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=False, lastRelu=True, input_channel=3, maxdisp=-1):
super(DToNNet, self).__init__()
self.input_channel = input_channel
self.batchNorm = batchNorm
self.lastRelu = lastRelu
self.maxdisp = maxdisp
#self.dn_transformer = Disp2Norm(16, 768, 384, {"fx":1050, "fy":1050})
# First Block (DispNetC)
self.dispnetc = DispNetC(batchNorm = self.batchNorm, input_channel=input_channel)
# Second and third Block (DispNetS), input is 6+3+1+1=11
self.normnets = NormNetS(input_channel=3+3+3+1)
self.fx = None
self.fy = None
def __init__(self,
batchNorm=False,
lastRelu=True,
input_channel=3,
maxdisp=-1):
super(DNFusionNet, self).__init__()
self.input_channel = input_channel
self.batchNorm = batchNorm
self.lastRelu = lastRelu
self.maxdisp = maxdisp
# First Block (DispNetC)
self.dispnetc = DispNetC(self.batchNorm,
input_channel=input_channel,
get_features=True)
# Second and third Block (DispNetS), input is 6+3+1+1=11
self.normnetdf = NormNetDF(input_channel=3 + 3 + 1)
self.fx = None
self.fy = None
from dataloader import KITTI_submission_loader2012 as DA
test_left_img, test_right_img = DA.dataloader(args.datapath)
devices = [int(item) for item in args.devices.split(',')]
ngpus = len(devices)
if args.model == 'stackhourglass':
model = stackhourglass(args.maxdisp)
elif args.model == 'basic':
model = basic(args.maxdisp)
elif args.model == 'dispnetcres':
model = DispNetCSRes(ngpus, False, True)
elif args.model == 'dispnetc':
model = DispNetC(ngpus, False, True)
else:
print('no model')
model = nn.DataParallel(model, device_ids=devices)
model.cuda()
if args.loadmodel is not None:
state_dict = torch.load(args.loadmodel)
model.load_state_dict(state_dict['state_dict'])
print('Number of model parameters: {}'.format(sum([p.data.nelement() for p in model.parameters()])))
def test(imgL,imgR, imgsize):
model.eval()
#imgL = imgL.unsqueeze(0)