def test_nest(self, _model_1, _config):
path_wd = _config.get('paths', 'path_wd')
model_name = _config.get('paths', 'filename_ann')
keras.models.save_model(_model_1,
os.path.join(path_wd, model_name + '.h5'))
updates = {
'tools': {'evaluate_ann': False},
'simulation': {
'simulator': 'nest',
'duration': 50,
'num_to_test': 10,
'batch_size': 1,
'dt': 0.1},
'cell': {
'tau_refrac': 0.1,
'delay': 0.1,
'v_thresh': 0.01},
'output': {
'log_vars': {'activations_n_b_l', 'spiketrains_n_b_l_t'}}}
_config.read_dict(updates)
initialize_simulator(_config)
acc = run_pipeline(_config)
assert acc[0] >= 0.95
corr = get_correlations(_config)
assert np.all(corr[:-1] > 0.97)
assert corr[-1] > 0.5
def test_brian2(self, _parsed_model, _testset, _config):
"""Needs to be tested separately because no saving function implemented.
"""
from importlib import import_module
from snntoolbox.bin.utils import initialize_simulator
_config.read_dict({
'simulation': {
'simulator': 'brian2',
'num_to_test': 2
},
'input': {
'poisson_input': True
}
})
try:
initialize_simulator(_config)
except ImportError:
return
target_sim = import_module(
'snntoolbox.simulation.target_simulators.brian2_target_sim')
spiking_model = target_sim.SNN(_config)
spiking_model.build(_parsed_model)
score = spiking_model.run(**_testset)
assert round(100 * score, 2) >= 99.00
def test_spinnaker_sparse(self, _model_3, _config):
path_wd = _config.get('paths', 'path_wd')
model_name = _config.get('paths', 'filename_ann')
models.save_model(_model_3, os.path.join(path_wd, model_name + '.h5'))
updates = {
'tools': {
'evaluate_ann': False
},
'input': {
'poisson_input': True
},
'simulation': {
'simulator': 'spiNNaker',
'duration': 100,
'num_to_test': 1, # smaller to make more feasible
'batch_size': 1
},
'output': {
'log_vars': {'activations_n_b_l', 'spiketrains_n_b_l_t'}
}
}
_config.read_dict(updates)
initialize_simulator(_config)
acc = run_pipeline(_config)
assert acc[0] >= 0.95
corr = get_correlations(_config)
assert np.all(corr[:-1] > 0.97)
assert corr[-1] > 0.5
def test_parsing(self, _model_4_first):
self.prepare_model(_model_4_first)
updates = {
'tools': {
'evaluate_ann': True,
'parse': True,
'normalize': False,
'convert': False,
'simulate': False
},
'input': {
'model_lib': 'pytorch'
},
'simulation': {
'num_to_test': 100,
'batch_size': 50
}
}
self.config.read_dict(updates)
initialize_simulator(self.config)
acc = run_pipeline(self.config)
assert acc[0] >= 0.8
def test_pipeline(self, _model_4_first):
self.prepare_model(_model_4_first)
updates = {
'tools': {
'evaluate_ann': False
},
'input': {
'model_lib': 'pytorch'
},
'simulation': {
'duration': 100,
'num_to_test': 100,
'batch_size': 50
},
'output': {
'log_vars': {'activations_n_b_l', 'spiketrains_n_b_l_t'}
}
}
self.config.read_dict(updates)
initialize_simulator(self.config)
acc = run_pipeline(self.config)
assert acc[0] >= 0.8
corr = get_correlations(self.config)
assert np.all(corr[:-1] > 0.97)
assert corr[-1] > 0.5
def test_loihi(self, _model_1, _config):
path_wd = _config.get('paths', 'path_wd')
model_name = _config.get('paths', 'filename_ann')
keras.models.save_model(_model_1,
os.path.join(path_wd, model_name + '.h5'))
updates = {
'tools': {
'evaluate_ann': True,
'normalize': False
},
'loihi': {
'reset_mode': 'soft',
'desired_threshold_to_input_ratio': 1,
'compartment_kwargs': {
'biasExp': 6,
'vThMant': 512
},
'connection_kwargs': {
'numWeightBits': 8,
'weightExponent': 0,
'numBiasBits': 12
},
'validate_partitions': False,
'save_output': False,
'do_overflow_estimate': False,
'normalize_thresholds': True
},
'simulation': {
'simulator': 'loihi',
'duration': 512,
'num_to_test': 100,
'batch_size': 1
},
'output': {
'log_vars': {'activations_n_b_l', 'spiketrains_n_b_l_t'}
}
}
_config.read_dict(updates)
initialize_simulator(_config)
acc = run_pipeline(_config)
assert acc[0] >= 0.95
corr = get_correlations(_config)
assert np.all(corr[:-1] > 0.97)
assert corr[-1] > 0.5
def test_loading(self, _model_4, _config):
import keras
assert keras.backend.image_data_format() == 'channels_first', \
"Pytorch to Keras parser needs image_data_format == channel_first."
self.prepare_model(_model_4, _config)
updates = {
'tools': {
'evaluate_ann': True,
'parse': False,
'normalize': False,
'convert': False,
'simulate': False
},
'input': {
'model_lib': 'pytorch'
},
'simulation': {
'num_to_test': 100,
'batch_size': 50
}
}
_config.read_dict(updates)
initialize_simulator(_config)
normset, testset = get_dataset(_config)
model_lib = import_module('snntoolbox.parsing.model_libs.' +
_config.get('input', 'model_lib') +
'_input_lib')
input_model = model_lib.load(_config.get('paths', 'path_wd'),
_config.get('paths', 'filename_ann'))
# Evaluate input model.
acc = model_lib.evaluate(input_model['val_fn'],
_config.getint('simulation', 'batch_size'),
_config.getint('simulation', 'num_to_test'),
**testset)
assert acc >= 0.8
def test_nest(self, _model_1, _config):
path_wd = _config.get('paths', 'path_wd')
model_name = _config.get('paths', 'filename_ann')
keras.models.save_model(_model_1,
os.path.join(path_wd, model_name + '.h5'))
updates = {
'tools': {
'evaluate_ann': False
},
'input': {
'poisson_input': True
},
'simulation': {
'simulator': 'nest',
'duration': 100,
'num_to_test': 100,
'batch_size': 1
},
'output': {
'log_vars': {'activations_n_b_l', 'spiketrains_n_b_l_t'}
}
}
_config.read_dict(updates)
try:
initialize_simulator(_config)
except (ImportError, KeyError, ValueError):
return
acc = run_pipeline(_config)
assert acc[0] >= 0.95
corr = get_correlations(_config)
assert np.all(corr[:-1] > 0.97)
assert corr[-1] > 0.5
def get_parameters_for_simtests():
from snntoolbox.bin.utils import load_config, initialize_simulator
config_defaults = load_config(
os.path.abspath(
os.path.join(os.path.dirname(__file__), '..', 'snntoolbox',
'config_defaults')))
config_ini = {
'simulation': {
'simulator': 'INI',
'target_acc': 99.00,
'num_to_test': 200
}
}
config_nest = {
'simulation': {
'simulator': 'nest',
'target_acc': 99.00,
'num_to_test': 2
},
'input': {
'poisson_input': True
}
}
config_brian = {
'simulation': {
'simulator': 'brian',
'target_acc': 99.00,
'num_to_test': 2
},
'input': {
'poisson_input': True
}
}
config_neuron = {
'simulation': {
'simulator': 'Neuron',
'target_acc': 99.00,
'num_to_test': 2
},
'input': {
'poisson_input': True
}
}
config_megasim = {
'simulation': {
'simulator': 'MegaSim',
'batch_size': 1,
'target_acc': 99.00,
'num_to_test': 2
},
'input': {
'poisson_input': True
}
}
configs_to_test = [
config_ini, config_nest, config_brian, config_neuron, config_megasim
]
_sm = []
for config_to_test in configs_to_test:
config_defaults.read_dict(config_to_test)
try:
initialize_simulator(config_defaults)
except (ImportError, KeyError, ValueError):
continue
config_defaults['restrictions']['is_installed'] = 'True'
_sm.append(config_defaults)
return _sm
def test_initialize_simulator(config):
from snntoolbox.bin.utils import initialize_simulator
if config.getboolean('restrictions', 'is_installed'):
assert initialize_simulator(config)
else:
pytest.raises(ImportError, initialize_simulator, config=config)
