def test_hash_model_nochange(self):
model1 = uniform_model(10, ActivationLayer)
model2 = uniform_model(10, ActivationLayer)
model_set = []
genome_set = []
EEGNAS.utilities.NAS_utils.hash_model(model1, model_set, genome_set)
EEGNAS.utilities.NAS_utils.hash_model(model2, model_set, genome_set)
assert (len(model_set)) == 1
assert (len(genome_set)) == 1
def test_breed(self):
model1 = uniform_model(10, BatchNormLayer)
model2 = uniform_model(10, DropoutLayer)
model3, _, _ = breed_layers(0, model1, model2, cut_point=4)
for i in range(10):
if i < 4:
assert(type(model3[i]).__name__ == type(model1[i]).__name__)
else:
assert (type(model3[i]).__name__ == type(model2[i]).__name__)
finalize_model(model3)
pass
def test_hash_model(self):
model1 = uniform_model(10, ActivationLayer)
model2 = uniform_model(10, ActivationLayer)
global_vars.set('mutation_rate', 1)
model3, _ = breed_layers(1, model1, model2)
model_set = []
genome_set = []
EEGNAS.utilities.NAS_utils.hash_model(model1, model_set, genome_set)
EEGNAS.utilities.NAS_utils.hash_model(model2, model_set, genome_set)
EEGNAS.utilities.NAS_utils.hash_model(model3, model_set, genome_set)
assert (len(model_set)) == 1 or 2
assert (len(genome_set)) == 1 or 2
def test_state_inheritance_breeding(self):
global_vars.set('inherit_breeding_weights', True)
global_vars.set('num_layers', 4)
global_vars.set('mutation_rate', 0)
model1 = uniform_model(4, ConvLayer)
model1_state = finalize_model(model1).state_dict()
model2 = uniform_model(4, ConvLayer)
model2_state = finalize_model(model2).state_dict()
model3, model3_state, _ = breed_layers(0, model1, model2, model1_state, model2_state, 2)
for s1, s3 in zip(list(model1_state.values())[:4], list(model3_state.values())[:4]):
assert((s1==s3).all())
for s2, s3 in zip(list(model2_state.values())[6:8], list(model3_state.values())[6:8]):
assert((s2==s3).all())
def test_fix_model(self):
model1 = uniform_model(3, ConvLayer)
try:
models_generation.new_model_from_structure_pytorch(model1)
assert False
except Exception:
assert True
try:
models_generation.new_model_from_structure_pytorch(model1, applyFix=True)
assert True
except Exception:
assert False