def _config(_path_wd, _datapath):
path_wd = str(_path_wd)
datapath = str(_datapath)
filename_ann = 'mnist_cnn'
configparser = import_configparser()
config = configparser.ConfigParser()
config.read_dict({
'paths': {
'path_wd': path_wd,
'dataset_path': datapath,
'filename_ann': filename_ann
}
})
with open(os.path.join(path_wd, filename_ann + '.h5'), 'w'):
pass
config_filepath = os.path.join(path_wd, 'config')
with open(config_filepath, 'w') as configfile:
config.write(configfile)
config = update_setup(config_filepath)
return config
def main(filepath=None):
"""Entry point for running the toolbox.
Note
----
There is no need to call this function directly, because python sets up an
executable during :ref:`installation` that can be called from terminal.
"""
from snntoolbox.bin.utils import update_setup, run_pipeline
if filepath is not None:
config = update_setup(filepath)
run_pipeline(config)
return
parser = argparse.ArgumentParser(
description='Run SNN toolbox to convert an analog neural network into '
'a spiking neural network, and optionally simulate it.')
parser.add_argument('config_filepath',
nargs='?',
help='Path to configuration file.')
parser.add_argument('-t',
'--terminal',
action='store_true',
help='Set this flag to run the toolbox from terminal. '
'Omit this flag to open GUI.')
args = parser.parse_args()
_filepath = os.path.abspath(args.config_filepath)
if _filepath is not None:
config = update_setup(_filepath)
if args.terminal:
run_pipeline(config)
else:
from snntoolbox.bin.gui import gui
gui.main()
else:
if args.terminal:
parser.error("When using the SNN toolbox from terminal, a "
"config_filepath argument must be provided.")
return
else:
from snntoolbox.bin.gui import gui
gui.main()
def test_updating_settings(params, expect_pass, _path_wd):
configparser = import_configparser()
config = configparser.ConfigParser()
config.read_dict(params)
configpath = os.path.join(str(_path_wd), 'config')
with open(configpath, 'w') as file:
config.write(file)
if expect_pass:
assert update_setup(configpath)
else:
pytest.raises(AssertionError, update_setup, configpath)
def main():
"""Entry point for running the toolbox.
Note
----
There is no need to call this function directly, because python sets up an
executable during :ref:`installation` that can be called from terminal.
"""
import numpy as np
np.random.seed(100) #LQ: same random seed for every run
parser = argparse.ArgumentParser(
description='Run SNN toolbox to convert an analog neural network into '
'a spiking neural network, and optionally simulate it.')
parser.add_argument('config_filepath',
nargs='?',
help='Path to configuration file.')
parser.add_argument('-t',
'--terminal',
action='store_true',
help='Set this flag to run the toolbox from terminal. '
'Omit this flag to open GUI.')
args = parser.parse_args()
filepath = os.path.abspath(args.config_filepath)
#filepath = '/mnt/2646BAF446BAC3B9/Repositories/NPP/snn_toolbox/examples/models/lenet5/keras/config'
#filepath = '/home/rbodo/.snntoolbox/data/mnist/cnn/lenet5/keras/32bit/log/gui/14/config'
#args.terminal = True
print(filepath) #LQ: Print out the path of the configuration file
if filepath is not None:
assert os.path.isfile(filepath), \
"Configuration file not found at {}.".format(filepath)
from snntoolbox.bin.utils import update_setup
config = update_setup(filepath)
if args.terminal:
from snntoolbox.bin.utils import test_full
test_full(config)
else:
from snntoolbox.bin.gui import gui
gui.main()
else:
if args.terminal:
parser.error("When using the SNN toolbox from terminal, a "
"config_filepath argument must be provided.")
return
else:
from snntoolbox.bin.gui import gui
gui.main()
def test_updating_settings(params, expect_pass, _path_wd):
from snntoolbox.bin.utils import update_setup
try:
import configparser
except ImportError:
import ConfigParser as configparser
config = configparser.ConfigParser()
config.read_dict(params)
configpath = os.path.join(str(_path_wd), 'config')
with open(configpath, 'w') as f:
config.write(f)
if expect_pass:
assert update_setup(configpath)
else:
pytest.raises(AssertionError, update_setup, configpath)
def _config():
from snntoolbox.bin.utils import update_setup
return update_setup(
os.path.join(os.path.dirname(__file__), 'configurations', 'config0'))
def test_examples(self, _example_filepath):
from snntoolbox.bin.utils import update_setup, test_full
config = update_setup(_example_filepath)
assert test_full(config)[0] >= 0.5
np.array(copy.deepcopy(weights[i]), dtype="int32").flatten())
return weights
model_lib = import_module("snntoolbox.parsing.model_libs.keras_input_lib")
input_model = model_lib.load(os.path.dirname(model_path),
os.path.basename(model_path))
acc = model_lib.evaluate(input_model['val_fn'],
batch_size=1,
num_to_test=50,
x_test=testX[:50],
y_test=testY[:50])
config = update_setup(config_path)
config.set("paths", "path_wd", os.path.dirname(model_path))
config.set("paths", "dataset_path", os.path.dirname(model_path))
config.set("paths", "filename_ann", os.path.basename(model_path))
model_parser = model_lib.ModelParser(input_model['model'], config)
model_parser.parse()
parsed_model = model_parser.build_parsed_model()
# Normalize
norm_data = {'x_norm': testX}
normalize_parameters(parsed_model, config, **norm_data)
score_norm = model_parser.evaluate(batch_size=1,
num_to_test=50,
x_test=testX[:50],
y_test=testY[:50])
