alov = AlovDataset(os.path.join(alov_root_dir,
'ALOV_images/'),
os.path.join(alov_root_dir,
'ALOV_ann/'),
os.path.join(alov_root_dir,
'ALOV_results/'),
device)
from deeplkt.config import *
params = dotdict({
'mode' : MODE,
'max_iterations' : MAX_LK_ITERATIONS,
'epsilon' : EPSILON,
'num_classes': NUM_CLASSES,
'num_channels': 3,
'info': "LearnedLKTALOV"
})
# lr = 0.0005
# momentum = 0.5
net = PureLKTNet(device, params)
tracker = LKTTracker(net)
train_params = dotdict({
'batch_size' : BATCH_SIZE,
'val_split' : VALIDATION_SPLIT,
'train_examples':TRAIN_EXAMPLES,
'shuffle_train': SHUFFLE_TRAIN,
# 'ALOV_images/'),
# os.path.join(alov_root_dir,
# 'ALOV_ann/'),
# os.path.join(alov_root_dir,
# 'ALOV_results/'),
# device)
# train_loader = DataLoader(alov, batch_size=1, shuffle=False)
params = dotdict({
'mode' : MODE,
'max_iterations' : MAX_LK_ITERATIONS,
'epsilon' : EPSILON,
'info': "Pure LKT"
})
# lr = 0.0005
# momentum = 0.5
nn = PureLKTNet(device, params)
tracker = LKTTracker(nn)
video_name = "../red_square.mp4"
dir_name = "../red_square"
outdir_name = "../red_square_results"
window_name = "ABC"
make_dir(dir_name)