# -*- coding: utf-8 -*-
from typing import Callable, Optional, Sequence
import brainpy.math as bm
from brainpy._src.dnn.base import Layer
__all__ = [
'Activation',
'Flatten',
'Unflatten',
'FunAsLayer',
]
[docs]
class Activation(Layer):
r"""Applies an activation function to the inputs
Parameters:
----------
activate_fun: Callable, function
The function of Activation
name: str, Optional
The name of the object
mode: Mode
Enable training this node or not. (default True).
"""
update_style = 'x'
def __init__(
self,
activate_fun: Callable,
name: Optional[str] = None,
mode: bm.Mode = None,
**kwargs,
):
super().__init__(name, mode)
self.activate_fun = activate_fun
self.kwargs = kwargs
[docs]
def update(self, *args, **kwargs):
return self.activate_fun(*args, **kwargs, **self.kwargs)
[docs]
class Flatten(Layer):
r"""
Flattens a contiguous range of dims into a tensor. For use with :class:`~nn.Sequential`.
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).
name: str, Optional. The name of the object.
mode: Mode. Enable training this node or not. (default True).
Examples::
>>> import brainpy.math as bm
>>> inp = bm.random.randn(32, 1, 5, 5)
>>> # With default parameters
>>> m = Flatten()
>>> output = m(inp)
>>> output.shape
(32, 25)
>>> # With non-default parameters
>>> m = Flatten(0, 2)
>>> output = m(inp)
>>> output.shape
(160, 5)
"""
def __init__(
self,
start_dim: int = 0,
end_dim: int = -1,
name: Optional[str] = None,
mode: bm.Mode = None,
):
super().__init__(name, mode)
self.start_dim = start_dim
self.end_dim = end_dim
[docs]
def update(self, x):
if self.mode.is_child_of(bm.BatchingMode):
start_dim = (self.start_dim + 1) if self.start_dim >= 0 else (x.ndim + self.start_dim + 1)
else:
start_dim = self.start_dim if self.start_dim >= 0 else x.ndim + self.start_dim
return bm.flatten(x, start_dim, self.end_dim)
def __repr__(self) -> str:
return f'{self.__class__.__name__}(start_dim={self.start_dim}, end_dim={self.end_dim})'
[docs]
class Unflatten(Layer):
r"""
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: int, Dimension to be unflattened.
sizes: Sequence of int. New shape of the unflattened dimension.
Examples:
>>> import brainpy as bp
>>> import brainpy.math as bm
>>> input = bm.random.randn(2, 50)
>>> # With tuple of ints
>>> m = bp.Sequential(
>>> bp.dnn.Linear(50, 50),
>>> Unflatten(1, (2, 5, 5))
>>> )
>>> output = m(input)
>>> output.shape
(2, 2, 5, 5)
>>> # With torch.Size
>>> m = bp.Sequential(
>>> bp.dnn.Linear(50, 50),
>>> Unflatten(1, [2, 5, 5])
>>> )
>>> output = m(input)
>>> output.shape
(2, 2, 5, 5)
"""
def __init__(self, dim: int, sizes: Sequence[int], mode: bm.Mode = None, name: str = None) -> None:
super().__init__(mode=mode, name=name)
self.dim = dim
self.sizes = sizes
if isinstance(sizes, (tuple, list)):
for idx, elem in enumerate(sizes):
if not isinstance(elem, int):
raise TypeError("unflattened_size must be tuple of ints, " +
"but found element of type {} at pos {}".format(type(elem).__name__, idx))
else:
raise TypeError("unflattened_size must be tuple or list, but found type {}".format(type(sizes).__name__))
[docs]
def update(self, x):
dim = self.dim + 1 if self.mode.is_batch_mode() else self.dim
return bm.unflatten(x, dim, self.sizes)
def __repr__(self):
return f'{self.__class__.__name__}(dim={self.dim}, sizes={self.sizes})'
[docs]
class FunAsLayer(Layer):
def __init__(
self,
fun: Callable,
name: Optional[str] = None,
mode: bm.Mode = None,
**kwargs,
):
super().__init__(name, mode)
self._fun = fun
self.kwargs = kwargs
[docs]
def update(self, *args, **kwargs):
return self._fun(*args, **kwargs, **self.kwargs)
