def setUp(self):
args = parse_args(['-e', 'tests', '-c', '../configurations/config.ini'])
init_config(args.config)
configs = get_configurations('tests')
assert(len(configs) == 1)
global_vars.set_config(configs[0])
set_params_by_dataset('../configurations/dataset_params.ini')
def test_get_multiple_values(self):
args = parse_args([
'-e', 'test_multiple_values', '-c', '../configurations/config.ini'
])
init_config(args.config)
configs = get_configurations(args)
assert (len(configs) == 12)
for str_conf in [
'cross_subject', 'num_conv_blocks', 'num_generations'
]:
assert (str_conf in get_multiple_values(configs))
global_vars.set_config(configs[0])
def setUp(self):
args = parse_args(
['-e', 'tests', '-c', '../configurations/config.ini'])
init_config(args.config)
configs = get_configurations(args.experiment)
assert (len(configs) == 1)
global_vars.set_config(configs[0])
global_vars.set('eeg_chans', 22)
global_vars.set('num_subjects', 9)
global_vars.set('input_time_len', 1125)
global_vars.set('n_classes', 4)
set_params_by_dataset()
input_shape = (50, global_vars.get('eeg_chans'),
global_vars.get('input_time_len'))
class Dummy:
def __init__(self, X, y):
self.X = X
self.y = y
dummy_data = Dummy(X=np.ones(input_shape, dtype=np.float32),
y=np.ones(50, dtype=np.longlong))
self.iterator = BalancedBatchSizeIterator(
batch_size=global_vars.get('batch_size'))
self.loss_function = F.nll_loss
self.monitors = [
LossMonitor(),
MisclassMonitor(),
GenericMonitor('accuracy', acc_func),
RuntimeMonitor()
]
self.stop_criterion = Or([
MaxEpochs(global_vars.get('max_epochs')),
NoDecrease('valid_misclass',
global_vars.get('max_increase_epochs'))
])
self.naiveNAS = NaiveNAS(iterator=self.iterator,
exp_folder='../tests',
exp_name='',
train_set=dummy_data,
val_set=dummy_data,
test_set=dummy_data,
stop_criterion=self.stop_criterion,
monitors=self.monitors,
loss_function=self.loss_function,
config=global_vars.config,
subject_id=1,
fieldnames=None,
model_from_file=None)
from naiveNAS import NaiveNAS
from EEGNAS.models_generation import target_model
from braindecode.torch_ext.util import np_to_var
import numpy as np
from torch import nn
from braindecode.torch_ext.util import var_to_np
import torch as th
import matplotlib
matplotlib.use('qt5agg')
args = parse_args(['-e', 'tests', '-c', '../configurations/config.ini'])
global_vars.init_config(args.config)
configs = get_configurations(args.experiment)
assert (len(configs) == 1)
global_vars.set_config(configs[0])
subject_id = 1
low_cut_hz = 0
fs = 250
valid_set_fraction = 0.2
dataset = 'BCI_IV_2a'
data_folder = '../data/'
global_vars.set('dataset', dataset)
set_params_by_dataset()
global_vars.set('cuda', True)
model_select = 'deep4'
model_dir = '143_x_evolution_layers_cross_subject'
model_name = 'best_model_9_8_6_7_2_1_3_4_5.th'
train_set = {}
val_set = {}
low_cut_hz = 0
valid_set_fraction = 0.2
listen()
exp_id = get_exp_id('results')
exp_funcs = {'per_subject': per_subject_exp,
'leave_one_out': leave_one_out_exp,
'cross_subject': cross_subject_exp}
experiments = args.experiment.split(',')
all_exps = defaultdict(list)
first_run = True
for experiment in experiments:
configurations = get_configurations(experiment, global_vars.configs)
multiple_values = get_multiple_values(configurations)
for index, configuration in enumerate(configurations):
global_vars.set_config(configuration)
if index+1 < global_vars.get('start_exp_idx'):
continue
if global_vars.get('exp_id'):
exp_id = global_vars.get('exp_id')
configuration['DEFAULT']['exp_id'] = exp_id
if FIRST_RUN:
FIRST_DATASET = global_vars.get('dataset')
if global_vars.get('include_params_folder_name'):
multiple_values.extend(global_vars.get('include_params_folder_name'))
FIRST_RUN = False
exp_name = f"{exp_id}_{index+1}_{experiment}"
exp_name = add_params_to_name(exp_name, multiple_values)
ex.config = {}
ex.add_config({**configuration, **{'tags': [exp_id]}})
if len(ex.observers) == 0 and not args.debug_mode:
def set_default_config(path):
global_vars.init_config(path)
configurations = get_configurations('default_exp', global_vars.configs)
global_vars.set_config(configurations[0])