def __init__(self, nout, insize, nch, gpu_ids, opt=None):
super(DecD, self).__init__()
def opt_default(): 1
opt_default.noise_std = 0
opt_default.nclasses = 1
self.opt = init_opts(opt, opt_default)
self.gpu_ids = gpu_ids
self.fcsize = insize/64
self.noise = torch.zeros(0)
self.main = nn.Sequential(
nn.Conv2d(nch, 64, ksize, dstep, 1),
# nn.BatchNorm2d(64),
nn.LeakyReLU(0.2, inplace=True),
nn.Conv2d(64, 128, ksize, dstep, 1),
nn.BatchNorm2d(128),
nn.LeakyReLU(0.2, inplace=True),
nn.Conv2d(128, 256, ksize, dstep, 1),
nn.BatchNorm2d(256),
nn.LeakyReLU(0.2, inplace=True),
nn.Conv2d(256, 512, ksize, dstep, 1),
nn.BatchNorm2d(512),
nn.LeakyReLU(0.2, inplace=True),
nn.Conv2d(512, 512, ksize, dstep, 1),
nn.BatchNorm2d(512),
nn.LeakyReLU(0.2, inplace=True),
nn.Conv2d(512, 512, ksize, dstep, 1),
nn.BatchNorm2d(512),
nn.LeakyReLU(0.2, inplace=True)
)
self.fc = nn.Linear(512*int(self.fcsize**2), nout)
if nout == 1:
self.nlEnd = nn.Sigmoid()
else:
self.nlEnd = nn.LogSoftmax()
def __init__(self, nout, nch, gpu_ids, opt=None):
super(DecD, self).__init__()
def opt_default(): 1
opt_default.noise_std = 0
opt_default.nclasses = 1
self.opt = init_opts(opt, opt_default)
self.gpu_ids = gpu_ids
self.fcsize = 2
self.noise = torch.zeros(0)
self.main = nn.Sequential(
nn.Conv3d(nch, 64, ksize, dstep, 1),
# nn.BatchNorm3d(64),
nn.LeakyReLU(0.2, inplace=True),
nn.Conv3d(64, 128, ksize, dstep, 1),
nn.BatchNorm3d(128),
nn.LeakyReLU(0.2, inplace=True),
nn.Conv3d(128, 256, ksize, dstep, 1),
nn.BatchNorm3d(256),
nn.LeakyReLU(0.2, inplace=True),
nn.Conv3d(256, 512, ksize, dstep, 1),
nn.BatchNorm3d(512),
nn.LeakyReLU(0.2, inplace=True),
nn.Conv3d(512, 1024, ksize, dstep, 1),
nn.BatchNorm3d(1024),
nn.LeakyReLU(0.2, inplace=True),
nn.Conv3d(1024, 1024, ksize, dstep, 1),
nn.BatchNorm3d(1024),
nn.LeakyReLU(0.2, inplace=True)
)
self.fc = nn.Linear(1024*int(self.fcsize), nout)
