def __init__(self, n_feat):
super(Downsample, self).__init__()
self.body = nn.Sequential(
nn.Conv2d(n_feat,
n_feat // 2,
kernel_size=3,
stride=1,
padding=1,
bias=False), nn.PixelUnshuffle(2))
def __init__(self):
super(NNVisionModule, self).__init__()
self.input = torch.randn(1, 4, 9, 9)
self.vision_modules = nn.ModuleList([
nn.PixelShuffle(2),
nn.PixelUnshuffle(3),
nn.Upsample(scale_factor=2, mode="nearest"),
nn.Upsample(scale_factor=2, mode="bilinear"),
nn.Upsample(scale_factor=2, mode="bicubic"),
nn.UpsamplingNearest2d(scale_factor=2),
nn.UpsamplingBilinear2d(scale_factor=2),
])
self.linear_sample = nn.Upsample(scale_factor=2, mode="linear")
self.trilinear_sample = nn.Upsample(scale_factor=2, mode="trilinear")
def __init__(self):
super(Model, self).__init__()
self.down_0 = nn.PixelUnshuffle(2)
self.down_1 = nn.PixelUnshuffle(4)
def __init__(self, r):
super(PixelShuffle2d, self).__init__()
self.r = r
self.shuffle = nn.PixelShuffle(r)
self.unshuffle = nn.PixelUnshuffle(r)