def init_processor(self):
self.processor = get_processor(self.configs["processor"])
self.init_optimizer()
if 'regularizer' in dir(self.processor):
self.loss_function = self.loss_with_regularizer
else:
self.loss_function = self.loss_fn
def load_voltage_ranges(configs):
if configs['processor_type'] == 'dnpu' or configs[
'processor_type'] == 'surrogate':
p = get_processor(configs)
amplitude = np.array(p.info['data_info']['input_data']['amplitude'])
offset = np.array(p.info['data_info']['input_data']['offset'])
else:
amplitude = np.array(configs['input_data']['amplitude'])
offset = np.array(configs['input_data']['offset'])
min_voltage = offset - amplitude
max_voltage = offset + amplitude
return min_voltage, max_voltage
def init_processor(self, configs):
self.input_electrode_no = configs['input_electrode_no']
self.input_indices = configs['input_indices']
self.nr_control_genes = self.input_electrode_no - len(
self.input_indices)
self.control_voltage_indices = get_control_voltage_indices(
self.input_indices, self.input_electrode_no)
self.processor = get_processor(configs)
self.load_control_voltage_configs(configs)
self.clipvalue = configs['waveform'][
'output_clipping_value'] * self.processor.get_amplification_value(
) # 3.55
def __init__(self, configs, is_main=True):
# configs = self.load_directory_configs(configs)
self.is_main = is_main
self.algorithm = get_algorithm(configs['algorithm_configs'])
self.load_methods(configs['algorithm_configs'])
self.load_task_configs(configs)
if 'validation' in configs:
self.validation_processor_configs = configs['validation'][
'processor']
self.validation_processor = get_processor(
configs['validation']['processor'])
else:
self.validation_processor_configs = None
def run_test(self):
self.processor = get_processor(self.configs['processor'])
experiments = ["IV1", "IV2", "IV3", "IV4", "IV5", "IV6", "IV7"]
output = {}
output_array = []
for exp in experiments:
output[exp] = {}
output_array = self.processor.get_output(
IVtest.create_input_arrays(self))
for i, dev in enumerate(self.configs["processor"]['devices']):
output[exp][dev] = output_array[:, i].T
self.iv_plot(output)
def validate_prediction(self, name, inputs, control_voltages, predictions,
mask):
print(
'=========================================================================================='
)
print(f"Validating model prediction of {name}...")
inputs = self.clip(inputs,
max_value=MAX_CLIPPING_VALUE,
min_value=MIN_CLIPPING_VALUE)
control_voltages = self.clip(control_voltages,
max_value=MAX_CLIPPING_VALUE,
min_value=MIN_CLIPPING_VALUE)
input_matrix = self.generate_input_matrix(inputs, control_voltages)
if self.configs["processor"]["shape"] != len(predictions):
print(
f"Changing shape key of processor from value {self.configs['processor']['shape']} to {len(predictions)}"
)
self.configs["processor"]["shape"] = len(predictions)
processor = get_processor(self.configs['processor'])
measurement = processor.get_output(input_matrix)
processor.close_tasks()
error = ((measurement[mask, 0] - predictions[mask])**2).mean(
) # TODO: predictions should be 2 dim array with last dim singleton
print(f'MSE: {str(error)}')
var_measurement = np.var(measurement[mask], ddof=1)
print(f'(var) NMSE: {100*error/var_measurement} %')
self.plot_validation(name,
measurement[mask],
predictions[mask],
show_plots=self.show_plots,
save_dir=self.validation_dir)
print(
'=========================================================================================='
)
return error, var_measurement, measurement[mask]
def init_processors(self):
self.validation_processor = get_processor(
self.configs['validation']['processor'])
self.processor = get_processor(
self.configs['algorithm_configs']['processor'])
def __init__(self, configs):
self.configs = configs
self.processor = get_processor(configs['algorithm_configs']['processor'])
self.task = Task(configs)