def on_epoch_end(self, epoch, logs={}):
self.loss.append(logs["val_loss"])
if len(self.loss) == 1 or min(self.loss[:-1]) > self.loss[-1]:
ev = Evaluator(self.model, SIGMA_REL_TH)
ev.process_NED2012(self.NED2012_path)
ev.print_res()
sd_SD, sd_SI = ev.get_res()
logs["NED2012 SD component error"] = np.array(sd_SD,
dtype="float64")
logs["NED2012 SI component error"] = np.array(sd_SI,
dtype="float64")
verbose=2,
callbacks=callbacks,
)
print("NoiseNet training completed. Path to model: %s" % model_path)
elif args.mode == 'eval':
print("NoiseNet evaluation mode.")
print("Evaluating model %s" % model_path)
try:
netBNPE = load_model(
model_path,
compile=False,
custom_objects={"SIGMA_EPSILON": SIGMA_EPSILON})
print("Model %s successfully loaded" % model_path)
except:
print("Failed to load model %s" % model_path)
sys.exit(0)
our_dir = os.path.dirname(model_path)
ev = Evaluator(netBNPE, SIGMA_REL_TH, 1, our_dir)
# perform test on pure gaussian noise
ev.pure_noise_test()
if args.NED2012_path is None:
print(
"NED2012_path not specified. Skipping NED2012 evaluation stage"
)
else:
# perform test on NED2012 database
ev.process_NED2012(args.NED2012_path)
else:
Warning("Unsupported mode")
