AttributeError: 'Upsample' object has no attribute 'recompute_scale_factor'

I get error on line x_stats = dec(z).float().

import torch.nn.functional as F
z_logits = enc(x)
z = torch.argmax(z_logits, axis=1)
z = F.one_hot(z, num_classes=enc.vocab_size).permute(0, 3, 1, 2).float()
x_stats = dec(z).float()
x_rec = unmap_pixels(torch.sigmoid(x_stats[:, :3]))
x_rec = T.ToPILImage(mode='RGB')(x_rec[0])
display_markdown('Reconstructed image:')
display(x_rec)

I tried to downgrade and reinstall the torch package but that didn't help the issue. My package version is torch==1.11.0

Full traceback:

AttributeError Traceback (most recent call last)
/Users/hanpham/github/DALL-E/notebooks/usage.ipynb Cell 4' in <cell line: 7>() 4 z = torch.argmax(z_logits, axis=1) 5 z = F.one_hot(z, num_classes=enc.vocab_size).permute(0, 3, 1, 2).float()
----> 7 x_stats = dec(z).float() 8 x_rec = unmap_pixels(torch.sigmoid(x_stats[:, :3])) 9 x_rec = T.ToPILImage(mode='RGB')(x_rec[0])
File /opt/homebrew/lib/python3.9/site-packages/torch/nn/modules/module.py:1110, in Module._call_impl(self, *input, **kwargs) 1106 # If we don't have any hooks, we want to skip the rest of the logic in 1107 # this function, and just call forward. 1108 if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks 1109 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1110 return forward_call(*input, **kwargs) 1111 # Do not call functions when jit is used 1112 full_backward_hooks, non_full_backward_hooks = [], []
File /opt/homebrew/lib/python3.9/site-packages/dall_e/decoder.py:94, in Decoder.forward(self, x) 91 if x.dtype != torch.float32: 92 raise ValueError('input must have dtype torch.float32')
---> 94 return self.blocks(x)
File /opt/homebrew/lib/python3.9/site-packages/torch/nn/modules/module.py:1110, in Module._call_impl(self, *input, **kwargs) 1106 # If we don't have any hooks, we want to skip the rest of the logic in 1107 # this function, and just call forward. 1108 if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks 1109 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1110 return forward_call(*input, **kwargs) 1111 # Do not call functions when jit is used 1112 full_backward_hooks, non_full_backward_hooks = [], []
File /opt/homebrew/lib/python3.9/site-packages/torch/nn/modules/container.py:141, in Sequential.forward(self, input) 139 def forward(self, input): 140 for module in self:
--> 141 input = module(input) 142 return input
File /opt/homebrew/lib/python3.9/site-packages/torch/nn/modules/module.py:1110, in Module._call_impl(self, *input, **kwargs) 1106 # If we don't have any hooks, we want to skip the rest of the logic in 1107 # this function, and just call forward. 1108 if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks 1109 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1110 return forward_call(*input, **kwargs) 1111 # Do not call functions when jit is used 1112 full_backward_hooks, non_full_backward_hooks = [], []
File /opt/homebrew/lib/python3.9/site-packages/torch/nn/modules/container.py:141, in Sequential.forward(self, input) 139 def forward(self, input): 140 for module in self:
--> 141 input = module(input) 142 return input
File /opt/homebrew/lib/python3.9/site-packages/torch/nn/modules/module.py:1110, in Module._call_impl(self, *input, **kwargs) 1106 # If we don't have any hooks, we want to skip the rest of the logic in 1107 # this function, and just call forward. 1108 if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks 1109 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1110 return forward_call(*input, **kwargs) 1111 # Do not call functions when jit is used 1112 full_backward_hooks, non_full_backward_hooks = [], []
File /opt/homebrew/lib/python3.9/site-packages/torch/nn/modules/upsampling.py:154, in Upsample.forward(self, input) 152 def forward(self, input: Tensor) -> Tensor: 153 return F.interpolate(input, self.size, self.scale_factor, self.mode, self.align_corners,
--> 154 recompute_scale_factor=self.recompute_scale_factor)
File /opt/homebrew/lib/python3.9/site-packages/torch/nn/modules/module.py:1185, in Module.__getattr__(self, name) 1183 if name in modules: 1184 return modules[name]
-> 1185 raise AttributeError("'{}' object has no attribute '{}'".format( 1186 type(self).__name__, name))
AttributeError: 'Upsample' object has no attribute 'recompute_scale_factor'

4 Answers

Install Torch version, this will solve the issue

pip install torchvision==0.10.1
pip install torch==1.9.1

I think your issue might be along the lines of .

I'm not familiar with pytorch; but suggestions were:

1. wait for the next version (not really that great a suggestion, sorry)

2. comment out the code as pointed in , that is:

In /opt/homebrew/lib/python3.9/site-packages/torch/nn/modules/upsampling.py in line 153-154:

Change:

 return F.interpolate(input, self.size, self.scale_factor, self.mode, self.align_corners,
recompute_scale_factor=self.recompute_scale_factor)

To:

 return F.interpolate(input, self.size, self.scale_factor, self.mode, self.align_corners)
# recompute_scale_factor=self.recompute_scale_factor)

or

 return F.interpolate(input, self.size, self.scale_factor, self.mode, self.align_corners,
# recompute_scale_factor=self.recompute_scale_factor
)

In my opinion, as a 'workaround', you could do the comment out option until a new version comes out at which, you can undo the comment out, and upgrade.

While I agree this is an 'answer', it isn't the perfect answer. My apologies.

Also getting this error with torch 1.11.0 Would love to hear how people solve it

Looks like it's an issue with 1.11.0:

Edit: Following these instructions solved it for me:

Comment out the recompute_scale_factor=self.recompute_scale_factor part in the source code. File /opt/homebrew/lib/python3.9/site-packages/torch/nn/modules/upsampling.py:154, in Upsample.forward(self, input)

152 def forward(self, input: Tensor) -> Tensor:
153 return F.interpolate(input, self.size, self.scale_factor, self.mode, self.align_corners)
154
#recompute_scale_factor=self.recompute_scale_factor)