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