def load_model(model_path, encoder_path=None): model = NeuralNetwork() model.load(file_name=model_path) if encoder_path is not None: encoder = pickle.load(encoder_path) return model, encoder else: return model
def load_model(model_path, encoder=None): model = NeuralNetwork() model.load(file_name=model_path) if encoder is not None: if type(encoder) == str: encoder = pickle.load(open(encoder, 'rb')) return model, encoder elif type(encoder) == onehot_decoder: return model, encoder else: return model
p_processed.setYRange(-1, 1) p_processed.setLabel('left', text='Z position (m)') p_processed.setLabel('bottom', text='Doppler (m/s)') s_processed = p_processed.plot([], [], pen=None, symbol='o') # Main loop detObj = {} frameData = {} preprocessed_frameArray = [] # reading RNN model from keras.models import load_model if isPredict: regressive_classifier = NeuralNetwork() regressive_classifier.load(file_name='trained_models/radar_model/072319_02/regressive_classifier.h5') onNotOn_ann_classifier = NeuralNetwork() onNotOn_ann_classifier.load(file_name='F:/config_detection/models/onNotOn_ANN/classifier_080719_2.h5') onNotOn_encoder = pickle.load(open('F:/config_detection/models/onNotOn_ANN/encoder_080719_2', 'rb')) rnn_timestep = 100 num_padding = 50 def input_thread(a_list): input() interrupt_list.append(True) class prediction_thread(Thread): def __init__(self, event): Thread.__init__(self)
s_processed = p_processed.plot([], [], pen=None, symbol='o') # Main loop detObj = {} frameData = {} preprocessed_frameArray = [] frameArray = [] # reading RNN model from keras.models import load_model if isPredict: # regressive_classifier = NeuralNetwork() # regressive_classifier.load(file_name='trained_models/radar_model/072319_02/regressive_classifier.h5') onNotOn_ann_classifier = NeuralNetwork() onNotOn_ann_classifier.load(file_name='F:/config_detection/models/onNotOn_ANN/classifier_080919_2.h5') onNotOn_encoder = pickle.load(open('F:/config_detection/models/onNotOn_ANN/encoder_080919_2', 'rb')) rnn_timestep = 100 num_padding = 50 def input_thread(a_list): input() interrupt_list.append(True) class prediction_thread(Thread): def __init__(self, event): Thread.__init__(self) self.stopped = event