Lr = 1e-4
batch = 1
trainstep = 100000
showiter = 20
snapshot = 10000
paramName = 'models/' + exp_prefix + 'stereo_2'
predModel = 'models/9-2-2_stereo_2_50000.pkl'
lossfilename = exp_prefix + 'loss'
stereonet = StereoNet()
stereonet.cuda()
# loadPretrain(stereonet,predModel)
normalize = Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
sceneDataset = SceneflowDataset(transform=Compose([
RandomCrop(size=(320, 640)),
RandomHSV((7, 37, 37)),
ToTensor(), normalize
]))
dataloader = DataLoader(sceneDataset,
batch_size=batch,
shuffle=True,
num_workers=8)
criterion = nn.SmoothL1Loss()
# stereoOptimizer = optim.Adam(stereonet.parameters(), lr = Lr)
stereoOptimizer = optim.Adam([{
'params': stereonet.preLoadedParams,
'lr': Lr
}, {
'params': stereonet.params
showiter = 20 snapshot = 10000 paramName = 'models/'+exp_prefix+'stereo_2' predModel = 'models/9-3_stereo_2_100000.pkl' lossfilename = exp_prefix+'loss' SceneTurn = 5 ImgHeight = 320 ImgWidth = 640 stereonet = StereoNet() stereonet.cuda() loadPretrain(stereonet,predModel) normalize = Normalize(mean=[0.485, 0.456, 0.406],std=[0.229, 0.224, 0.225]) sceneDataset = SceneflowDataset(transform=Compose([ RandomCrop(size=(ImgHeight,ImgWidth)), RandomHSV((10,80,80)), ToTensor(), normalize])) kittiDataset = KittiDataset(transform=Compose([ RandomCrop(size=(ImgHeight,ImgWidth)), RandomHSV((7,50,50)), ToTensor(), normalize]), surfix='train') sceneDataloader = DataLoader(sceneDataset, batch_size=batch, shuffle=True, num_workers=4) kittiDataloader = DataLoader(kittiDataset, batch_size=batch, shuffle=True, num_workers=4) sceneiter = iter(sceneDataloader) kittiiter = iter(kittiDataloader) criterion = nn.SmoothL1Loss()
paramName = 'models/'+exp_prefix+'stereo_gan'
predModel = 'models/12-3-3_stereo_gan_100000.pkl'
dnetPreModel = 'models/12-3-3_stereo_gan_dnet_100000.pkl'
lamb = 1
stereonet = StereoNet()
stereonet.cuda()
loadPretrain(stereonet,predModel)
print '---'
dnet = DNet()
dnet.cuda()
loadPretrain(dnet,dnetPreModel)
normalize = Normalize(mean=[0.485, 0.456, 0.406],std=[0.229, 0.224, 0.225])
sceneDataset = SceneflowDataset(transform=Compose([ RandomCrop(size=(320,640)),
RandomHSV((7,37,37)),
ToTensor(),
normalize]))
dataloader = DataLoader(sceneDataset, batch_size=batch, shuffle=True, num_workers=8)
criterion1 = nn.SmoothL1Loss()
# stereoOptimizer = optim.Adam(stereonet.parameters(), lr = Lr)
stereoOptimizer = optim.Adam([{'params':stereonet.preLoadedParams,'lr': stereo_lr},
{'params':stereonet.params}], lr = stereo_lr)
criterion2 = nn.BCELoss()
dnetOptimizer = optim.Adam(dnet.parameters(), lr = dnet_lr)