def main():
"""main"""
args = parse_arguments()
data_files = list(Path(args.data_path).glob("*.csv"))
width, height = parse_resolution(args.resolution)
max_parameter = MaxBitrateParameter(w_max=width, h_max=height)
dfw = DataFrameWrapper(
read_df_by_keys(data_files, KEYS.TRAINING_SET), max_parameter
)
dfw_val = DataFrameWrapper(
read_df_by_keys(data_files, KEYS.VALIDATION_SET), max_parameter
)
in_train = dfw.get_ml_input()
in_val = dfw_val.get_ml_input()
out_train = dfw.get_ml_output()
out_val = dfw_val.get_ml_output()
input_scaler = StandardScaler().fit(np.concatenate([in_train, in_val], axis=0))
output_scaler = StandardScaler().fit(np.concatenate([out_train, out_val], axis=0))
train_db = create_db(
in_train, out_train, input_scaler, output_scaler, args.batch_size
)
val_db = create_db(in_val, out_val, input_scaler, output_scaler, args.batch_size)
model = create_model(args.model, args.learning_rate)
train(model, train_db, val_db, args.epochs)
model = tf.keras.models.load_model(MODEL_CHECKPOINT)
save_model(model, args.batch_size, args.learning_rate, args.epochs)
validate(model, dfw, input_scaler, output_scaler, "training")
validate(model, dfw_val, input_scaler, output_scaler, "validation")
dfw_test = DataFrameWrapper(
read_df_by_keys(data_files, KEYS.TEST_SET), max_parameter
)
validate(model, dfw_test, input_scaler, output_scaler)
def gcf(k_size, l, m, o): # noqa: E741
return l * pow(MaxBitrateParameter().k_size_min / k_size, m) + o
def rcf(width, j):
return pow(width / MaxBitrateParameter().w_max, j)
def ncf(gop, c, d, e):
return c * pow(MaxBitrateParameter().gop_min / gop, d) + e
def tcf(rate, b):
return pow(rate / MaxBitrateParameter().f_max, b)
def scf(qp, a):
return pow(qp / MaxBitrateParameter().qp_min, -a)
def sdcf(sigma, t, u, v):
return t * pow(MaxBitrateParameter().sigma_min / sigma, u) + v