def test_test_real():
args = parse()
print(args)
dataset = SepeDataset(args.poses_train,args.images_train,coor_layer_flag =False)
dataloader = DataLoader(dataset, batch_size=10,shuffle=False ,num_workers=1,drop_last=True)
dvo_feature_extractor = DVOFeature()
dvo_regressor = DVORegression()
dvo_discriminator = Discriminator(500,500,2)
dvo_feature_extractor.load_state_dict(torch.load(('feature_ntsd_2_10.pt')))
dvo_regressor.load_state_dict(torch.load('regressor_seed_ntsd_2_10.pt'))
test(dvo_feature_extractor,dvo_regressor,dataloader,args)
def test_test():
args = parse()
print(args)
motion_ax_i = [int(i) for i in args.motion_ax.split(' ')]
test_motion_ax_i = [int(i) for i in args.test_motion_ax.split(' ')]
dataset = RandomDataset(2,motion_ax = test_motion_ax_i)
dataloader = DataLoader(dataset, batch_size=1,shuffle=False ,num_workers=1,drop_last=True)
dvo_feature_extractor = DVOFeature()
dvo_regressor = DVORegression()
dvo_discriminator = Discriminator(500,500,2)
dvo_feature_extractor.load_state_dict(torch.load('feature'+args.motion_ax.replace(' ','')+'.pt'))
dvo_regressor.load_state_dict(torch.load('regressor'+args.motion_ax.replace(' ','')+'.pt'))
test(dvo_feature_extractor,dvo_regressor,dataloader,args)
def test_adapt():
args = parse()
print(args)
dataset = SepeDataset(args.poses_train,args.images_train,coor_layer_flag =False)
dataloader = DataLoader(dataset, batch_size=1,shuffle=True ,num_workers=1,drop_last=True,worker_init_fn=lambda wid:np.random.seed(np.uint32(torch.initial_seed() + wid)))
dataset_tgt = SepeDataset(args.poses_target,args.images_target,coor_layer_flag =False)
dataloader_tgt = DataLoader(dataset_tgt, batch_size=1,shuffle=True ,num_workers=1,drop_last=True,worker_init_fn=lambda wid:np.random.seed(np.uint32(torch.initial_seed() + wid)))
src_extractor = DVOFeature()
tgt_extractor = DVOFeature()
src_extractor.load_state_dict(torch.load(args.feature_model))
tgt_extractor.load_state_dict(torch.load(args.feature_model))
dvo_discriminator = Discriminator(500,500,2)
adapt(src_extractor,tgt_extractor,dvo_discriminator,dataloader,dataloader_tgt,args)
torch.save(tgt_extractor.state_dict(),'tgt_feature_'+args.tag+str(args.epoch)+'.pt')
torch.save(dvo_discriminator.state_dict(),'dis_'+args.tag+str(args.epoch)+'.pt')
def test_model(image):
dvo_feature_extractor = DVOFeature()
dvo_regressor = DVORegression()
dvo_discriminator = Discriminator(500,500,2)
feature = dvo_feature_extractor(image)
print(feature.shape)
motion = dvo_regressor(feature)
print(motion.shape)
dis = dvo_discriminator(feature)
print(dis)
def test_train_real():
args = parse()
print(args)
dataset = SepeDataset(args.poses_train,args.images_train,coor_layer_flag =False)
dataloader = DataLoader(dataset, batch_size=3,shuffle=True ,num_workers=1,drop_last=True,worker_init_fn=lambda wid:np.random.seed(np.uint32(torch.initial_seed() + wid)))
dvo_feature_extractor = DVOFeature()
dvo_regressor = DVORegression()
dvo_discriminator = Discriminator(500,500,2)
trained_feature,trained_regressor = train(dvo_feature_extractor,dvo_regressor,dataloader,args)
torch.save(trained_feature.state_dict(),'feature_'+args.tag+str(args.epoch)+'.pt')
torch.save(trained_regressor.state_dict(),'regressor_'+args.tag+str(args.epoch)+'.pt')
def test_train():
args = parse()
print(args)
motion_ax_i = [int(i) for i in args.motion_ax.split(' ')]
dataset = RandomDataset(20000,motion_ax = motion_ax_i)
dataloader = DataLoader(dataset, batch_size=1000,shuffle=False ,num_workers=1,drop_last=True,worker_init_fn=lambda wid:np.random.seed(np.uint32(torch.initial_seed() + wid)))
dvo_feature_extractor = DVOFeature()
dvo_regressor = DVORegression()
dvo_discriminator = Discriminator(500,500,2)
trained_feature,trained_regressor = train(dvo_feature_extractor,dvo_regressor,dataloader,args)
torch.save(trained_feature.state_dict(),'feature_seed'+args.motion_ax.replace(' ','')+str(args.epoch)+'.pt')
torch.save(trained_regressor.state_dict(),'regressor_seed'+args.motion_ax.replace(' ','')+str(args.epoch)+'.pt')