Error on running on my Jupyter notebook

[salimcodes]

When I tried to run the example notebook, it returned an error.

This error was specifically on the x_stats = dec(z).float() line.

[Arkitu]

I have the same error, with 'Upsample' object has no attribute 'recompute_scale_factor'

[duanjiding]

me too

[hejonathan]

me too

[alosdiallo]

I am getting something similar but get a warning first:
:28: DeprecationWarning: LANCZOS is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.LANCZOS instead.
img = TF.resize(img, s, interpolation=PIL.Image.LANCZOS)
/Users/adiallo/opt/miniconda3/lib/python3.8/site-packages/torchvision/transforms/functional.py:417: UserWarning: Argument 'interpolation' of type int is deprecated since 0.13 and will be removed in 0.15. Please use InterpolationMode enum.
warnings.warn(

Then I get: AttributeError: 'Upsample' object has no attribute 'recompute_scale_factor'

[wzhao6898]

Error message I got:
AttributeError Traceback (most recent call last)
Input In [15], 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 ~\Anaconda3\lib\site-packages\torch\nn\modules\module.py:1130, in Module._call_impl(self, *input, **kwargs)
1126 # If we don't have any hooks, we want to skip the rest of the logic in
1127 # this function, and just call forward.
1128 if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks
1129 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1130 return forward_call(*input, **kwargs)
1131 # Do not call functions when jit is used
1132 full_backward_hooks, non_full_backward_hooks = [], []

File ~\Anaconda3\lib\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 ~\Anaconda3\lib\site-packages\torch\nn\modules\module.py:1130, in Module._call_impl(self, *input, **kwargs)
1126 # If we don't have any hooks, we want to skip the rest of the logic in
1127 # this function, and just call forward.
1128 if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks
1129 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1130 return forward_call(*input, **kwargs)
1131 # Do not call functions when jit is used
1132 full_backward_hooks, non_full_backward_hooks = [], []

File ~\Anaconda3\lib\site-packages\torch\nn\modules\container.py:139, in Sequential.forward(self, input)
137 def forward(self, input):
138 for module in self:
--> 139 input = module(input)
140 return input

File ~\Anaconda3\lib\site-packages\torch\nn\modules\module.py:1130, in Module._call_impl(self, *input, **kwargs)
1126 # If we don't have any hooks, we want to skip the rest of the logic in
1127 # this function, and just call forward.
1128 if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks
1129 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1130 return forward_call(*input, **kwargs)
1131 # Do not call functions when jit is used
1132 full_backward_hooks, non_full_backward_hooks = [], []

File ~\Anaconda3\lib\site-packages\torch\nn\modules\container.py:139, in Sequential.forward(self, input)
137 def forward(self, input):
138 for module in self:
--> 139 input = module(input)
140 return input

File ~\Anaconda3\lib\site-packages\torch\nn\modules\module.py:1130, in Module._call_impl(self, *input, **kwargs)
1126 # If we don't have any hooks, we want to skip the rest of the logic in
1127 # this function, and just call forward.
1128 if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks
1129 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1130 return forward_call(*input, **kwargs)
1131 # Do not call functions when jit is used
1132 full_backward_hooks, non_full_backward_hooks = [], []

File ~\Anaconda3\lib\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 ~\Anaconda3\lib\site-packages\torch\nn\modules\module.py:1207, in Module.getattr(self, name)
1205 if name in modules:
1206 return modules[name]
-> 1207 raise AttributeError("'{}' object has no attribute '{}'".format(
1208 type(self).name, name))

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

[Naphier]

ultralytics/yolov5#6948 (comment)

Find E:\condaaa\Lib\site-packages\torch\nn\modules\upsampling.py and change to this:

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

Commenting out this optional parameter was a quick fix for me but I think that pinning versions to this:

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

is what's really appropriate. I'll make a PR with the versions pinned after testing.

[digitalShaman]

The error
'Upsample' object has no attribute 'recompute_scale_factor'

is related to a change in the torch Upscale class from 1.10 to 1.11.

It appears that 'old' Upscale objects are saved within the model after this line of code:
model = load_model("https://cdn.openai.com/dall-e/decoder.pkl", 'cuda')
i used the following code immediatly after the load_model call to patch this:

# Patch for torch 1.11 and higher: replace the old Upsample object by the new version
# that exposes recompute_scale_factor
_ = model.blocks.group_1.upsample
model.blocks.group_1.upsample = torch.nn.Upsample(scale_factor = _.scale_factor, mode= _.mode)
_ = model.blocks.group_2.upsample
model.blocks.group_2.upsample = torch.nn.Upsample(scale_factor = _.scale_factor, mode= _.mode)
_ = model.blocks.group_3.upsample
model.blocks.group_3.upsample = torch.nn.Upsample(scale_factor = _.scale_factor, mode= _.mode)

and it's running fine with torch 1.12.1!