class CaffeServer(object):
def __init__(self, caffe_root, feature_layers, gpu):
self.extractor = CaffeExtractor(caffe_root=caffe_root, feature_layers=feature_layers, gpu=gpu)
def getImageFeatures(self, image , image_shape):
image = np.fromstring(snappy.uncompress(image), dtype=np.float32)
image.resize(image_shape)
feature_dic = self.extractor.getImageFeatures(image)
feature_dic = {layer:snappy.compress(features) for layer,features in feature_dic.items()}
return feature_dic
def getImageLabels(self):
labels = self.extractor.getImageLabels()
return labels
def model_mobileface2(do_mirror):
model_dir = './models/mobilefacenet/'
model_proto = model_dir + 'model.prototxt'
model_path = model_dir + 'model.caffemodel'
image_size = (112, 112)
extractor = CaffeExtractor(model_proto,
model_path,
do_mirror=do_mirror,
featLayer='fc1')
return extractor, image_size
def model_mobileface(do_mirror):
model_dir = './models/mobilefacenet/'
model_proto = model_dir + 'mobilefacenet-res2-6-10-2-dim128-opencv.prototxt'
model_path = model_dir + 'mobilefacenet-res2-6-10-2-dim128.caffemodel'
image_size = (112, 112)
extractor = CaffeExtractor(model_proto,
model_path,
do_mirror=do_mirror,
featLayer='fc1')
return extractor, image_size
def model_arcface(do_mirror):
model_dir = './models/arcface/'
model_proto = model_dir + 'model.prototxt'
model_path = model_dir + 'model-r50-am.caffemodel'
image_size = (112, 112)
extractor = CaffeExtractor(model_proto,
model_path,
do_mirror=do_mirror,
featLayer='fc1')
return extractor, image_size
def model_sphereface(do_mirror):
model_dir = './models/sphereface/'
model_proto = model_dir + 'sphereface_deploy.prototxt'
model_path = model_dir + 'sphereface_model.caffemodel'
image_size = (96, 112)
extractor = CaffeExtractor(model_proto,
model_path,
do_mirror=do_mirror,
featLayer='fc5')
return extractor, image_size
def test_model(model,
weight,
dist_type='cosine',
do_mirror=False,
feat_layer='fc5'):
extractor = CaffeExtractor(model,
weight,
do_mirror=do_mirror,
featLayer=feat_layer)
test_loss(extractor, weight, dist_type)
def model_yours(do_mirror):
model_dir = '/path/to/your/model/'
model_proto = model_dir + 'deploy.prototxt'
model_path = model_dir + 'weights.caffemodel'
image_size = (112, 112)
extractor = CaffeExtractor(model_proto,
model_path,
do_mirror=do_mirror,
featLayer='fc5')
return extractor, image_size
def model_tzk(do_mirror):
model_dir = '/home/ysten/tzk/fr/insightface/caffe/AMSoftmax/'
model_proto = model_dir + 'deploy.prototxt'
model_path = model_dir + 'tune_iter_20000.caffemodel'
image_size = (112, 112)
extractor = CaffeExtractor(model_proto,
model_path,
do_mirror=do_mirror,
featLayer='fc5')
return extractor, image_size
def model_AMSoftmax(do_mirror):
model_dir = './models/AMSoftmax/'
if do_mirror:
model_proto = model_dir + 'face_deploy_mirror_normalize.prototxt'
else:
model_proto = model_dir + 'deploy.prototxt'
model_path = model_dir + 'face_train_test_iter_30000.caffemodel'
image_size = (96, 112)
extractor = CaffeExtractor(model_proto,
model_path,
do_mirror=False,
featLayer='fc5')
return extractor, image_size
def __init__(self, caffe_root, feature_layers, gpu):
self.extractor = CaffeExtractor(caffe_root=caffe_root, feature_layers=feature_layers, gpu=gpu)
# load model
feat_layer = 'fc5'
if config.get('model').feat_layer:
featLayer = config.get('model').feat_layer
if config.get(model_name).feat_layer != None:
feat_layer = config.get(model_name).feat_layer
model = config.get(model_name).model
weights = config.get(model_name).weights
suffix = config.get(model_name).suffix
print('Test model:%s feat:%s suffix:%s' % (model_name, feat_layer, suffix))
extractor = CaffeExtractor(model,
weights,
featLayer=feat_layer,
gpu_id=config.get('model').gpu_id)
# devkit/templatelists
templatelists_dir = config.get('devkit').templatelists_dir
# Extract facescrub features
facescrub_dir = config.get('facescrub').aligned_dir
facescrub_feature_dir = config.get('facescrub').feature_dir
extract_facescrub_uncropped(facescrub_dir, templatelists_dir,
facescrub_feature_dir, suffix, extractor)
# Extract MegaFace features
mega_dir = config.get('megaface').aligned_dir
mega_feature_dir = config.get('megaface').feature_dir
extract_megaface_features(mega_dir, templatelists_dir, mega_feature_dir,
def test_model(model, weight, dist_type='cosine', do_mirror=False):
extractor = CaffeExtractor(model, weight, do_mirror=do_mirror)
test_loss(extractor, weight, dist_type)