spyrit.core.recon.DCDRUNet.reconstruct

DCDRUNet.reconstruct(x)[source]

Reconstruction step of a reconstruction network

Args:

x: raw measurement vectors

Shape:

x: raw measurement vectors with shape \((BC,2M)\)

output: reconstructed images with shape \((BC,1,H,W)\)

Example:

>>> B, C, H, M = 10, 1, 64, 64**2
>>> Ord = torch.ones((H,H))
>>> meas = HadamSplit(M, H, Ord)
>>> noise = NoNoise(meas)
>>> prep = SplitPoisson(1.0, M, H*H)
>>> sigma = torch.rand([H**2, H**2])
>>> n_channels = 1                   # 1 for grayscale image
>>> model_drunet_path = './spyrit/drunet/model_zoo/drunet_gray.pth'
>>> denoi_drunet = drunet(in_nc=n_channels+1, out_nc=n_channels, nc=[64, 128, 256, 512], nb=4, act_mode='R',
    downsample_mode="strideconv", upsample_mode="convtranspose")
>>> recnet = DCDRUNet(noise,prep,sigma,denoi_drunet)
>>> x = torch.rand((B*C,2*M), dtype=torch.float)
>>> z = recnet.reconstruct(x)
>>> print(z.shape)
torch.Size([10, 1, 64, 64])