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()
# stereoOptimizer = optim.Adam(stereonet.parameters(), lr = Lr)
stereoOptimizer = optim.Adam([{'params':stereonet.preLoadedParams,'lr':Lr},
loadPretrain(stereonet, predModel)
print '---'
dnet = DNet()
dnet.cuda()
loadPretrain(dnet, dnetPreModel)
dnet2 = DNet2()
dnet2.cuda()
loadPretrain(dnet2, dnetPreModel)
normalize = Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
sceneDataset = SceneflowDataset(filename='val.txt',
transform=Compose([
RandomCrop(size=(320, 640)),
RandomHSV((7, 37, 37)),
ToTensor(), normalize
]))
dataloader = DataLoader(sceneDataset,
batch_size=batch,
shuffle=True,
num_workers=2)
criterion1 = nn.SmoothL1Loss()
criterion2 = nn.BCELoss()
label = torch.FloatTensor(batch)
real_label = 1
fake_label = 0
label = Variable(label)
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
}],
# paramName = 'models/'+exp_prefix+'stereo_2'
predModel = 'models/12-3-4_stereo_gan_80000.pkl'
dataset = 'scene'
stereonet = StereoNet()
stereonet.cuda()
loadPretrain(stereonet, predModel)
normalize = Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
if dataset == 'scene':
sceneDataset = SceneflowDataset(filename='val.txt',
transform=Compose([
RandomCrop(size=(320, 640)),
RandomHSV((0, 0, 0)),
ToTensor(), normalize
]))
else:
sceneDataset = KittiDataset(transform=Compose([
RandomCrop(size=(320, 640)),
RandomHSV((0, 0, 0)),
ToTensor(), normalize
]))
dataloader = DataLoader(sceneDataset,
batch_size=1,
shuffle=True,
num_workers=8)
# criterion = nn.SmoothL1Loss()