parser.add_argument('--backend',type=str,default='caffe+scipy',choices=['torch','caffe+scipy'],help='reconstruction implementation')
parser.add_argument('--device_id',type=int,default=0,help='zero-indexed CUDA device')
parser.add_argument('--K',type=int,default=100,help='number of nearest neighbors')
parser.add_argument('--scaling',type=str,default='beta',choices=['none','beta'],help='type of step scaling')
parser.add_argument('--iter',type=int,default=500,help='number of reconstruction iterations')
parser.add_argument('--postprocess',type=str,default='color',help='comma-separated list of postprocessing operations')
parser.add_argument('--delta',type=str,default='0.4',help='comma-separated list of interpolation steps')
config=parser.parse_args()
postprocess=set(config.postprocess.split(','))
print(json.dumps(config.__dict__))
# load CUDA model
minimum_resolution=200
if config.backend=='torch':
import deepmodels_torch
model=deepmodels_torch.vgg19g_torch(device_id=config.device_id)
elif config.backend=='caffe+scipy':
model=deepmodels.vgg19g(device_id=config.device_id)
else:
raise ValueError('Unknown backend')
# download AEGAN cropped+aligned LFW (if needed)
if not os.path.exists('images/lfw_aegan'):
url='https://www.dropbox.com/s/isz4ske2kheuwgr/lfw_aegan.tar.gz?dl=1'
subprocess.check_call(['wget',url,'-O','lfw_aegan.tar.gz'])
subprocess.check_call(['tar','xzf','lfw_aegan.tar.gz'])
subprocess.check_call(['rm','lfw_aegan.tar.gz'])
# read test data
data=numpy.load('tests/dmt2-lfw-multiple-attribute-test.npz')
pairs=list(data['pairs'][[0,1,2,4,5,6]]) # skip flushed face, not interesting
parser.add_argument('-ip', '--input_path', type=str,default='images/celeba', help='the training image folder')
parser.add_argument('-gpu', type=str, default='0', help='the gpu id to use')
parser.add_argument('--backend', type=str, default='torch', choices=['torch', 'caffe+scipy'],
help='reconstruction implementation')
parser.add_argument('--K', type=int, default=100, help='number of nearest neighbors')
parser.add_argument('--delta', type=str, default='3.5', help='comma-separated list of interpolation steps')
parser.add_argument('--npz_path', type=str, default='attribute_vector', help='the path to store npz data')
config = parser.parse_args()
# print(json.dumps(config.__dict__))
os.environ['CUDA_VISIBLE_DEVICES'] = config.gpu
# load models
if config.backend == 'torch':
import deepmodels_torch
model = deepmodels_torch.vgg19g_torch(device_id=0)
elif config.backend == 'caffe+scipy':
model = deepmodels.vgg19g(device_id=0)
else:
raise ValueError('Unknown backend')
classifier = deepmodels.facemodel_attributes()
fields = classifier.fields()
gender = fields.index('Male')
smile = fields.index('Smiling')
face_d, face_p = alignface.load_face_detector()
# Set the free parameters
K = config.K
delta_params = [float(x.strip()) for x in config.delta.split(',')]
image_list = glob.glob(config.input_path+'/*')
X = image_list
t0 = time.time()