def v1_greedy_optimization_protocol(config_path,use_cpu = False,write=False): D = DBAdd(image_initialize,args = (config_path,)) oplist = do_initialization(image_initialize,args = (config_path,)) image_certificate = oplist[0]['outcertpaths'][0] if use_cpu or not GPU_SUPPORT: convolve_func = v1f.v1like_filter_numpy else: convolve_func = v1f.v1like_filter_pyfft config = get_config(config_path) task = config['evaluation_task'] initial_model = config['model'] modifier_args = config['modifier_args'] modifier_class = config.get('modifier') rep_limit = config.get('rep_limit') if modifier_class is None: modifier = config_modifiers.BaseModifier(modifier_args) else: modifier = modifier_class(modifier_args) newhash = get_config_string(config) outfile = '../.optimization_certificates/' + newhash op = ('optimization_' + newhash,greedy_optimization,(outfile,task,image_certificate,initial_model,convolve_func,rep_limit,modifier_args,modifier)) D.append(op) if write: actualize(D) return D
def v1_evaluation_protocol(task_config_path,feature_config_path,use_cpu=False): oplist = do_initialization(v1_initialize,args = (feature_config_path,use_cpu)) feature_creates = tuple(oplist[-1]['outcertpaths']) hash = get_config_string(oplist[-1]['out_args']) feature_config = get_config(feature_config_path) task_config = get_config(task_config_path) D = [] for task in task_config['train_test']: c = (feature_config,task) newhash = get_config_string(c) outfile = '../.performance_certificates/' + newhash op = ('svm_evaluation_' + newhash,train_test_loop,(outfile,feature_creates,task,feature_config_path,hash)) D.append(op) return D