def load_models():
models = []
nb_models = 5
for run in range(nb_models):
print("====== Loading ensemble model: %d ======" % run)
m = BaseModel('data/', dense_nodes=4096)
model_prefix = "da_r%d_" % run
m.model_name = model_prefix + m.model_name
print("model path: %s" % m.model_path)
m.load_model()
models = models + [m]
return models
def test_and_submit():
print("====== loading model ======")
m = BaseModel('data/')
m.load_model()
print("====== running test ======")
preds, test_batches = m.test()
print("======= making submission ========")
submits_path = 'submits/base_model_subm.gz'
submit(preds, test_batches, submits_path)
print("======= pushing to kaggle ========")
push_to_kaggle(submits_path)
ap.add_argument("-i", "--input", required=True, type=str, help="Path to image or video file")
ap.add_argument("-d", "--device", type=str, default="CPU", help="Specify the target device to infer on")
ap.add_argument("-o", "--output", type=str, default="output", help="output file for storing stats")
ap.add_argument("-pt", "--threshold", type=float, default=0.5, help="Probability threshold for detections filtering")
ap.add_argument("-fdv", "--fdv", type=str, help="FaceDetection visualization")
ap.add_argument("-lmv", "--lmv", type=str, help="LandmarksDetection visualization")
ap.add_argument("-hpv", "--hpv", type=str, help="HeadPoseEstimation visualization")
args = vars(ap.parse_args())
pyautogui.FAILSAFE = False
mltime_s = time.time()
#importing models
net1 = BaseModel(args['model1'])
m1time_s = time.time()
fd_shape, fd_name = net1.load_model()
face_load_time= time.time() - m1time_s
print('FaceDetection Model Load Time: {}'.format(face_load_time))
net2 = BaseModel(args['model2'])
m2time_s = time.time()
lm_shape, lm_name = net2.load_model()
land_load_time= time.time() - m2time_s
print('LandmarksDetection Model Load Time: {}'.format(land_load_time))
net3 = BaseModel(args['model3'])
m3time_s = time.time()
hp_shape, hp_name = net3.load_model()
head_load_time= time.time() - m3time_s
print('HeadPoseEstimation Model Load Time: {}'.format(head_load_time))