def __init__(self,
batchNorm=True,
lastRelu=False,
resBlock=True,
maxdisp=-1,
input_channel=3):
super(FADNet, self).__init__()
self.input_channel = input_channel
self.batchNorm = batchNorm
self.lastRelu = lastRelu
self.maxdisp = maxdisp
self.resBlock = resBlock
# First Block (DispNetC)
self.dispnetc = DispNetC(self.batchNorm,
maxdisp=self.maxdisp,
input_channel=input_channel)
# warp layer and channelnorm layer
self.channelnorm = ChannelNorm()
self.resample1 = Resample2d()
# Second Block (DispNetRes), input is 11 channels(img0, img1, img1->img0, flow, diff-mag)
in_planes = 3 * 3 + 1 + 1
self.dispnetres = DispNetRes(in_planes,
self.batchNorm,
lastRelu=self.lastRelu,
maxdisp=self.maxdisp,
input_channel=input_channel)
self.relu = nn.ReLU(inplace=False)
def __init__(self,
batchNorm=False,
lastRelu=True,
resBlock=True,
maxdisp=-1,
input_channel=3):
super(DispNetCS, self).__init__()
self.input_channel = input_channel
self.batchNorm = batchNorm
self.resBlock = resBlock
self.lastRelu = lastRelu
self.maxdisp = maxdisp
# First Block (DispNetC)
self.dispnetc = DispNetC(batchNorm=self.batchNorm,
resBlock=self.resBlock,
maxdisp=self.maxdisp,
input_channel=input_channel)
# Second and third Block (DispNetS), input is 6+3+1+1=11
self.dispnets1 = DispNetS(11,
batchNorm=self.batchNorm,
resBlock=self.resBlock,
maxdisp=self.maxdisp,
input_channel=3)
# warp layer and channelnorm layer
self.channelnorm = ChannelNorm()
self.resample1 = Resample2d()
self.relu = nn.ReLU(inplace=False)
def __init__(self,
batchNorm=True,
lastRelu=False,
resBlock=True,
maxdisp=-1,
input_channel=3,
input_img_shape=None,
warp_size=None):
super(MobileFADNet, self).__init__()
self.input_channel = input_channel
self.batchNorm = batchNorm
self.lastRelu = lastRelu
self.maxdisp = maxdisp
self.resBlock = resBlock
self.warp_size = warp_size #(1, 3, 576, 960)
if warp_size is not None:
B, C, H, W = warp_size
xx = torch.arange(0, W).float().cuda()
yy = torch.arange(0, H).float().cuda()
xx = xx.view(1, -1).repeat(H, 1)
yy = yy.view(-1, 1).repeat(1, W)
xx = xx.view(1, 1, H, W).repeat(B, 1, 1, 1)
yy = yy.view(1, 1, H, W).repeat(B, 1, 1, 1)
yy = 2.0 * yy / max(H - 1, 1) - 1.0
self.warp_grid = (xx, yy)
else:
self.warp_grid = None
# First Block (DispNetC)
self.dispnetc = MobileDispNetC(self.batchNorm,
maxdisp=self.maxdisp,
input_channel=input_channel,
input_img_shape=input_img_shape)
# warp layer and channelnorm layer
self.channelnorm = ChannelNorm()
self.resample1 = Resample2d()
# Second Block (DispNetRes), input is 11 channels(img0, img1, img1->img0, flow, diff-mag)
in_planes = 3 * 3 + 1 + 1
self.dispnetres = MobileDispNetRes(in_planes,
self.batchNorm,
lastRelu=self.lastRelu,
maxdisp=self.maxdisp,
input_channel=input_channel)
self.relu = nn.ReLU(inplace=False)