def check_input(self, input: Tensor,
batch_sizes: Optional[Tensor]) -> None:
expected_input_dim = 2 if batch_sizes is not None else 3
if input.dim() != expected_input_dim:
raise RuntimeError('input must have {} dimensions, got {}'.format(
expected_input_dim, input.dim()))
if self.input_size != input.size(-1):
raise RuntimeError(
'input.size(-1) must be equal to input_size. Expected {}, got {}'
.format(self.input_size, input.size(-1)))
def from_pretrained(cls, embeddings: Tensor, freeze: bool = True, max_norm: Optional[float] = None,
norm_type: float = 2., scale_grad_by_freq: bool = False,
mode: str = 'mean', sparse: bool = False, include_last_offset: bool = False) -> 'EmbeddingBag':
r"""Creates EmbeddingBag instance from given 2-dimensional FloatTensor.
Args:
embeddings (Tensor): FloatTensor containing weights for the EmbeddingBag.
First dimension is being passed to EmbeddingBag as 'num_embeddings', second as 'embedding_dim'.
freeze (boolean, optional): If ``True``, the tensor does not get updated in the learning process.
Equivalent to ``embeddingbag.weight.requires_grad = False``. Default: ``True``
max_norm (float, optional): See module initialization documentation. Default: ``None``
norm_type (float, optional): See module initialization documentation. Default ``2``.
scale_grad_by_freq (boolean, optional): See module initialization documentation. Default ``False``.
mode (string, optional): See module initialization documentation. Default: ``"mean"``
sparse (bool, optional): See module initialization documentation. Default: ``False``.
include_last_offset (bool, optional): See module initialization documentation. Default: ``False``.
Examples::
>>> # FloatTensor containing pretrained weights
>>> weight = torch.FloatTensor([[1, 2.3, 3], [4, 5.1, 6.3]])
>>> embeddingbag = nn.EmbeddingBag.from_pretrained(weight)
>>> # Get embeddings for index 1
>>> input = torch.LongTensor([[1, 0]])
>>> embeddingbag(input)
tensor([[ 2.5000, 3.7000, 4.6500]])
"""
assert embeddings.dim() == 2, \
'Embeddings parameter is expected to be 2-dimensional'
rows, cols = embeddings.shape
embeddingbag = cls(
num_embeddings=rows,
embedding_dim=cols,
_weight=embeddings,
max_norm=max_norm,
norm_type=norm_type,
scale_grad_by_freq=scale_grad_by_freq,
mode=mode,
sparse=sparse,
include_last_offset=include_last_offset)
embeddingbag.weight.requires_grad = not freeze
return embeddingbag