o h@s\ddlmZddlmZddlmZddlmZddgZGdddeZ Gd ddeZ d S) )Union)Tensor)_size)ModuleFlatten UnflattencsjeZdZUdZddgZeed<eed<ddededdffdd Zd edefd d Z de fd dZ Z S)ra Flattens a contiguous range of dims into a tensor. For use with :class:`~nn.Sequential`, see :meth:`torch.flatten` for details. Shape: - Input: :math:`(*, S_{\text{start}},..., S_{i}, ..., S_{\text{end}}, *)`,' where :math:`S_{i}` is the size at dimension :math:`i` and :math:`*` means any number of dimensions including none. - Output: :math:`(*, \prod_{i=\text{start}}^{\text{end}} S_{i}, *)`. Args: start_dim: first dim to flatten (default = 1). end_dim: last dim to flatten (default = -1). Examples:: >>> input = torch.randn(32, 1, 5, 5) >>> # With default parameters >>> m = nn.Flatten() >>> output = m(input) >>> output.size() torch.Size([32, 25]) >>> # With non-default parameters >>> m = nn.Flatten(0, 2) >>> output = m(input) >>> output.size() torch.Size([160, 5]) start_dimend_dimrreturnNcst||_||_dSN)super__init__r r )selfr r  __class__l/var/www/html/construction_image-detection-poc/venv/lib/python3.10/site-packages/torch/nn/modules/flatten.pyr/s  zFlatten.__init__inputcC||j|jSr )flattenr r rrrrrforward4zFlatten.forwardcCd|jd|jS)Nz start_dim=z , end_dim=)r r rrrr extra_repr7zFlatten.extra_repr)rr ) __name__ __module__ __qualname____doc__ __constants__int__annotations__rrrstrr __classcell__rrrrr s cseZdZUdZeeeefZddgZe eefe d<e e efe d<de eefde e efddffdd Z dd Z d d Zd edefd dZdefddZZS)ra Unflattens a tensor dim expanding it to a desired shape. For use with :class:`~nn.Sequential`. * :attr:`dim` specifies the dimension of the input tensor to be unflattened, and it can be either `int` or `str` when `Tensor` or `NamedTensor` is used, respectively. * :attr:`unflattened_size` is the new shape of the unflattened dimension of the tensor and it can be a `tuple` of ints or a `list` of ints or `torch.Size` for `Tensor` input; a `NamedShape` (tuple of `(name, size)` tuples) for `NamedTensor` input. Shape: - Input: :math:`(*, S_{\text{dim}}, *)`, where :math:`S_{\text{dim}}` is the size at dimension :attr:`dim` and :math:`*` means any number of dimensions including none. - Output: :math:`(*, U_1, ..., U_n, *)`, where :math:`U` = :attr:`unflattened_size` and :math:`\prod_{i=1}^n U_i = S_{\text{dim}}`. Args: dim (Union[int, str]): Dimension to be unflattened unflattened_size (Union[torch.Size, Tuple, List, NamedShape]): New shape of the unflattened dimension Examples: >>> input = torch.randn(2, 50) >>> # With tuple of ints >>> m = nn.Sequential( >>> nn.Linear(50, 50), >>> nn.Unflatten(1, (2, 5, 5)) >>> ) >>> output = m(input) >>> output.size() torch.Size([2, 2, 5, 5]) >>> # With torch.Size >>> m = nn.Sequential( >>> nn.Linear(50, 50), >>> nn.Unflatten(1, torch.Size([2, 5, 5])) >>> ) >>> output = m(input) >>> output.size() torch.Size([2, 2, 5, 5]) >>> # With namedshape (tuple of tuples) >>> input = torch.randn(2, 50, names=('N', 'features')) >>> unflatten = nn.Unflatten('features', (('C', 2), ('H', 5), ('W', 5))) >>> output = unflatten(input) >>> output.size() torch.Size([2, 2, 5, 5]) dimunflattened_sizer NcsNtt|tr||nt|tr||ntd||_||_ dS)Nz'invalid argument type for dim parameter) rr isinstancer$_require_tuple_intr&_require_tuple_tuple TypeErrorr(r))rr(r)rrrrps      zUnflatten.__init__cCs`t|tr$t|D]\}}t|ts!tddt|jd|q dStddt|j)Nz*unflattened_size must be tuple of tuples, but found element of type  at pos z,unflattened_size must be a tuple of tuples, zbut found type )r*tuple enumerater-typerrridxelemrrrr,s  zUnflatten._require_tuple_tuplecCs`t|ttfr&t|D]\}}t|ts#tddt|jd|q dStdt|j)Nz(unflattened_size must be tuple of ints, r.r/z9unflattened_size must be a tuple of ints, but found type )r*r0listr1r$r-r2rr3rrrr+s zUnflatten._require_tuple_intrcCrr ) unflattenr(r)rrrrrrzUnflatten.forwardcCr)Nzdim=z, unflattened_size=)r(r)rrrrrrzUnflatten.extra_repr)rr r!r"r0r&r$ NamedShaper#rr%rrr,r+rrrr'rrrrr;s" .   N) typingrtorchr torch.typesrmoduler__all__rrrrrrs    .