class BiDense(nn.Cell):
r"""
The bilinear dense connected layer.
Applies dense connected layer for two inputs. This layer implements the operation as:
.. math::
\text{outputs} = \text{X_{1}}^{T} * \text{kernel} * \text{X_{2}} + \text{bias},
where :math:`X_{1}` is the first input tensor, math:`X_{2}` is the second input tensor
, :math:`\text{kernel}` is a weight matrix with the same data type as the :math:`X` created by the layer
, and :math:`\text{bias}` is a bias vector with the same data type as the :math:`X` created by the layer
(only if has_bias is True).
Args:
in1_channels (int): The number of channels in the input1 space.
in2_channels (int): The number of channels in the input2 space.
out_channels (int): The number of channels in the output space.
weight_init (Union[Tensor, str, Initializer, numbers.Number]): The trainable weight_init parameter. The dtype
is same as `x`. The values of str refer to the function `initializer`. Default: None.
bias_init (Union[Tensor, str, Initializer, numbers.Number]): The trainable bias_init parameter. The dtype is
same as `x`. The values of str refer to the function `initializer`. Default: None.
has_bias (bool): Specifies whether the layer uses a bias vector. Default: True.
Inputs:
- **input1** (Tensor) - Tensor of shape :math:`(*, in1\_channels)`. The `in_channels` in `Args` should be equal
to :math:`in1\_channels` in `Inputs`.
- **input2** (Tensor) - Tensor of shape :math:`(*, in2\_channels)`. The `in_channels` in `Args` should be equal
to :math:`in2\_channels` in `Inputs`.
Outputs:
Tensor of shape :math:`(*, out\_channels)`.
Raises:
TypeError: If `in1_channels`, `in2_channels` or `out_channels` is not an int.
TypeError: If `has_bias` is not a bool.
ValueError: If length of shape of `weight_init` is not equal to 3 or shape[0] of `weight_init`
is not equal to `out_channels` or shape[1] of `weight_init` is not equal to `in1_channels`
or shape[2] of `weight_init` is not equal to `in2_channels`.
ValueError: If length of shape of `bias_init` is not equal to 1
or shape[0] of `bias_init` is not equal to `out_channels`.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> x1 = Tensor(np.random.randn(128, 20), mindspore.float32)
>>> x2 = Tensor(np.random.randn(128, 30), mindspore.float32)
>>> net = nn.BiDense(20, 30, 40)
>>> output = net(x1, x2)
>>> print(output.shape)
(128, 40)
"""
def __init__(self,
in1_channels,
in2_channels,
out_channels,
weight_init=None,
bias_init=None,
has_bias=True):
super().__init__()
# self.in_channels = Validator.check_positive_int(in1_channels, "in1_channels", self.cls_name)
# self.in_channels = Validator.check_positive_int(in2_channels, "in2_channels", self.cls_name)
# self.out_channels = Validator.check_positive_int(out_channels, "out_channels", self.cls_name)
# self.has_bias = Validator.check_bool(has_bias, "has_bias", self.cls_name)
self.in1_channels = in1_channels
self.in2_channels = in2_channels
self.out_channels = out_channels
self.has_bias = has_bias
bound = 1 / math.sqrt(in1_channels)
if weight_init is None:
weight_init = Uniform(bound)
if isinstance(weight_init, Tensor):
if weight_init.ndim != 3 or weight_init.shape[0] != out_channels or \
weight_init.shape[1] != in1_channels or weight_init.shape[2] != in2_channels:
raise ValueError(f"For '{self.cls_name}', weight init shape error. The ndim of 'weight_init' must "
f"be equal to 3, the first dim must be equal to 'out_channels', the "
f"second dim must be equal to 'in1_channels', and the third dim must be "
f"equal to 'in2_channels'. But got 'weight_init': {weight_init}, "
f"'out_channels': {out_channels}, 'in_channels': {in1_channels}, "
f"'in2_channels': {in2_channels}")
self.weight = Parameter(initializer(weight_init, (out_channels, in1_channels, in2_channels)), 'weight')
if self.has_bias:
if bias_init is None:
bias_init = Uniform(bound)
if isinstance(bias_init, Tensor):
if bias_init.ndim != 1 or bias_init.shape[0] != out_channels:
raise ValueError(f"For '{self.cls_name}', bias init shape error. The ndim of 'bias_init' should "
f"be equal to 1, and the first dim must be equal to 'out_channels'. But got "
f"'bias_init': {bias_init}, 'out_channels': {out_channels}.")
self.bias = Parameter(initializer(bias_init, [out_channels]), name="bias")
self.bias_add = P.BiasAdd()
self.matmul = P.MatMul()
def construct(self, input1, input2):
input1_shape = input1.shape
input2_shape = input2.shape
# check_dense_input_shape(input1_shape, self.cls_name)
# check_dense_input_shape(input2_shape, self.cls_name)
check_dense_inputs_same_shape(input1_shape, input2_shape, self.cls_name)
if len(input1_shape) != 2:
input1 = input1.reshape((-1, input1_shape[-1]))
input2 = input2.reshape((-1, input2_shape[-1]))
batch_size = input1.shape[0]
output = self.matmul(input1, self.weight.transpose(1, 2, 0).view(self.in1_channels, -1))
output = output.view(batch_size, self.in2_channels, self.out_channels)
output = output.transpose(2, 0, 1) * input2
output = output.sum(2).swapaxes(0, 1)
if self.has_bias:
output = self.bias_add(output, self.bias)
if len(input1_shape) != 2:
out_shape = input1_shape[:-1] + (-1,)
output = output.reshape(out_shape)
return output
def extend_repr(self):
s = 'in1_channels={}, in2_channels={}, output_channels={}'.format(
self.in1_channels, self.in2_channels, self.out_channels)
if self.has_bias:
s += ', has_bias={}'.format(self.has_bias)
return s
