Conv3DTranspose issue with torch backend padding arguments (padding=same) and output_padding=1) lead to a Torch output_padding (1) that is greater than strides (1).

[pure-rgb]

I ended up with the following issue keras-team/keras-core#774

Currently a warning is given but what is the solution or workaround?

ValueError: Exception encountered when calling Conv3DTranspose.call().

The padding arguments (padding=same) and output_padding=1) lead to a Torch output_padding (1) that is greater than strides (1). This is not supported. You can change the padding arguments, kernel or stride, or run on another backend.

Arguments received by Conv3DTranspose.call():
• inputs=torch.Tensor(shape=torch.Size([16, 96, 6, 6, 128]), dtype=float32)

[dhantule]

Hi @pure-rgb, thanks for reporting this. Could you provide some reproducible code?

[pure-rgb]

@dhantule

Sample code.

import  os
os.environ["KERAS_BACKEND"] = "torch"

import numpy as np
import keras

a = np.ones((1, 8, 8, 8, 16), dtype=np.float32)
x = keras.layers.Conv3DTranspose(
    32,
    kernel_size=2,
    strides=1,
    padding='same',
)
output = x(a)
print(output.shape)

This works in tf and jax, output: (1, 8, 8, 8, 32). But in torch backend, it gives

/opt/conda/lib/python3.10/site-packages/keras/src/backend/common/backend_utils.py:91: UserWarning: You might experience inconsistencies across backends when calling conv transpose with kernel_size=2, stride=1, dilation_rate=1, padding=same, output_padding=1.
  warnings.warn(
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
Cell In[2], line 8
      1 a = np.ones((1, 8, 8, 8, 16), dtype=np.float32)
      2 x = keras.layers.Conv3DTranspose(
      3     32,
      4     kernel_size=2,
      5     strides=1,
      6     padding='same',
      7 )
----> 8 output = x(a)
      9 print(output.shape)

File /opt/conda/lib/python3.10/site-packages/keras/src/utils/traceback_utils.py:122, in filter_traceback.<locals>.error_handler(*args, **kwargs)
    119     filtered_tb = _process_traceback_frames(e.__traceback__)
    120     # To get the full stack trace, call:
    121     # `keras.config.disable_traceback_filtering()`
--> 122     raise e.with_traceback(filtered_tb) from None
    123 finally:
    124     del filtered_tb

File /opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py:1518, in Module._wrapped_call_impl(self, *args, **kwargs)
   1516     return self._compiled_call_impl(*args, **kwargs)  # type: ignore[misc]
   1517 else:
-> 1518     return self._call_impl(*args, **kwargs)

File /opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py:1527, in Module._call_impl(self, *args, **kwargs)
   1522 # If we don't have any hooks, we want to skip the rest of the logic in
   1523 # this function, and just call forward.
   1524 if not (self._backward_hooks or self._backward_pre_hooks or self._forward_hooks or self._forward_pre_hooks
   1525         or _global_backward_pre_hooks or _global_backward_hooks
   1526         or _global_forward_hooks or _global_forward_pre_hooks):
-> 1527     return forward_call(*args, **kwargs)
   1529 try:
   1530     result = None

ValueError: Exception encountered when calling Conv3DTranspose.call().

The padding arguments (padding=same) and output_padding=1) lead to a Torch output_padding (1) that is greater than strides (1). This is not supported. You can change the padding arguments, kernel or stride, or run on another backend. 

Arguments received by Conv3DTranspose.call():
  • inputs=torch.Tensor(shape=torch.Size([1, 8, 8, 8, 16]), dtype=float32)