def test_maxpool(self, _model_maxpool2D_1_last, _config_last):
"""Test maxpooling."""
config = _config_last
path_wd = config.get('paths', 'path_wd')
model_name = config.get('paths', 'filename_ann')
models.save_model(_model_maxpool2D_1_last,
os.path.join(path_wd, model_name + '.h5'))
updates = {
'tools': {
'evaluate_ann': False,
'normalize': False
},
'simulation': {
'duration': 100,
'num_to_test': 100,
'batch_size': 50
},
'output': {
'log_vars': {'activations_n_b_l', 'spiketrains_n_b_l_t'}
}
}
config.read_dict(updates)
acc = run_pipeline(config)
acc_ann = get_ann_acc(config)
assert acc[0] >= 0.9 * acc_ann
corr = get_correlations(config)
assert np.all(corr[:-1] > 0.99)
assert corr[-1] > 0.90
def test_maxpool_fallback(self, _model_maxpool2D_1, _config):
"""Test that maxpooling falls back on average pooling."""
path_wd = _config.get('paths', 'path_wd')
model_name = _config.get('paths', 'filename_ann')
models.save_model(_model_maxpool2D_1,
os.path.join(path_wd, model_name + '.h5'))
updates = {
'tools': {
'evaluate_ann': False,
'normalize': False
},
'conversion': {
'max2avg_pool': True
},
'simulation': {
'duration': 100,
'num_to_test': 100,
'batch_size': 50
},
'output': {
'log_vars': {'activations_n_b_l', 'spiketrains_n_b_l_t'}
}
}
_config.read_dict(updates)
acc = run_pipeline(_config)
assert acc[0] >= 0.8
corr = get_correlations(_config)
assert np.all(corr[:-1] > 0.99)
assert corr[-1] > 0.90
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