False).requires_grad_()
new_first_layer.weight[:,:3,:,:].data[...] =  Variable(weight1, requires_grad=True)
backbone.features.conv0 = new_first_layer
optimizer = torch.optim.SGD(res.parameters(), 0.001,
                                 weight_decay=0.1)