def __init__(self):
super(NNNormalizationModule, self).__init__()
self.input1d = torch.randn(1, 4, 50)
self.module1d = nn.ModuleList([
nn.BatchNorm1d(4),
nn.InstanceNorm1d(4),
])
self.input2d = torch.randn(1, 4, 30, 10)
self.module2d = nn.ModuleList([
nn.BatchNorm2d(4),
nn.GroupNorm(4, 4),
nn.InstanceNorm2d(4),
nn.LayerNorm([4, 30, 10]),
nn.LocalResponseNorm(2),
])
self.input3d = torch.randn(1, 4, 10, 4, 4)
self.module3d = nn.ModuleList([
nn.BatchNorm3d(4),
nn.InstanceNorm3d(4),
nn.ChannelShuffle(2),
])
def __init__(self):
super(NNShuffleModule, self).__init__()
self.shuffle = nn.ChannelShuffle(2)
def __init__(self):
super(Model, self).__init__()
self.shuffle_0 = nn.ChannelShuffle(2)
self.shuffle_1 = nn.ChannelShuffle(16)