returnn.tensor.dim#
Represents a dimension of a tensor. A dimension can come with further information such as individual sequence lengths.
This identifies one axis/dimension, like a time-dimension, etc.
This was called DimensionTag earlier, and referred to as dimension tag.
This is used by Tensor (earlier Data). See Tensor.dims().
This would be passed as dims when creating a Tensor instance.
It is not to specify the specific axis in a specific Tensor, but to specify the content and dimension. I.e. if we have the same Dim for two Data instances, the dimensions should match. I.e.:
- data1.dims[i] == data2.dims[j]
=> data1.raw_tensor.shape[i] == data2.raw_tensor.shape[j]
This also includes further information such as sequence lengths or a vocabulary.
Deprecated: We differentiate between the batch dim, spatial dim or feature dim, although that is just flag and in many contexts there is no real difference between a spatial dim and a feature dim (the batch dim is often handled differently).
- class returnn.tensor.dim.Dim(dimension: int | Tensor | None, *, name: str | None = None, capacity: int | None = None, dyn_size_ext: Tensor | None = None, description: str | None = None, **kwargs)[source]#
Represents a dimension of a tensor. This potentially comes with further information such as individual sequence lengths. See the module docstring.