def evaluate_protocol(evaluate_config_path,model_config_path,image_config_path,write=False):
model_config_gen = get_config(model_config_path)
image_config_gen = get_config(image_config_path)
overall_config_gen = SON([('models',model_config_gen['models']),
('images',image_config_gen['images'])])
feature_hash = get_config_string(overall_config_gen)
feature_certificate = '../.feature_certificates/' + feature_hash
evaluate_config = get_config(evaluate_config_path)
task_config = evaluate_config.pop('train_test')
D = []
ext_hashes = []
for task in task_config:
overall_config_gen = SON([('models',model_config_gen),
('image',image_config_gen),('task',task)])
ext_hash = get_config_string(overall_config_gen)
outfile = '../.performance_certificates/' + ext_hash
op = ('svm_evaluation_' + ext_hash,evaluate,(outfile,feature_certificate,
evaluate_config_path,task,ext_hash))
D.append(op)
ext_hashes.append(ext_hash)
if write:
actualize(D)
return D,ext_hashes
def extract_and_evaluate_protocol(evaluate_config_path,model_config_path,image_config_path,convolve_func_name='numpy', write=False,parallel=False):
model_config_gen = get_config(model_config_path)
model_hash = get_config_string(model_config_gen['models'])
model_certificate = '../.model_certificates/' + model_hash
image_config_gen = get_config(image_config_path)
image_hash = get_config_string(image_config_gen['images'])
image_certificate = '../.image_certificates/' + image_hash
evaluate_config = get_config(evaluate_config_path)
task_config = evaluate_config.pop('train_test')
D = []
DH = {}
for task in task_config:
overall_config_gen = SON([('models',model_config_gen),('images',image_config_gen),('task',task)])
ext_hash = get_config_string(overall_config_gen)
if not parallel:
performance_certificate = '../.performance_certificates/' + ext_hash
op = ('evaluation_' + ext_hash,extract_and_evaluate,(performance_certificate,image_certificate,model_certificate,evaluate_config_path,convolve_func_name,task,ext_hash))
D.append(op)
DH[ext_hash] = [op]
else:
performance_certificate = '../.performance_certificates/' + ext_hash
batch_certificate = '../.batch_certificates/' + ext_hash
op = ('evaluation' + ext_hash,extract_and_evaluate_parallel,(batch_certificate,image_certificate,model_certificate,evaluate_config_path,convolve_func_name,task,ext_hash))
D.append(op)
DH[ext_hash] = [op]
if write:
actualize(D)
return DH
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
def model_protocol(config_path,write = False,parallel=False):
config = get_config(config_path)
model_hash = get_config_string(config['models'])
model_certificate = '../.model_certificates/' + model_hash
D = [('generate_models',generate_models,(model_certificate,model_hash,config))]
if write:
actualize(D)
return D,model_hash
def extract_features_protocol(image_config_path,
model_config_path,
convolve_func_name = 'numpy',
write = False,
parallel=False,
batch_size=1000):
model_config_gen = get_config(model_config_path)
model_hash = get_config_string(model_config_gen['models'])
model_certificate = '../.model_certificates/' + model_hash
image_config_gen = get_config(image_config_path)
image_hash = get_config_string(image_config_gen['images'])
image_certificate = '../.image_certificates/' + image_hash
overall_config_gen = SON([('models',model_config_gen['models']),('images',image_config_gen['images'])])
feature_hash = get_config_string(overall_config_gen)
feature_certificate = '../.feature_certificates/' + feature_hash
if not parallel:
D = [('extract_features',
extract_features,(feature_certificate,
image_certificate,
model_certificate,
overall_config_gen,
feature_hash,
convolve_func_name))]
else:
D = [('extract_features',
extract_features_parallel,(feature_certificate,
image_certificate,
model_certificate,
overall_config_gen,
feature_hash,
convolve_func_name,
batch_size))]
if write:
actualize(D)
return D, feature_hash
def image_protocol_hash(config_path):
config = get_config(config_path)
image_hash = get_config_string(config['images'])
return image_hash