diff --git a/deel/torchlip/modules/normalization.py b/deel/torchlip/modules/normalization.py index ddc7d2f..bb5fd43 100644 --- a/deel/torchlip/modules/normalization.py +++ b/deel/torchlip/modules/normalization.py @@ -23,9 +23,6 @@ def forward(self, x): LayerCentering2d = LayerCentering -# class LayerCentering2D(LayerCentering): -# def __init__(self, size = 1, dim=[-2,-1]): -# super(LayerCentering2D, self).__init__(size = size,dim=[-2,-1]) class BatchCentering(nn.Module): @@ -72,25 +69,4 @@ def forward(self, x): return x - mean.view(mean_shape) -# class BatchCenteringBiases(BatchCentering): -# def __init__(self, size =1, dim=[0,-2,-1], momentum=0.05): -# super(BatchCenteringBiases, self).__init__(size = size, dim = dim, momentum = momentum) -# if isinstance(size, tuple): -# self.alpha = nn.Parameter(torch.zeros(size), requires_grad=True) -# else: -# self.alpha = nn.Parameter(torch.zeros(1,size,1,1), requires_grad=True) - -# def forward(self, x): -# #print(x.mean(dim=self.dim, keepdim=True).abs().mean().cpu().numpy(), self.running_mean.abs().cpu().mean().numpy(), self.alpha.abs().mean().cpu().numpy()) -# #print(x.mean(dim=self.dim, keepdim=True).abs().mean().cpu().numpy(),(x.mean(dim=self.dim, keepdim=True)-self.running_mean).abs().mean().cpu().numpy()) -# return super().forward(x) + self.alpha - BatchCentering2d = BatchCentering - -# class BatchCenteringBiases2D(BatchCenteringBiases): -# def __init__(self, size =1, momentum=0.05): -# super(BatchCenteringBiases2D, self).__init__(size = size, dim=[0,-2,-1],momentum=momentum) - -# class BatchCentering2D(BatchCentering): -# def __init__(self, size =1, momentum=0.05): -# super(BatchCentering2D, self).__init__(size = size, dim=[0,-2,-1],momentum=momentum) diff --git a/tests/test_normalization.py b/tests/test_normalization.py index 382180c..6aa3d5f 100644 --- a/tests/test_normalization.py +++ b/tests/test_normalization.py @@ -26,7 +26,6 @@ # ===================================================================================== import os import pytest -from functools import partial import numpy as np @@ -249,6 +248,6 @@ def test_BatchCentering_runningmean(size, input_shape, bias): mean_x = np.mean(x, axis=(0, 2, 3)) x = uft.to_tensor(x) for _ in range(1000): - y = bn(x) + y = bn(x) # noqa: F841 np.testing.assert_allclose(bn.running_mean, mean_x, atol=1e-5)