Differences with torch.Bilinear

torch.nn.Bilinear

torch.nn.Bilinear(
    in1_features,
    in2_features,
    out_features,
    bias=True)(input1, input2) -> Tensor

For more information, see torch.nn.Bilinear.

mindspore.nn.BiDense

mindspore.nn.BiDense(
    in1_channels,
    in2_channels,
    out_channels,
    weight_init=None,
    bias_init=None,
    has_bias=True)(input1, input2) -> Tensor

For more information, see mindspore.nn.BiDense.

Differences

PyTorch: Bilinear fully connected layer.

MindSpore: MindSpore API basically implements the same function as PyTorch. The initialization methods for weights and biases can be set via weight_init and bias_init respectively, which is not available for PyTorch.

Categories	Subcategories	PyTorch	MindSpore	Differences
Parameters	Parameter 1	in1_features	in1_channels	Same function, different parameter names
	Parameter 2	in2_features	in2_channels	Same function, different parameter names
	Parameter 3	out_features	out_channels	Same function, different parameter names
	Parameter 4	-	weight_init	Initialization method for the weight parameter, which is not available for PyTorch
	Parameter 5	-	bias_init	Initialization method for the bias parameter, which is not available for PyTorch
	Parameter 6	bias	has_bias	Same function, different parameter names
Inputs	Input 1	input1	input1	Same function
	Input 2	input2	input2	Same function

Code Example

# PyTorch
import torch

m = torch.nn.Bilinear(20, 30, 40)
input1 = torch.randn(128, 20)
input2 = torch.randn(128, 30)
output = m(input1, input2)
print(output.shape)
# torch.Size([128, 40])

# MindSpore
import mindspore
m = mindspore.nn.BiDense(20, 30, 40)
input1 = mindspore.ops.randn(128, 20)
input2 = mindspore.ops.randn(128, 30)
output = m(input1, input2)
print(output.shape)
# (128, 40)