# Seed for reproducibility torch.manual_seed(0) random.seed(0) # Device device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") # Constants NUM_EPOCHS = 50 # Load model from argument model = torch.load(sys.argv[1]) if torch.cuda.is_available(): model.cuda() model.eval() print(util.get_model_layers(model)) ''' Helper Functions''' def parse_layer(layer): if "main_net" in layer: # FIXME: Need to change for MNAS _, _, cell_num, _, edge, _ = tuple(layer.split("_")) return int(cell_num), edge else: raise error("Invalid Layer Name") @torch.no_grad() def get_layer(model, layer, X): #cell_num, edge = parse_layer(layer) hook = render.ModuleHook(model.main_net[0].ops[str((1, 2))].ops[str(
def test_inceptionv1_import_layer_repr(): model = inceptionv1() layer_names = util.get_model_layers(model, getLayerRepr=True) for layer_name in important_layer_names: assert layer_names[layer_name] == 'CatLayer()'
def get_layers_for_lucent(model): print(get_model_layers(model))
def test_inceptionv1_graph_import(): model = inceptionv1() layer_names = util.get_model_layers(model) for layer_name in important_layer_names: assert layer_name in layer_names