| @@ -0,0 +1,30 @@ | |||||
| import torch | |||||
| import unittest | |||||
| from fastNLP.modules.other_modules import GroupNorm, LayerNormalization, BiLinear | |||||
| class TestGroupNorm(unittest.TestCase): | |||||
| def test_case_1(self): | |||||
| gn = GroupNorm(num_features=1, num_groups=10, eps=1.5e-5) | |||||
| x = torch.randn((20, 50, 10)) | |||||
| y = gn(x) | |||||
| class TestLayerNormalization(unittest.TestCase): | |||||
| def test_case_1(self): | |||||
| ln = LayerNormalization(d_hid=5, eps=2e-3) | |||||
| x = torch.randn((20, 50, 5)) | |||||
| y = ln(x) | |||||
| class TestBiLinear(unittest.TestCase): | |||||
| def test_case_1(self): | |||||
| bl = BiLinear(n_left=5, n_right=5, n_out=10, bias=True) | |||||
| x_left = torch.randn((7, 10, 20, 5)) | |||||
| x_right = torch.randn((7, 10, 20, 5)) | |||||
| y = bl(x_left, x_right) | |||||
| print(bl) | |||||
| bl2 = BiLinear(n_left=15, n_right=15, n_out=10, bias=False) | |||||