def create_param(device_id_, image_list_, image_pair_list_, model_infos_):
model_param_config = []
for elem in model_infos_:
if (elem.has_key('mean_value')):
each_param = caffe_pb_feature.ModelInitParameter(
image_root_path=elem['image_root_path'],
deploy_path=elem['deploy_path'],
model_path=elem['model_path'],
output_path=elem['output_path'],
data_transform=caffe_pb_feature.TransformationParameter(
mean_value=elem['mean_value'], scale=elem['scale']))
else:
each_param = caffe_pb_feature.ModelInitParameter(
image_root_path=elem['image_root_path'],
deploy_path=elem['deploy_path'],
model_path=elem['model_path'],
output_path=elem['output_path'])
model_param_config.append(each_param)
feature = caffe_pb_feature.ExtractFeatureParameter(
run_mode=caffe_pb_feature.ExtractFeatureParameter.RunMode.Value('GPU'),
device_id=device_id_,
image_list=image_list_,
image_pair_list=image_pair_list_,
model_config=model_param_config)
return feature
def createParamFile(self,
batch_model_info,
output_result_path,
mean_value=None,
scale=1.0):
model_param_config = []
for elem in batch_model_info:
deploy_path = elem[0]
model_lists = elem[1]
for model_name_path in model_lists:
model_name = model_name_path.split('/')[-1]
patch_info = utility.crop_patch_info_model_name(model_name)
if mean_value != None:
each_param = caffe_pb_feature.ModelInitParameter(
image_root_path='{}/{}'.format(
ConfigPath.test_data_set[self.test_set]
['imgs_folder'], patch_info),
deploy_path=deploy_path,
model_path=model_name_path,
output_path=output_result_path,
data_transform=caffe_pb_feature.
TransformationParameter(mean_value=mean_value,
scale=scale))
else:
if model_name_path.find('Means') >= 0:
each_param = caffe_pb_feature.ModelInitParameter(
image_root_path='{}/{}'.format(
ConfigPath.test_data_set[self.test_set]
['imgs_folder'], patch_info),
deploy_path=deploy_path,
model_path=model_name_path,
output_path=output_result_path,
data_transform=caffe_pb_feature.
TransformationParameter(
mean_value=[127.5, 127.5, 127.5],
scale=0.0078125))
else:
each_param = caffe_pb_feature.ModelInitParameter(
image_root_path='{}/{}'.format(
ConfigPath.test_data_set[self.test_set]
['imgs_folder'], patch_info),
deploy_path=deploy_path,
model_path=model_name_path,
output_path=output_result_path,
)
model_param_config.append(each_param)
feature = caffe_pb_feature.ExtractFeatureParameter(
run_mode=caffe_pb_feature.ExtractFeatureParameter.RunMode.Value(
'GPU'),
device_id=0,
image_list=ConfigPath.test_data_set[self.test_set]['image_list'],
image_pair_list=ConfigPath.test_data_set[
self.test_set]['pairs_file'],
model_config=model_param_config)
return feature