Using Ragged Tensors in Conv2D

Question

I am trying to use Ragged Tensors for a Convolution model. The input tensors have a shape of (None, None, 8) excluding the batch size and 'None' represents the ragged dimension

Here is a simple model for taking in Ragged Tensor inputs. The tensors are of dtype 'tf.float32'

m = Input(shape = (None, None, 8), ragged = True)
m1 = Conv2D(16, 3, strides = 1)(m)

Model(inputs = m, outputs = m1)

But it shows an error as below

TypeError                                 Traceback (most recent call last)
C:\Users\VAISHN~1\AppData\Local\Temp/ipykernel_15148/4293567980.py in 
      1 m = Input(shape = (None, None, 8), ragged = True)
----> 2 m1 = Conv2D(16, 3, strides = 1)(m)
      3 
      4 Model(inputs = m, outputs = m1)

~\AppData\Roaming\Python\Python38\site-packages\keras\utils\traceback_utils.py in error_handler(*args, **kwargs)
     68             # To get the full stack trace, call:
     69             # `tf.debugging.disable_traceback_filtering()`
---> 70             raise e.with_traceback(filtered_tb) from None
     71         finally:
     72             del filtered_tb

~\AppData\Roaming\Python\Python38\site-packages\tensorflow\python\framework\tensor_util.py in make_tensor_proto(values, dtype, shape, verify_shape, allow_broadcast)
    544       str_values = [compat.as_bytes(x) for x in proto_values]
    545     except TypeError:
--> 546       raise TypeError(f"Failed to convert elements of {values} to Tensor. "
    547                       "Consider casting elements to a supported type. See "
    548                       "https://www.tensorflow.org/api_docs/python/tf/dtypes "

TypeError: Exception encountered when calling layer "conv2d" (type Conv2D).

Failed to convert elements of tf.RaggedTensor(values=tf.RaggedTensor(values=Tensor("Placeholder:0", shape=(None, 8), dtype=float32), row_splits=Tensor("Placeholder_1:0", shape=(None,), dtype=int64)), row_splits=Tensor("Placeholder_2:0", shape=(None,), dtype=int64)) to Tensor. Consider casting elements to a supported type. See https://www.tensorflow.org/api_docs/python/tf/dtypes for supported TF dtypes.

Call arguments received by layer "conv2d" (type Conv2D):
  • inputs=tf.RaggedTensor(values=tf.RaggedTensor(values=Tensor("Placeholder:0", shape=(None, 8), dtype=float32), row_splits=Tensor("Placeholder_1:0", shape=(None,), dtype=int64)), row_splits=Tensor("Placeholder_2:0", shape=(None,), dtype=int64))

I also checked if Conv2D supports Ragged Tensors. Below is the class definition of Conv2D from tensorflow

class Conv2D(Conv):
  """2D convolution layer (e.g. spatial convolution over images).

  This layer creates a convolution kernel that is convolved
  with the layer input to produce a tensor of
  outputs. If `use_bias` is True,
  a bias vector is created and added to the outputs. Finally, if
  `activation` is not `None`, it is applied to the outputs as well.

  When using this layer as the first layer in a model,
  provide the keyword argument `input_shape`
  (tuple of integers or `None`, does not include the sample axis),
  e.g. `input_shape=(128, 128, 3)` for 128x128 RGB pictures
  in `data_format="channels_last"`. You can use `None` when
  a dimension has variable size.

Could someone please explain what the issue is? Ragged Tensors are compatible with other layers like Dense and LSTM but I am not sure why it shows this error in Conv2D