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,
'lr': LR,
'momentum': MOMENTUM,
'l2': L2,
'random_seed': RANDOM_SEED
})
model = BaseModel(tracker, 'checkpoint', 'logs', train_params)
def count_parameters(model):
return sum(p.numel() for p in model.parameters() if p.requires_grad)
print(count_parameters(net))
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)
make_dir(outdir_name)
# convertVideoToDir(video_name, dir_name)
frames = readDir(dir_name)
first_frame = True
cnt = 0
for frame in frames:
frame = np.expand_dims(frame, 0)