def main():
##################################################
# parse data from original data & construct images
##################################################
print(
"parsing data from log files which are generated by Atheros-CSI-TOOL\n"
)
data_generator = DataLogParser(conf.n_timestamps, conf.D, conf.step_size,
conf.ntx_max, conf.nrx_max,
conf.nsubcarrier_max, conf.data_folder,
conf.log_folder, conf.skip_frames,
conf.time_offset_ratio, conf.day_conf,
conf.label)
data_generator.generate_image_no_label(conf.draw_date, conf.draw_label)
# train_data, test_data: classes (key: label, value: images under this label)
test_data = data_generator.get_data_no_label()
##################################################
# apply signal processing blocks to images
##################################################
print("Pre-processing data\n")
data_process = DataPreprocess(conf.n_timestamps, conf.D, conf.step_size,
conf.ntx_max, conf.ntx, conf.nrx_max,
conf.nrx, conf.nsubcarrier_max,
conf.nsubcarrier, conf.data_shape_to_nn,
conf.data_folder, conf.label)
data_process.add_image_no_label(test_data)
data_process.signal_processing(conf.do_fft, conf.fft_shape)
data_process.prepare_shape()
final_test_data = data_process.get_data_no_label()
##################################################
# train or test data with neural netowrk
##################################################
nn_model = NeuralNetworkModel(conf.data_shape_to_nn, conf.abs_shape_to_nn,
conf.phase_shape_to_nn, conf.total_classes)
print("Get test result using existing model (in test mode)\n")
nn_model.load_model(conf.model_name)
for key in final_test_data:
plt.figure()
total_test = len(final_test_data[key])
cc = 1
for idx in final_test_data[key]:
# if want to output motion probability, please set output_label == False
result = nn_model.get_no_label_result(final_test_data[key][idx],
output_label=True)
plt.subplot(total_test, 1, cc)
plt.plot(result)
plt.title(idx)
plt.ylim(0, 1.05)
cc = cc + 1
plt.suptitle(key)
nn_model.end()
plt.show()
print("Done!")
def main():
args = get_input_arguments()
training_mode = (args.mode == 'Y')
if args.mode not in ['Y', 'N']:
raise ValueError('Invalid input value for m should be either Y or N')
data_folder = conf.data_folder
if training_mode:
label = conf.train_label
print('in training mode')
print('training data from {} \nvalidation data from {}\n'.format(
conf.training_date, conf.training_validate_date))
print('training label is {}\n'.format(label))
data_folder += "training/"
else:
label = conf.test_label
print('in test mode')
print('test date from {}'.format(conf.test_date))
print('test label is {}\n'.format(label))
data_folder += "test/"
##################################################
# parse data from original data & construct images
##################################################
print(
"parsing data from log files which are generated by Atheros-CSI-TOOL\n"
)
data_generator = DataLogParser(conf.n_timestamps, conf.D, conf.step_size,
conf.ntx_max, conf.nrx_max,
conf.nsubcarrier_max, data_folder,
conf.log_folder, conf.skip_frames,
conf.time_offset_ratio, conf.day_conf,
label)
train_date = conf.training_date if training_mode else []
if training_mode:
data_generator.generate_image(conf.training_date,
conf.training_validate_date)
else:
data_generator.generate_image([], conf.test_date)
# train_data, test_data: classes (key: label, value: images under this label)
train_data, test_data = data_generator.get_data()
##################################################
# apply signal processing blocks to images
##################################################
print("Pre-processing data\n")
data_process = DataPreprocess(conf.n_timestamps, conf.D, conf.step_size,
conf.ntx_max, conf.ntx, conf.nrx_max,
conf.nrx, conf.nsubcarrier_max,
conf.nsubcarrier, conf.data_shape_to_nn,
data_folder, label)
data_process.load_image(training_mode, False, train_data, test_data)
data_process.signal_processing(conf.do_fft, conf.fft_shape)
data_process.prepare_shape()
x_train, y_train, x_test, y_test = data_process.get_data()
##################################################
# train or test data with neural netowrk
##################################################
nn_model = NeuralNetworkModel(conf.data_shape_to_nn, conf.abs_shape_to_nn,
conf.phase_shape_to_nn, conf.total_classes)
nn_model.add_data(x_train, y_train, x_test, y_test)
if training_mode:
print("Building a new model (in training mode)\n")
nn_model.cnn_model_abs_phase()
nn_model.fit_data(conf.epochs)
nn_model.save_model(conf.model_name)
else:
print("Get test result using existing model (in test mode)\n")
nn_model.load_model(conf.model_name)
result = nn_model.get_test_result(label)
# nn_model.save_result(result, conf.file_prefix+conf.test_result_filename)
nn_model.end()
print("Done!")